Cost and efficiency assumptions for modelling low carbon vehicles out to 2050

A review of the efficiency and cost
assumptions for road transport
vehicles to 2050
FINAL
Report for the Committee on Climate Change
AEA/R/ED57444
Issue Number 1
Date 25/04/2012

A review of the efficiency and cost assumptions for road transport vehicles to 2050
Ref: AEA/ED57444/Issue Number 2 ii
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Author:
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John Norris and Duncan Kay
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Date:
25 April 2012
Signed:
AEA reference:
Ref: ED57444 - Issue Number 2

Ref: AEA/ED57444/Issue Number 2 ii
Executive Summary
Introduction
The Committee on Climate Change (CCC) has a number of priorities, including a key priority
of providing advice, analysis and information to Government and the Devolved
Administrations on the setting of carbon budgets. The CCC utilises a range of analytical
tools for informing and developing its recommendations on the UK’s carbon budgets,
including sectoral or economy wide models with a strong emphasis on reducing GHG
emissions as cost-effectively as possible.
The CCC has identified a need to carry out a thorough review and update of its current
assumptions on the fuel efficiency and capital costs of road vehicle technologies to inform its
understanding of the potential future development of their performance and costs in the
period 2010-2050.
The CCC therefore commissioned this project, with the following main aims and objectives:
i. Carry out a review of the literature on the fuel efficiency and capital costs associated
with road transport technologies to 2050;
ii. Using information from the literature review, develop assumptions for fuel efficiency
and capital costs for each technology to 2050;
iii. Based on these findings advise the CCC on the validity of their default assumption
that there would be no or minimal change to the fuel efficiency and capital costs of
the dominant vehicle technologies in the absence of any government policy to reduce
GHG emissions.
The aim of the study was to develop a detailed understanding of how the uptake of
technological options to improve efficiency/reduce GHG emissions is likely to impact on
overall costs and efficiencies of different vehicle classes in the period 2010-2050.
The primary deliverable for this work is the dataset provided to CCC alongside this report
detailing the projected costs and vehicle efficiencies developed for the study. The purpose of
this report is to provide a summary of the methodological approach and the key sources and
assumptions used to define this dataset and a short summary of the results of the analysis.
Development of the Road Vehicle Cost and Efficiency Dataset
The initial phase of the work included establishing a categorisation framework within which to
carry out a literature review of the performance and cost characteristics of road transport
vehicles to 2050, before developing the future trajectory of these characteristics to 2050. The
categorisation agreed with CCC included 9 mode categories (cars, vans, motorcycles, small
rigid trucks, large rigid trucks, articulated trucks, construction trucks, buses and coaches) and
an additional split by core powertrain technology (including 13 categories for cars/vans, 8 for
heavy duty vehicles and 4 for motorcycles/mopeds).
As part of the project’s analysis phase an Excel-based calculation framework was developed
to facilitate consistent repeatable calculations across all modes and allow selected key
parameter assumptions to be easily changed, such as the vehicle characteristics, technology
performance, costs and deployment levels and potential learning rates for cost trajectories.
One of the principal objectives of the work was also to help CCC gain a better understanding
of how the improvements in efficiency and resulting capital costs break down into different
impact areas and components. The information collected and the subsequent disaggregation
of cost and efficiency calculations were carried out under the following agreed categories:

Ref: AEA/ED57444/Issue Number 2 iii
Core Powertrain Energy Storage Glider
Powertrain Efficiency Technologies Aerodynamics Rolling Resistance
Vehicle Weight Other Options
The methodological approach developed also included an estimation of the real-world fuel
efficiency of the final vehicle, using a percentage uplift factor on the test-cycle based fuel
efficiency figure. This was included in an attempt to factor in a range of considerations that
affect the performance of vehicles in the real-world versus the specific conditions used in the
calculation of fuel consumption using regulatory (or other) test-cycles.
Full details on the methodology developed and implemented in the calculation framework
and on the key data sources and assumptions used in the calculations are provided in the
main body of this report.
Summary of Key Results from the Analysis
Key results from the developed calculation framework analysis are summarised below:
For passenger cars and vans:
• Conventional powertrains have the greatest potential for % improvements in fuel
efficiency in the long term (though being less efficient in absolute terms), versus
increasingly electrified powertrain alternatives. The overall potential reduction in
energy consumption 2010-2050 ranges from 27%-50% depending on powertrain.
• Capital cost differentials are expected to narrow substantially by 2030, with many
alternatives becoming cost-competitive if fuel savings are included (depending on
future tax rates for different fuels). Assumptions on electric driving range and battery
cost reductions are critical factors. Under low cost assumptions BEV cars become
comparable in price to ICEs by 2050, but under high cost assumptions H2FC variants
become the more cost-effective ultra-low GHG option.
• The benefits of additional improvements to the ICE appear to be marginal for REEVs
after 2020. Also the cost of efficiency improvements to BEVs beyond those to the
basic powertrain are extremely high per gCO2e/km abated. Therefore uptake of
these may be more limited than for other powertrains, although the impacts on
battery capacity/costs also need to be factored into the equation.
For motorcycles the reduction potential identified for different powertrain technologies is
lower than cars and vans (10-36%), but may be due to insufficient information in the
literature. BEV and HEV technologies may become cost-competitive with ICE by 2030.
For heavy duty vehicles in predominantly urban cycles (small rigid trucks and buses):
• Efficiency improvement benefits by 2050 are expected to reach 16-28%. These
reductions are predominantly due to powertrain improvements, with lower levels of
benefit from rolling resistance and lightweighting. The greatest benefits are therefore
achieved through switching from conventional ICE to more efficient alternative
powertrains.
• Purely in terms of capital costs, H2FC technology is the lowest cost ultra-low GHG
option for the long-term, however the comparison with BEV changes if fuel costs are
included. Factoring in likely future fuel costs brings most technologies to overall cost
levels comparable with or lower than Diesel ICE by 2030 (depending on future fuel
tax levels).
For heavy duty vehicles with the greatest proportions of their km outside of urban areas
(large rigid, articulated and construction trucks, coaches):
• Efficiency improvement benefits by 2050 are expected to reach 23-43% (depending
on type/powertrain). These reductions are mostly due to improvements in the

Ref: AEA/ED57444/Issue Number 2 iv
powertrain and aerodynamics (except construction trucks), with lower levels of
benefit from rolling resistance, lightweighting and other technologies.
• Capital costs of alternative powertrains drop to within 6-14% of Diesel ICE by 2050
(depending on vehicle type/powertrain). Factoring in likely future fuel costs brings
most technologies to combined cost levels comparable with or lower than Diesel ICE
by 2030 and essentially all by 2050 (depending on future fuel tax levels).
• DNG ICE powertrains appear to offer a cost effective alternative (under current tax
levels) versus alternatives with substantial lifecycle GHG savings in the short-medium
term, which could be further improved through the use of biomethane. In the long
term H2FC offer greater GHG savings at similar capital costs.
Assessment of CCC’s Default Trajectory
The purpose of Task 4 was to: “advise on the validity of the CCC’s default assumption that in
the absence of any government policy to reduce GHG emissions (including existing new
vehicle CO2 regulations), there would be no or minimal change to the fuel efficiency and
capital costs of the dominant vehicle technologies within each vehicle category.”
The following provides a summary of the main findings of this assessment:
For passenger cars there is not sufficiently strong evidence to suggest that the
assumption of a flat counterfactual is incorrect and that the CCC should therefore
continue to use this assumption in its modelling work.
For van/light commercial vehicle efficiency there is some evidence to suggest that the
assumption of a flat counterfactual is not valid for vans and it may be more appropriate
for CCC to revise this assumption in its modelling work to reflect a gradual rate of annual
improvement in van efficiency.
For heavy duty truck efficiency there is good evidence to suggest that the assumption of
a flat counterfactual is incorrect for specific sizes of heavy trucks. However, the general
trend of increasing vehicle sizing (presumably in a drive to increase operational efficiency
on a tonne-km basis) means that the fleet as a whole has a trend to increasing MPG.
CCC may therefore wish revise these elements into its modelling work to reflect annual
increases in heavy truck efficiency, but factoring in changes in relative vehicle sizing
affecting actual energy consumption per km.
For buses and coaches there some evidence to suggest that the assumption of a flat
counterfactual for bus and coach efficiency is incorrect and that the CCC should therefore
consider revising this assumption in its modelling work.
For the capital costs of vans, trucks, busses and coaches, there is not sufficiently strong
evidence to suggest that the assumption of a flat counterfactual is incorrect and that the
CCC should therefore continue to use this assumption in its modelling work for the capital
costs of other vehicles.
For motorcycles and mopeds, no evidence has been identified to suggest a change in the
current assumption.

Ref: AEA/ED57444/Issue Number 2 v
Table of contents
1 Introduction ................................................................................................................ 1
1.1 Background........................................................................................................ 1
1.2 Aims and Objectives .......................................................................................... 1
1.3 Scope of the Work.............................................................................................. 2
1.4 Structure of the Report....................................................................................... 2
2 Methodology, Vehicle and Technology Definitions ................................................. 3
2.1 Vehicle Categorisation ....................................................................................... 3
2.2 Methodological Overview ................................................................................... 6
2.3 Vehicle Characteristics....................................................................................... 7
2.4 Efficiency Improvement Technologies...............................................................17
3 Efficiency Assumptions............................................................................................23
3.1 Calculation Methodology...................................................................................23
3.2 Key Sources and Assumptions..........................................................................24
4 Capital Cost Assumptions........................................................................................33
4.1 Calculation Methodology...................................................................................33
4.2 Key Sources and Assumptions..........................................................................38
5 Technology Compatibility and Deployment Assumptions.....................................45
5.1 Compatibility and Stackability............................................................................45
5.2 Deployment.......................................................................................................49
6 Results: Cost and Efficiency Trajectories from 2010 to 2050 ................................61
6.1 Light Duty Vehicles and Motorcycles.................................................................63
6.2 Heavy Duty Vehicles .........................................................................................79
7 Evaluation of CCC’s Default Trajectory Assumptions..........................................106
7.1 Assumptions and scope ..................................................................................106
7.2 Passenger Cars ..............................................................................................107
7.3 Vans/LCVs......................................................................................................119
7.4 Heavy Trucks..................................................................................................122
7.5 Other Vehicles ................................................................................................127
7.6 Capital Costs of Vans, Trucks and Other Vehicles ..........................................128
7.7 Summary of Recommendations ......................................................................129
8 References...............................................................................................................131
Appendices
Appendix 1 Technology Specific Characteristics................................................................137

Ref: AEA/ED57444/Issue Number 2 vi
Table of tables
Table 2.1: Road vehicle categorisation utilised in the study assessment ............................3
Table 2.2: Summary descriptions of road transport vehicle powertrain technologies ..........4
Table 2.3: Summary and definition of the categorisation of vehicle components and
efficiency improvements in the applied methodology .........................................6
Table 2.4: Example – basic components for a hydrogen fuel cell electric vehicle................7
Table 2.5: Summary of basic vehicle characteristics used in the analysis...........................8
Table 2.6: Assumptions on basic light duty vehicle and motorcycle characteristics used in
the analysis........................................................................................................9
Table 2.7: Assumptions on basic heavy duty vehicle characteristics used in the analysis.11
Table 2.8: Rigid goods vehicles1 over 3.5 tonnes licensed by gross weight and body type,
Great Britain, annually: 2010 * .........................................................................12
Table 2.9: Summary of technology specific characteristics used in the analysis ...............13
Table 2.10: Summary of key data sources for the technology specific characteristics used in
the analysis of light duty vehicles and motorcycles ..........................................15
Table 2.11: Summary of key data basis/sources for the technology specific characteristics
used in the analysis of heavy duty vehicle .......................................................16
Table 2.12: Summary of the efficiency improvement technologies included in the car and
van analysis.....................................................................................................17
Table 2.13: Summary of the efficiency improvement technologies included in the motorcycle
analysis............................................................................................................20
Table 2.14: Summary of the efficiency improvement technologies included in the heavy duty
vehicle analysis................................................................................................21
Table 3.2: Summary of the light duty vehicle and motorcycle powertrain efficiency
assumptions used in the study analysis ...........................................................26
Table 3.3: Summary of the heavy duty vehicle powertrain efficiency assumptions used in
the study analysis ............................................................................................26
Table 3.4: Summary of the technology efficiency assumptions for the car and van analysis27
Table 3.5: Summary of the technology efficiency assumptions for the motorcycle analysis28
Table 3.6: Summary of the technology efficiency assumptions for the HDV analysis........29
Table 3.7: Summary of the differences found between gCO2/km values from NEDC and
Autocar Magazine tests ...................................................................................30
Table 3.8: Summary of the basic real-world efficiency uplift assumptions used in the study
analysis for different vehicle powertrains..........................................................31
Table 3.9: Summary of the sources/methods used to estimate the basic real-world
efficiency uplift assumptions used in the study analysis for different vehicle
powertrains ......................................................................................................31
Table 3.10: Additional real-world correction factors used in the study analysis ...................32
Table 4.1: Summary of alternative potential options for forward projecting capital costs...34
Table 4.2: Summary of the additional heavy duty powertrain technology capital cost
assumptions used in the study analysis ...........................................................39

Ref: AEA/ED57444/Issue Number 2 vii
Table 4.3: Summary of the basic component technology capital cost assumptions used in
the study analysis ............................................................................................40
Table 4.4: Summary of the technology cost assumptions for the car and van analysis .....42
Table 4.5: Summary of the technology cost assumptions for the motorcycle analysis ......43
Table 4.6: Summary of the technology cost assumptions for the heavy duty vehicle
analysis............................................................................................................44
Table 5.1: Summary of Light Duty Vehicle (car and van) technology compatibility /
stackability .......................................................................................................46
Table 5.2: Summary of Heavy Duty Vehicle (all trucks, buses and coaches) technology
compatibility / stackability.................................................................................47
Table 5.3: Summary of motorcycle and moped technology compatibility / stackability ......48
Table 5.4: Deployment assumptions for passenger car efficiency improvement
technologies.....................................................................................................51
Table 5.5: Deployment assumptions for van efficiency improvement technologies ...........52
Table 5.6: Deployment assumptions for motorcycle efficiency improvement technologies53
Table 5.7: Rigid truck and articulated trailer body types....................................................54
Table 5.8: Deployment assumptions for small rigid truck efficiency improvement
technologies.....................................................................................................55
Table 5.9: Deployment assumptions for large rigid truck efficiency improvement
technologies.....................................................................................................56
Table 5.10: Deployment assumptions for articulated truck efficiency improvement
technologies.....................................................................................................57
Table 5.11: Deployment assumptions for construction truck efficiency improvement
technologies.....................................................................................................58
Table 5.12: Deployment assumptions for bus efficiency improvement technologies ...........59
Table 5.13: Deployment assumptions for coach efficiency improvement technologies .......60
Table 6.1: Additional assumptions on the trajectory of carbon intensity and price of energy
carriers from 2010-2050, excluding biofuel effects ...........................................63
Table 7.1: Increases in van efficiency and reported in the DfT new van counterfactual
study..............................................................................................................120
Table 7.2: Increases in van efficiency as reported in the DfT new van counterfactual study,
projected from 2020 to 2050 ..........................................................................121
Table 7.3: BAU estimates on evolution of fuel consumption benefit (penalty) for base
conventional diesel vehicles - figures indicate benefit/penalty compared to
previous year .................................................................................................125
Table 7.4: Correlation between the truck categories used in this study and vehicle
categories in AEA-Ricardo (2011)..................................................................125
Table 7.5: Recommended projection of changes in road vehicle fuel consumption in the
absence of any government policy to reduce GHG emissions* ......................130

Ref: AEA/ED57444/Issue Number 2 viii
Table of figures
Figure 2.1: Estimated breakdown of basic vehicle cost for passenger cars..........................9
Figure 6.1: Trajectory for Passenger Car Efficiency improvement cost-effectiveness by
technology, £ per gCO2e/km reduction * ..........................................................64
Figure 6.2: Trajectory for Passenger Car Efficiency and Costs for Petrol ICE, PHEV and
BEV .................................................................................................................65
Figure 6.3: Trajectory for Passenger Car Efficiency, Direct gCO2/km and Cost by
technology .......................................................................................................66
Figure 6.4: Analysis results for Passenger Car Efficiency for 2020, 2030 and 2050...........67
Figure 6.5: Analysis results for Passenger Car Capital Costs for 2020, 2030 and 2050.....68
Figure 6.6: Analysis results for 2050 Passenger Car Capital Costs for Best, Low and High
Cost assumptions for key vehicle components.................................................69
Figure 6.7: Trajectory for Van Efficiency and Costs for Diesel ICE, PHEV and BEV ..........71
Figure 6.8: Trajectory for Van Efficiency, Direct gCO2/km and Cost by technology ............72
Figure 6.9: Analysis results for Van Efficiency for 2020, 2030 and 2050............................73
Figure 6.10: Analysis results for Van Capital Costs for 2020, 2030 and 2050 ......................74
Figure 6.11: Trajectory for Motorcycle Efficiency and Costs for Petrol ICE, HEV and BEV ..75
Figure 6.12: Trajectory for Motorcycle Efficiency, Lifecycle gCO2/km and Cost by technology76
Figure 6.13: Analysis results for Motorcycle Efficiency for 2020, 2030 and 2050 .................77
Figure 6.14: Analysis results for Motorcycle Capital Costs for 2020, 2030 and 2050 ...........78
Figure 6.15: Trajectory for Small Rigid Truck Efficiency and Costs for Diesel ICE, HEV and
H2FC ...............................................................................................................82
Figure 6.16: Trajectory for Small Rigid Truck Efficiency, Lifecycle gCO2/km and Cost by
technology .......................................................................................................83
Figure 6.17: Analysis results for Small Rigid Truck Efficiency for 2020, 2030 and 2050.......84
Figure 6.18: Analysis results for Small Rigid Truck Capital Costs for 2020, 2030 and 2050.85
Figure 6.19: Trajectory for Large Rigid Truck Efficiency and Costs for Diesel ICE, HEV and
H2FC ...............................................................................................................86
Figure 6.20: Analysis results for Large Rigid Truck Efficiency, Lifecycle gCO2/km and Cost
by technology...................................................................................................87
Figure 6.21: Analysis results for Large Rigid Truck Efficiencies for 2020, 2030 and 2050....88
Figure 6.22: Analysis results for Large Rigid Truck Capital Costs for 2020, 2030 and 2050.89
Figure 6.23: Trajectory for Articulated Truck Efficiency and Costs for Diesel ICE, HEV and
H2FC ...............................................................................................................90
Figure 6.24: Analysis results for Articulated Truck Efficiency, Lifecycle gCO2/km and Cost by
technology .......................................................................................................91
Figure 6.25: Analysis results for Articulated Truck Efficiency for 2020, 2030 and 2050........92
Figure 6.26: Analysis results for Articulated Truck Capital Costs for 2020, 2030 and 2050..93
Figure 6.27: Trajectory for Construction Truck Efficiency and Costs for Diesel ICE, HEV and
H2FC ...............................................................................................................94

Ref: AEA/ED57444/Issue Number 2 ix
Figure 6.28: Analysis results for Construction Truck Efficiency, Lifecycle gCO2/km and Cost
by technology...................................................................................................95
Figure 6.29: Analysis results for Construction Truck Efficiency for 2020, 2030 and 2050.....96
Figure 6.30: Analysis results for Construction Truck Capital Costs for 2020, 2030 and 205097
Figure 6.31: Trajectory for Bus Efficiency and Costs for Diesel ICE, HEV and H2FC...........98
Figure 6.32: Analysis results for Bus Efficiency, Lifecycle gCO2/km and Cost by technology99
Figure 6.33: Analysis results for Bus Efficiency for 2020, 2030 and 2050 ..........................100
Figure 6.34: Analysis results for Bus Capital Costs for 2020, 2030 and 2050 ....................101
Figure 6.35: Trajectory for Coach Efficiency and Costs for Diesel ICE, HEV and H2FC ....102
Figure 6.36: Analysis results for Coach Efficiency, Lifecycle gCO2/km and Cost by
technology .....................................................................................................103
Figure 6.37: Analysis results for Coach Efficiency for 2020, 2030 and 2050 ......................104
Figure 6.38: Analysis results for Coach Capital Costs for 2020, 2030 and 2050 ................105
Figure 7.1: Number of factors impact on fuel efficiency improvements.............................107
Figure 7.2: Fall in new car CO2 in the UK since 2000.......................................................108
Figure 7.3: Share of diesel in the UK, 2000-2010.............................................................109
Figure 7.4: New car CO2 levels in the EU from 2000 to 2010...........................................109
Figure 7.5: Average new car fuel consumption (petrol two wheel drive vehicles only) in
Litres/km, from 1978 to 2004 .........................................................................110
Figure 7.6: Vehicle weight, 1970-2004.............................................................................111
Figure 7.7: EU-27 Harmonized indices of consumer prices indicate that vehicle prices have
remained constant .........................................................................................112
Figure 7.8: Chart of Motor Spirit Prices in January from 1991 to 2011 .............................114
Figure 7.9: Average new car fuel consumption (registration weighted) Great Britain: 1978-
2010 ..............................................................................................................115
Figure 7.10: Forecast retail petrol price 2012-30, pence per litre .......................................116
Figure 7.11: Evolution of CAFE standards and change in fuel economy, from 1978 to 2019117
Figure 7.12: The CO2 emissions, and fuel efficiency, as a function of time for three different
sized trucks deduced from inventory emission factors. ..................................124
Figure 7.13: The estimated reductions in fuel consumption (and CO2 emissions) as a
function of time for three different sized heavy duty vehicles (as reported to EC
DG CLIMA) ....................................................................................................126
Figure 7.14: The quantities of fuel used by different vehicle types in 2009.........................127

Ref: AEA/ED57444/Issue Number 2 x
Glossary of terms and abbreviations1
BAU Business as usual, i.e. the projected baseline of a trend assuming that
there are no interventions to influence the trend.
BEV Battery electric vehicle, also referred to as a pure electric vehicle, or
simply a pure EV.
CNG Compressed Natural Gas. Natural gas can be compressed for use as a
transport fuel (typically at 200bar pressure).
CO2 Carbon dioxide, the principal GHG emitted by transport.
CO2e Carbon dioxide equivalent. There are a range of GHGs whose relative
strength is compared in terms of their equivalent impact to one tonne of
CO2. When the total of a range of GHGs is presented, this is done in
terms of CO2 equivalent or CO2e.
Diesel The most common fossil fuel, which is used in various forms in a range of
transport vehicles, e.g. heavy duty road vehicles, inland waterway and
maritime vessels, as well as some trains.
DOH Degree of hybridisation. This is usually defined as the percentage of the
total vehicle peak power provided by the electric motor.
EV Electric vehicle. A vehicle powered solely by electricity stored in on-board
batteries, which are charged from the electricity grid.
FCEV Fuel cell electric vehicle. A vehicle powered by a fuel cell, which uses
hydrogen as an energy carrier.
GHGs Greenhouse gases. Pollutant emissions from transport and other
sources, which contribute to the greenhouse gas effect and climate
change. GHG emissions from transport are largely CO2.
HDV Heavy duty vehicles – includes heavy trucks, buses and coaches
HEV Hybrid electric vehicle. A vehicle powered by both a conventional engine
and an electric battery, which is charged when the engine is used.
ICE Internal combustion engine, as used in conventional vehicles powered by
petrol, diesel, LPG and CNG.
IEA International Energy Agency
LDV Light duty vehicles – includes cars and vans
LED Light-emitting diode
Lifecycle
emissions
In relation to fuels, these are the total emissions generated in all of the
various stages of the lifecycle of the fuel, including extraction, production,
distribution and combustion. Also known as WTW emissions when
limited specifically to the energy carrier/fuel.
LNG Liquefied Natural Gas. Natural gas can be liquefied for use as a
transport fuel.
LPG Liquefied Petroleum Gas. A gaseous fuel, which is used in liquefied form
as a transport fuel.
MAC Mobile air conditioning
MACC Marginal Abatement Cost Curve
MPG Miles per gallon
MtCO2e Million tonnes of CO2e.
1
Terms highlighted in bold have a separate entry.

Ref: AEA/ED57444/Issue Number 2 xi
NAEI National Atmospheric Emissions Inventory. This is the UK inventory of air
quality pollutants (AQP) and greenhouse gasses (GHGI).
Natural gas A gaseous fossil fuel, largely consisting of methane, which is used at low
levels as a transport fuel in the EU.
NEDC New European Driving Cycle
NGOs Non-government organisations
NGV Natural Gas Vehicle. Vehicles using natural gas as a fuel, including in its
compressed (CNG) and liquefied (LNG) forms.
OECD Organisation for Economic Co-operation and Development
Petrol Also known as gasoline and motor spirit. The principal fossil fuel used in
light duty transport vehicles, such as cars and vans.
PHEV Plug-in hybrid electric vehicle. Vehicles that are powered by both a
conventional engine and electric motor plus battery, which can be
charged from the electricity grid. The battery is larger than that in an
HEV, but smaller than that in an EV. Typically the form of this vehicle
where the electric motor and ICE work in series is also known as range
extended electric vehicle (REEV)
PTWs Powered two-wheelers
REEV Range extended electric vehicle. This is a specific type of PHEV that
operates with the electric motor and ICE work in series, with the ICE
essentially operating like a generator to top-up the battery.
SOC State of charge for a battery – i.e. how full it is versus total capacity.
SUV Sport utility vehicle
TTW emissions Tank to wheel emissions, also referred to as direct or tailpipe emissions.
The emissions generated from the use of the fuel in the vehicle, i.e. in its
combustion stage.
VVA Variable valve actuation, also known as variable valve timing (VVT)
VVTL Variable valve timing and lift
WTT emissions Well to tank emissions, also referred to as fuel cycle emissions. The total
emissions generated in the various stages of the lifecycle of the fuel prior
to combustion, i.e. from extraction, production and distribution.
WTW emissions Well to wheel emissions. Also known as lifecycle emissions when
limited specifically to the energy carrier/fuel.

Ref: AEA/ED57444/Issue Number 2 1
1 Introduction
1.1 Background
The Committee on Climate Change (CCC) has a number of priorities, including a key priority
of providing advice, analysis and information to Government and the Devolved
Administrations on the setting of carbon budgets.
The CCC utilises a range of analytical tools for informing and developing its
recommendations on the UK’s carbon budgets, including sectoral or economy wide models
with a strong emphasis on reducing GHG emissions as cost-effectively as possible. As part
of its advice for the Fourth Carbon Budget Report (2023-2027), the CCC’s central scenarios
were designed to be consistent with the objective of delivering car, van and HGV fleets that
are almost entirely decarbonised by 2050. According to other CCC analysis it is envisaged
that this level of reduction will be necessary in order to achieve overall transport GHG
reduction objectives since there are fewer options/lower potential for reductions in aviation
and shipping.
As part of its forthcoming 2012 work plan, CCC need to carry out further detailed analysis
that will assess the emissions trajectory and economic costs of transport technology
deployment in the longer term for the period from 2030-2050. In order to do this the CCC
needs to extend its assumptions on the fuel efficiency and capital costs of all relevant vehicle
technologies through to 2050. In addition, there is also a need to review CCC’s current
assumptions for the period 2010-2030 as part of the 2014 review of the Fourth Carbon
Budget. There have been a number of significant new studies in the period since these
assumptions were last developed and updated and a range of estimates available now in the
literature for different vehicle technologies.
The CCC has therefore identified a need to carry out a thorough review and update of its
current assumptions to inform its understanding of the potential future development of road
vehicle technology performance and costs.
1.2 Aims and Objectives
In order to review and update its current assumptions for road transport vehicles, CCC
commissioned this project, with the following main aims and objectives:
iv. Carry out a review of the literature on the fuel efficiency and capital costs associated
with road transport technologies to 2050;
v. Using information from the literature review, develop assumptions for fuel efficiency
and capital costs for each technology to 2050;
vi. Based on these findings advise the CCC on the validity of their default assumption
that there would be no or minimal change to the fuel efficiency and capital costs of
the dominant vehicle technologies in the absence of any government policy to reduce
GHG emissions.
Supplied with the evidence from this project the CCC will be able to take a more informed,
forward looking view of the technically possible, economically viable and realistic deployment
of road transport technologies under different scenarios to 2050. Consequently, the CCC will
be in a better position to advise government on:
• Strategy to support the decarbonisation of the road transport sector;
• The potential impacts of this on future carbon budgets.

1.3 Scope of the Work
The focus of the study was on road transport technologies, with the aim to develop a detailed
understanding of how the uptake of technological options to improve efficiency/reduce GHG
emissions is likely to impact on overall costs and efficiencies of different vehicle classes in
the period 2010-2050.
The primary deliverable for this work is the dataset provided to CCC alongside this report
detailing the projected costs and vehicle efficiencies developed for the study. The purpose of
this report is to provide a summary of the methodological approach and the key sources and
assumptions used to define this dataset and a short summary of the results of the analysis.
1.4 Structure of the Report
This report is structured so that the main methodology and input assumptions are described
in the main body of the report, with the full details of assumptions and references provided in
the appendices. The main body of the report contains the following sections:
2 Methodology, Vehicle and
Technology Definitions
This chapter provides a summary of the general
methodological approach, the vehicle classes and
powertrains combinations assessed and a summary of
the sub-technologies included in the calculations.
3 Efficiency Assumptions This chapter provides a detailed review of the calculation
methodology, key data sources and assumptions used
for the assessment of vehicle efficiency improvements.
4 Capital Cost Assumptions This chapter provides a detailed review of the calculation
methodologies, key data sources and assumptions used
for the assessment of the costs associated vehicle
efficiency improvements.
5 Technology Compatibility and
Deployment Assumptions
This chapter provides a summary of the assumptions
used in the calculations with regards to possibilities for
combination / stacking of different technological options,
and the assumptions on the rates of deployment of the
different technologies used in the calculations.
6 Results: Cost and Efficiency
Trajectories from 2010 to 2050
This chapter provides a summary review of the key
results of the cost and efficiency calculations – the
resulting trajectories in efficiency and costs for different
vehicle classes and powertrains from 2010 to 2050.
7 Evaluation of CCC’s Default
Trajectory Assumptions
This chapter provides an assessment of the validity of
CCC’s default assumption that there would be no or
minimal change to the fuel efficiency and capital costs of
the dominant vehicle technologies in the absence of any
government policy to reduce GHG emissions.
8 References This chapter provides a full list of the references included
in this report, as well as all other principal literature
sources reviewed as part of the study.

2 Methodology, Vehicle and
Technology Definitions
2.1 Vehicle Categorisation
The purpose of first part of the study was to establish the categorisation framework within
which to carry out a literature review of the performance and cost characteristics of road
transport vehicles to 2050, before developing the future trajectory of these characteristics to
2050. Specifically, the objective was to establish a set of vehicle categories for each major
road transport mode, and the powertrain technologies to be considered within each vehicle
category. These needed to be sufficiently disaggregated to enable an accurate estimate of
the emissions trajectory and economic costs associated with deployment of vehicle
technologies, whilst also avoiding unnecessary detail to reduce analytical complexity.
The following categorisation in Table 2.1 was agreed with CCC at the start of the study, with
a short summary of the different powertrain technologies also provided in Table 2.2. It was
not deemed necessary to include separate variants for flex-fuel (i.e. E85) variants of petrol
vehicles, since these would be expected to be essentially identical in performance and incur
minimal additional capital cost (in the order of £100-200) according to industry sources.
Advanced biodiesel fuels (e.g. from biomass-to-liquid or hydrotreated oil processes) are also
not anticipated to have compatibility issues with conventional diesel technology, negating the
necessity of including specific vehicle variants for these fuels.
Table 2.1: Road vehicle categorisation utilised in the study assessment
Mode Category Powertrain Technology
Car Average car Petrol ICE
(defined as an average of the C+D Diesel ICE
market segments for this study) Petrol HEV
Diesel HEV
Petrol PHEV (30km electric range)
Diesel PHEV (30km electric range)
Van Average van Petrol REEV (60km electric range)
(defined according average split Diesel REEV (60km electric range)
across Class I, II and III vans) Battery Electric Vehicle (BEV)
Hydrogen Fuel Cell Vehicle (FCV)
Hydrogen Fuel Cell PHEV
Hydrogen Fuel Cell REEV
Natural Gas ICE *
Heavy Truck Small rigid truck (<15 t GVW)** Diesel ICE
Large rigid truck (>15 t GVW)** Diesel HEV
Articulated truck Diesel Flywheel Hybrid Vehicle (FHV)
Construction Diesel Hydraulic Hybrid Vehicle (HHV)
Buses and Coaches Bus Battery Electric Vehicle (BEV) *
Coach Natural Gas ICE ***
Dual Fuel Diesel-Natural Gas ICE
Motorbikes and mopeds Average motorbike or moped Petrol ICE
Petrol HEV
Battery Electric Vehicle (BEV)
Notes:
* BEVs are only assessed/deemed appropriate for small rigid trucks (often used for urban delivery) and buses.

** DfT statistics on heavy duty vehicle are not fully consistent on the size categorisation used for different statistical
datasets. For example, fuel consumption statistics included ranges ‘Over 7.5t to 14t’ and ‘Over 14t to 17t’, however
licensing statistics include ranges ‘Over 7.5 tonnes up to 15 tonnes’ and ‘Over 15 tonnes up to 18 tonnes’, and
annual vehicle km per vehicle statistics include ranges ‘Over 7.5 to 17t’ and ‘Over 17 to 25t’. For the purposes of
this study an approximate cut-off at around 15 t GVW has been utilised wherever possible.
*** For most vehicle classes it is assumed the form of natural gas used by the vehicle is CNG (compressed natural
gas), however it is most likely that LNG (liquefied natural gas) would be utilised in long-haul operations, where
articulated trucks are typically used.
Table 2.2: Summary descriptions of road transport vehicle powertrain technologies
Powertrain
technology
Summary description
ICE Internal combustion engines are used in conventional vehicles powered by petrol,
diesel, LPG and CNG.
Dual Fuel Dual Fuel diesel-natural engines derived from diesel gas internal combustion engines
have been recently introduced for heavy-duty vehicle applications. In these engines
a small amount of diesel is injected to ensure ignition of the fuel mix, but the majority
of the fuel is natural gas mixed with the incoming air. The advantage of this
technology is that (a) it uses compression ignition engine technology that is higher in
efficiency than spark-ignition engines used in dedicated natural gas vehicles, and (b)
if the vehicle runs out of natural gas it can operate entirely on diesel. The diesel
substitution rate depends on the integration of the fuel system and the type of vehicle
operation, with typical rates varying from 40 to 80% (TSB 2011).
FHV Flywheel hybrid vehicles. A vehicle powered by a conventional engine where surplus
or otherwise wasted (i.e. through braking) mechanical energy can be stored for short
periods in a flywheel system for use later to improve overall vehicle efficiency.
HHV Hydraulic hybrid vehicles. A vehicle powered by a conventional engine where surplus
or otherwise wasted energy (i.e. through braking) can be stored in a hydraulic system
for use later to improve overall vehicle efficiency.
HEV Hybrid electric vehicle. A vehicle powered by both a conventional engine and an
electric battery, which is charged when the engine is used. Surplus or otherwise
wasted energy (i.e. through braking) can be stored for use later to improve overall
vehicle efficiency. HEVs can have a very limited electric-only range (as full-hybrids),
but run only on electricity produced from the main petrol or diesel fuel.
PHEV Plug-in hybrid electric vehicles. These vehicles are a combination of HEVs and BEVs.
They vehicles operate in a similar way to HEVs, but have a larger battery (smaller
than BEVs) and can be plugged in and recharged directly from the electricity grid to
allow for electric-only drive for longer distances. These vehicles can be designed
with the ICE and electric motor in parallel configurations, or in series (where they are
often referred to as REEVs).
REEV Range extended electric vehicles are a form of PHEV that has the ICE and electric
motor operating in series. The ICE essentially acts as a generator and does not
provide direct traction to the wheels of the vehicle.
BEV Battery electric vehicles. A vehicle powered entirely by electrical energy stored
(generally) in a battery, recharged from the electricity grid (or other external source).
H2 FCV Hydrogen fuel cell electric vehicles. A vehicle powered by electrical energy obtained
from stored hydrogen which is converted into electricity using a fuel cell.
The categorisation in Table 2.1 was developed in an attempt to strike the best balance
between complexity, analytical needs and possible differences between different categories
of vehicle within a particular transport mode and the available time and study resources.
For passenger cars, vans, and motorbikes and mopeds only a single vehicle category for
each of these vehicle types was utilised. This is consistent with the approach taken for long-
term energy modelling often used in overall economy-wide analysis. Also, if it were desirable

in the future to split these categories further into for example different size categories for cars
and vans (e.g. to facilitate modelling of downsizing in cars, or a shift to larger vans) this could
be achieved relatively simply by CCC outside of the project by scaling the output to key size
parameters. This is because the technologies utilised for different sized light-duty vehicles
are essentially the same and differences in the relative performance and costs of different
technology components for different vehicle sizes are relatively minor.
However, the same cannot be said for heavy duty vehicles (heavy trucks, buses and
coaches), which are much more diverse in their relative sizes, technical specifications and
typical usage patterns. As a result, the technologies likely to be employed, their
effectiveness and their costs will vary significantly between different categories. For
example, in smaller trucks used typically in urban delivery cycles, vehicle light-weighting and
hybrid powertrains will have a greater impact due to significant stop-start activity and speed
fluctuations, compared to heavier trucks used for regional delivery or long haul operations.
Conversely the application of aerodynamic improvements has little effect on vehicles
predominantly used at lower urban speeds, but can achieve significant benefits for vehicles
travelling at high speeds on major roads and motorways for a significant proportion of their
activity. For heavy trucks the vehicle purpose and body type also can have a significant
effect on the application of measures.
For example trucks used in freight operations with relatively uniform shaped configurations
(i.e. box, curtain sided and refrigerated body types) can to utilise aerodynamic measures on
their bodies or trailers that could significantly reduce their fuel consumption. However, trucks
with more irregular or unpredictable body shapes due to their purpose (e.g. concrete mixers,
tankers, vehicle carriers) or load (e.g. tipper trucks, flat bed trailers) have fewer options/lower
potential here. These significant differences in operational profiles and technical
characteristics mean that it would not be appropriate to characterise trucks using a single
vehicle category. In contrast to cars and vans, it would not be possible to apply simple
scaling factors to the data for a single truck category at a later date (i.e. after the study work
was completed) should it be necessary characterise the costs and performance of different
types of trucks. For these reasons, it was deemed more robust to include up-front a variety
of truck types in the list of vehicle categories that were covered. Hence, heavy duty trucks
were split into four different categories, as set out in Table 2.1. For the same reasons, we
propose to characterise buses and coaches as two separate vehicles categories rather than
as a combined, single bus/coach category.
Some vocational vehicles have significantly different restrictions on the technologies that can
be applied and/or their effectiveness (and also different activity profiles for modelling).
Construction vehicle body types (tipper, concrete mixer and skip-loader) are the most
significant category account for over 20% of rigid vehicles according to DfT statistics (and
tipper truck semi-trailers for articulated vehicles account for around 8% of all semi-trailers
according to trailer statistics from CLEAR, 2010). Hence, it was decided to include a specific
category for construction vehicles.
In terms of the weight categorisation used to define different sizes of rigid trucks – this was
informed by DfT statistical definitions. DfT statistics on heavy duty vehicle are not fully
consistent on the size categorisation used for different statistical datasets. Some datasets
include ranges up to 14t or 15t GVW and then ranges above this (e.g. vehicle numbers from
licensing statistics and fuel consumption statistics), and others include ranges up to 17t or
18t GVW and then ranges above this (e.g. fuel consumption and activity/annual km per
vehicle statistics). For the purposes of this study an approximate cut-off at 15 t GVW has
been used and data scaled accordingly.
Heavy duty truck PHEVs were excluded from the powertrain category as the battery power is
used primarily to power auxiliary equipment or keep the vehicle's cab at a comfortable
temperature at a job site, rather than for providing motive power. This option was instead
assessed as a separate add-on technology (see Section 2.4).

2.2 Methodological Overview
The central aim of the project was to review the literature on the fuel efficiency and capital
costs associated with road transport technologies to 2050, and to develop assumptions for
fuel efficiency and capital costs for each technology to 2050. One of the principal objectives
of the work was also to help CCC gain a better understanding of how the improvements in
efficiency and resulting capital costs break down into different impact areas (e.g. engine
efficiency improvements, improved aerodynamics, reduced weight, etc) and components
(body and chassis, powertrain components). CCC also would like to better understand to
what degree the overall efficiency of PHEVs (and related REEVs) were affected due to the
application of technologies to improve the fuel efficiency of the vehicles would result merely
from the addition of the electric powertrain, and what degree of improvement would result
from additional factors affecting the ICE drive only.
The overall methodological approach developed was to categorise different efficiency and
cost component technologies into a series of ‘Basic Component’ and ‘Efficiency
Improvement’ categories, as summarised in Table 2.3, under which individual component
technologies would be included. An example is given for the basic components in a fuel cell
vehicle in Table 2.4.
Table 2.3: Summary and definition of the categorisation of vehicle components and efficiency
improvements in the applied methodology
Area Component Definition
Basic
components
Powertrain Includes combustion engines and transmission, electric
motors, fuel cells, electric drivetrain components, dual-fuel
systems, flywheels or hydraulic hybrid components, etc.
Energy
storage
Includes conventional liquid fuel tanks, gaseous or liquid
storage systems for natural gas or hydrogen, electric storage
medium (i.e. batteries or capacitors)
Glider Vehicle chassis and non-powertrain specific components,
excluding energy storage.
Efficiency
improvements
Powertrain
efficiency
Includes the application of additional* or alternative technical
measures aimed at generating improvements to the
powertrain systems (e.g. to conventional engine or
transmission efficiency)
Aerodynamics Technical options that are applied to reduce aerodynamic
drag and thereby reduce motive power requirements and
improve overall vehicle efficiency.
Rolling
resistance
Technical options that are applied to reduce rolling
resistance from tyres/wheels and thereby reduce motive
power requirements and improve overall vehicle efficiency.
Vehicle weight Technological options that are applied to reduce the overall
weight of the vehicle and thereby reduce motive power
requirements and improve overall vehicle efficiency.
Other options Other technical options not readily applying into the other
categories (e.g. improvement in the efficiency of auxiliaries,
thermo-electric heat recovery, etc)
Complete
vehicle
Real-World
Efficiency
% Uplift from test-cycle based efficiency figures to reflect the
actual typical in-use efficiency of the vehicle.
Notes: * Does not include general improvements to the core powertrain technology (e.g. general improvements in fuel cell
efficiency), which are accounted for in the ‘Basic components area’.

Table 2.4: Example – basic components for a hydrogen fuel cell electric vehicle
Area Component category Component
Basic
components
Powertrain 1. Fuel cell,
2. Electric motor,
3. Other electric powertrain components
Energy storage 1. Hydrogen storage,
2. Small battery
Glider Everything else
The total capital cost and efficiency improvements achieved under each of the categories in
Table 2.3 is calculated based on the appropriate combination of the different sub-component
options included within them. For capital costs, the category total are calculated on an
additive basis, i.e.:
Total Capital Cost = Cost Technology A + Cost Technology B + Cost Technology C + …
However, overall efficiency improvements are not additive and are calculated in a
multiplicative way from individual efficiency components, e.g. for 3 technical options (A, B
and C) achieving 3%, 4% and 5% energy savings individually:
Overall efficiency improvement = 1 – ((1 – 3%) x (1 – 4%) x (1 – 5%))
= 1 – (97% x 96% x 95%) = 11.54% < (3% + 4% + 5%)
There were a significant number of technical options for improving efficiency identified (see
Section 2.4), which are not all mutually compatible/stackable and also may have upper limits
in their levels of deployment (which are discussed in more detail in Chapter 5). Furthermore,
the purpose of this study is to establish what the average/typical change in vehicle
efficiencies and costs might be going forwards. Therefore in order to calculate this it was
necessary to generate the estimated average % deployment of each technology across the
new vehicle fleet. This therefore becomes a factor in the cost and efficiency calculations, i.e.
for technology ‘A’:
Net cost (A) = Basic cost (A) x % Deployment (A)
Net efficiency (A) = Basic efficiency saving (A) x % Deployment (A)
Further details on the assumed deployment levels for individual technology options is
provided in Chapter 5, bearing in mind incompatibilities and natural limits.
The final part of the methodological approach involves an estimation of the real-world fuel
efficiency of the final vehicle, using a percentage uplift factor on the test-cycle based fuel
efficiency figure. This is included in an attempt to factor in a range of considerations that
affect the performance of vehicles in the real-world versus the specific conditions used in the
calculation of fuel consumption using regulatory (or other) test-cycles. Real-world versus
test-cycle efficiency and the assumptions used in developing suitable uplift factors are
discussed in more detail in Section 3.2.3.
2.3 Vehicle Characteristics
In order to generate the characteristics of future vehicles of different powertrain types it is
necessary to have both an accurate description of the current baseline vehicles and likely
trends in key vehicle characteristics. These characteristics have been broadly split into two
categories for further discussion:

Basic characteristics: these are the core characteristics that define the base vehicle
technology starting point in terms of price, efficiency, weight/size and performance.
Technology specific characteristics: these are often time dependant
characteristics that influence the overall specification and performance of vehicles
with different powertrain types – e.g. all electric range, degree of hybridisation (DOH),
proportion of operation in different fuel modes (e.g. for PHEVs, dual fuel diesel-
natural gas trucks).
2.3.1 Basic characteristics
Table 2.5 provides a summary of the basic vehicle characteristics and their reason for
inclusion. The following sections provide an overview of the specific assumptions that were
used in the analysis subdivided into light duty (cars, vans and motorcycles) and heavy duty
(trucks, buses and coaches) vehicles.
Table 2.5: Summary of basic vehicle characteristics used in the analysis
Element Purpose for inclusion
Basic capital price, excluding VAT (£) Used in combination to estimate the basic capital cost of a
vehicle minus the manufacturer and dealer margins, from the
basic capital price, excluding VAT.
Average margin for vehicle
manufacture and sales (%)
Base new vehicle efficiency (MJ/km) The starting point for all new vehicle efficiency calculations on
a test-cycle basis (i.e. excluding real world impacts on fuel
consumption).
Max power (kW) Used in the calculation of capital costs for components that
scale in cost approximately with kW output (e.g. engines,
motors, fuel cells, etc).
Kerb and/or gross weight (kg)* Used in combination with max power to project likely future
changes in kW that will affect total capital costs.Power/Weight (kW/kg)
Average annual km /year To allow the estimation annual fuel costs**.
New vehicle lifetime, years To allow the estimation of lifetime fuel costs**.
Notes: * Kerb weight is used for light duty vehicles and motorcycles. Gross weight has been used for heavy
duty vehicles, as this is more relevant for power/weight ratio based calculations for these types of
vehicles. Kerb weight is used for scaling costs of aftertreatment systems and non-battery electric
powertrain costs for heavy duty vehicles – judged as a better measure of the physical size of the
vehicle for these elements (giving more realistic variations than GVW, compared to other datasets).
** This calculation is useful to get a closer idea on the likely overall changes in total costs over time and
in the cost-effectiveness of different powertrains, which is particularly helpful in understanding likely
take-up rates of technologies in commercial vehicles.
For the average margin for vehicle manufacture and sales, it is assumed that the figure
developed by EE (2011) for passenger cars is broadly applicable to other vehicle types in the
absence of alternative sources. This figure of 24.3% (see Figure 2.1) has been used in
preference to a slightly lower value of 16.8% from TNO (2006), since it has been more
recently developed and tested with stakeholders in the UK. The main difference between the
two estimates is an additional 6.3% for logistics and marketing is included in the EE (2011)
estimate. It was identified at the workshop organised to discuss draft results with key experts
(held on 2 February 2012 at CCC’s offices) that the margins for different modes would likely
be quite different (likely to be higher for heavy duty vehicles, which are sold in much lower
numbers and usually with relatively bespoke specifications). However, no specific
information could be identified that would allow the development/utilisation of figures different
from those provided in EE (2011).

Figure 2.1: Estimated breakdown of basic vehicle cost for passenger cars
75.7%
6.5%
11.5%
6.3%
24.3%
Basic vehicle cost
(excl. VAT)
OEM margin
Dealer margin
Logistics and
marketing
Source: EE (2011)
2.3.1.1 Light Duty Vehicles and Motorcycles
The following Table 2.6 provides a summary of the assumptions and sources for the basic
vehicle characteristics of light duty vehicles and motorcycles used in this study. For
passenger cars, the base data is mainly based on analysis for the Low Carbon Vehicle
Partnership from EE (2011) on the UK car market for the average of C+D market segments
(lower medium and upper medium cars). Van power and weight assumptions are based on
a detailed dataset (based on outputs from the SMMT’s MVRIS database2
) on van new
registrations previously used by in analysis for DfT (AEA, 2009). For motorcycles, estimates
are based on a range of sources for different motorcycle sizes scaled using data from DfT
licensing statistics (2011).
As already indicated, it is assumed that the margin for vehicle manufacture and sales figure
developed by EE (2011) for cars is broadly applicable to other vehicle types in the absence
of alternative sources.
Table 2.6: Assumptions on basic light duty vehicle and motorcycle characteristics used in the
analysis
Mode Element Powertrain 2010 Figure Source
Car Basic capital price (£) All £17,817 (1)
Average margin (%) All 24.3% (1)
New vehicle efficiency (MJ/km) Petrol ICE 2.321 (2)
Diesel ICE 1.863 (2)
New vehicle lifetime (years) All 14 (3)
Average annual distance (km) All 14,434 (11)
Max power (kW) Petrol 112 (4)
Diesel 106 (4)
Power/Weight (kW/kg) Petrol 0.0796 (1)
Diesel 0.0753 (1)
Total vehicle kerb weight (kg) Petrol 1407 (1)
Diesel 1407 (1)
Van Basic capital price (£) All £15,000 (3)
2
https://www.smmt.co.uk/members-lounge/member-services/market-intelligence/vehicle-data/mvris-new-vehicle-registrations-uk/

Diesel ICE 2.429 (6)
Max power (kW) Petrol 69 (7)
Diesel 82 (7)
Diesel 0.0459 (7)
Kerb weight (kg) Petrol 1,375 (7)
Diesel 1,786 (7)
Motorcycles Basic capital price (£) All £7,720 (8)
Max power (kW) Petrol 67.5 (10)
Kerb weight (kg) Petrol 166 (10)
Notes:
(1) EE (2011)
(2) Calculated from average new car CO2 emission factors in gCO2/km for the C+D market segments from
SMMT (2011) and petrol/diesel CO2 conversion factors from DCF (2011)
(3) AEA indicative estimate for typical UK vehicle broadly consistent with UK statistics
(4) Power for average petrol/diesel car from EE (2011) scaled to relative difference in petrol/diesel car power
from TNO (2011)
(5) Assumed similar to that for cars from EE (2011)
(6) Estimated relative to car efficiencies from average fleet emission factor for average car versus average van
for fuel type from DCF (2011)
(7) Average based on 2008 MVRIS database for new van registrations in the UK
(8) Indicative estimate calculated from DfT vehicle licensing statistics (DfT, 2011) for top 10 models and
Motorcycle News (MCN 2011) for motorcycle specifications and prices
(9) Based on NAEI speed-emission calculations for motorcycle fleet (test-cycle based)
(10) Estimated from DfT vehicle licensing statistics dataset (DfT, 2011) and data from MCN (2011)
(11) Based on DfT statistics (2011) for cars, vans (estimated similar to 3.5-7.5t truck) and motorbikes.
2.3.1.2 Heavy Duty Vehicles
The following Table 2.7 provides a summary of the assumptions and sources for the basic
vehicle characteristics of heavy duty vehicles used in this study. Typical truck prices are
based on a dataset sourced from the UK’s Freight Transport Association (FTA) that is
provided in FBP (2010). Other vehicle characteristics are largely based on datasets and
analysis from recent work for the European Commission by AEA and Ricardo (AEA-Ricardo,
2011). Base datasets for construction vehicles are based on those of small/large rigid trucks
and articulated trucks, weighted using information on the split of construction body types
from DfT (2011) licensing statistics for rigid trucks (see Table 2.8) and CLEAR (2010) for
semi-trailers for articulated trucks (tipper trailers account for around 8.3% of new trailer
registrations).
As already indicated, it is assumed that the margin for vehicle manufacture and sales figure
developed by EE (2011) for cars is broadly applicable to other vehicle types in the absence
of alternative sources.
Note: The test-cycle based vehicle efficiencies for trucks are indicative and are based on
average truck activity on urban/rural/motorway roads. In reality there are very significant
operational/mission characteristics for different types of truck which have a marked impact on
their fuel consumption. The figures are not equivalent to those for passenger cars and vans.

Table 2.7: Assumptions on basic heavy duty vehicle characteristics used in the analysis
Small Rigid Basic capital price (£) All £36,445 (1)
Truck Average margin (%) All 24.3% (2)
Heavy Truck Base new vehicle efficiency (MJ/km) Diesel ICE 6.637 (3)
Heavy Truck New vehicle lifetime (years) All 12 (4)
Heavy Truck Max power (kW) All 155.3 (5)
Heavy Truck Power/Weight (kW/kg) All 0.0155 (6)
Gross vehicle weight (kg) All 10,000 (4)
Kerb weight (kg) All 5,840 (3)
Large Rigid Basic capital price (£) All £59,676 (1)
Base new vehicle efficiency (MJ/km) Diesel ICE 11.382 (3)
Max power (kW) All 249.6 (5)
Power/Weight (kW/kg) All 0.0104 (7)
Articulated Basic capital price (£) All £76,368 (1)
Construction Basic capital price (£) All £62,272 (10)
Bus Basic capital price (£) All £130,000 (13)
Max power (kW) All 152 (15)
Coach Basic capital price (£) All £130,000 (17)

Max power (kW) All 220 (15)
Notes:
(1) FBP (2010)
(2) Assumed similar to that for cars from EE (2011)
(3) Estimate based on AEA-Ricardo (2011) analysis
(4) AEA estimate for typical vehicle based on UK statistics (EU statistics for bus lifetimes)
(5) Calculated from max power and power/weight ratio
(6) Based on data for a 7.5t vehicle from AEA-Ricardo (2011), estimate 0.5% p.a. increase in power
(7) Based on an average of data for a 18t and 26t vehicle for 2010 from AEA-Ricardo (2011); estimate 0.5%
p.a. power increase to 2050
(8) Estimated from DfT Licensing Statistics (2011) datasets
(9) Based on data for an average 44t vehicle for 2010 from AEA-Ricardo (2011); estimate 0.5% p.a. power
increase to 2050
(10) Estimated from FBP (2010) and DfT Licensing Statistics (2011) datasets
(11) DfT (2010) - freight best practice programme publication on tipper trucks
(12) AEA estimate - assumed to be similar to large rigid truck
(13) AEA (2007)
(14) Estimate based on an average of small and large rigid trucks
(15) Calculated based on averaged bus or coach data from Alexander Dennis (2012)
(16) Based on DfT statistics (2011) for heavy trucks. Construction trucks estimated based on approximate split
of rigid and articulated vehicles available from DfT statistics (2011) and CLEAR (2010). Annual km for
buses and coaches based on datasets sourced for the UK in AEA-Ricardo (2011).
(17) AEA estimate – assumed to be similar to bus
Table 2.8: Rigid goods vehicles1 over 3.5 tonnes licensed by gross weight and body type,
Great Britain, annually: 2010 *
1000s vehicles by
Body Type Up to 7.5 t
Over 7.5t
up to 15 t
Over 15 t
up to 18 t
Over 18 t
up to 26 t Over 26 t Total
2
Box Van 48.7 8.4 13.0 2.7 0.2 73.0
Tipper 17.7 1.3 4.2 4.8 14.9 42.8
Curtain Sided 10.7 2.1 9.7 5.3 0.2 28.1
Dropside Lorry 10.5 1.8 4.7 3.2 0.2 20.4
Flat Lorry 6.7 1.7 3.4 5.4 1.3 18.5
Refuse Disposal 0.9 1.1 1.7 10.8 1.6 16.1
Insulated Van 5.6 2.7 3.9 2.1 0.1 14.4
Skip Loader 1.0 0.6 5.6 1.1 3.3 11.6
Goods 3.0 0.9 1.2 1.5 0.8 7.4
Panel Van 7.1 0.1 0.1 0.0 0.0 7.3
Tanker 0.4 0.5 2.3 2.8 1.2 7.3
Street Cleansing 2.3 2.4 0.4 0.1 0.0 5.1
Livestock Carrier 3.5 0.3 0.1 0.2 0.0 4.2
Car Transporter 1.1 0.4 1.0 1.3 0.2 4.1
Concrete Mixer 0.0 0.1 0.4 2.0 1.2 3.8
Tractor 0.2 0.1 0.3 0.8 2.1 3.5
Skeletal Vehicle 0.6 0.3 0.5 0.3 0.3 1.9
Tower Wagon 1.7 0.1 0.0 0.0 0.0 1.8
Luton Van 1.3 0.1 0.1 0.0 0.0 1.6
Special Purpose 0.5 0.3 0.3 0.2 0.1 1.3
Specially Fitted Van 0.7 0.2 0.2 0.1 0.0 1.2
Van 1.1 0.1 0.1 0.0 0.0 1.2
Not Recorded 0.6 0.2 0.2 0.1 0.0 1.1
Truck 0.6 0.1 0.2 0.1 0.1 1.0
Others 2.2 0.9 1.3 0.8 0.5 5.8
Overall Total 128.5 26.6 54.9 45.9 28.5 284.4

1000s vehicles by
Body Type Up to 7.5 t
Over 7.5t
up to 15 t
Over 15 t
up to 18 t
Over 18 t
up to 26 t Over 26 t Total
2
% Overall Total 45.2% 9.4% 19.3% 16.1% 10.0% 100%
Construction Total 18.7 1.9 10.2 7.9 19.5 58.3
% Construction Total 32.1% 3.3% 17.5% 13.6% 33.4% 100%
% Overall Total 6.6% 0.7% 3.6% 2.8% 6.8% 20.5%
Study Category Small Rigid Large Rigid
Source: DfT Licensing statistics (2011)
Notes: * Body types with cells highlighted in green are assumed to be in the construction vehicles category for
the purposes of this study.
2.3.2 Technology specific characteristics
The following section provides a summary of the key technology specific attributes used to
define the relative performance and costs of different powertrain options, why they are
needed and general sources for assumptions and trajectories to 2050 (where relevant). A
summary description of these elements is provided in Table 2.9 below.
Table 2.9: Summary of technology specific characteristics used in the analysis
Element Description and purpose
ICE range (km)
(by powertrain type)
This is the assumption on required vehicle range operating in
ICE mode. May also be used to allow for initial reduced ranges
of natural gas fuelled vehicles if appropriate.
It is used in combination with the calculated vehicle efficiency
to calculate the required sizing of liquid fuel tanks and natural
gas storage tanks in the calculation of the capital costs of
these components.
Hydrogen range (km)
This is the assumption on required vehicle range operating on
hydrogen fuel.
to calculate the required sizing of hydrogen storage tanks in
the calculation of their capital costs.
Electric range (km)
This is the assumption on required vehicle range operating on
stored electricity.
and usable SOC (see below) to calculate the required sizing of
batteries/electric storage (in kWh) in the calculation of their
capital costs.
Distance in fuel mode 1 (%)
For powertrain types that can operate using more than one
fuel (i.e. dual-fuel (ICE/H2/NG), this is the average percentage
of the total km travelled by the vehicle in fuel mode 1. For the
purposes of the study analysis, fuel mode 1 is taken to be
petrol/diesel/hydrogen as appropriate for PHEVs, REEVs and
dual-fuel diesel-natural gas powertrains, as appropriate.
This factor is used in the calculation of the average net vehicle
efficiencies (and greenhouse gas emissions from fuel
consumption) of different powertrain options.

Element Description and purpose
Basic real-world % increase
This is an estimate of the typical differential between the
vehicle efficiency on typical/regulatory test cycles versus
performance in real-world driving conditions. There is some
evidence that the differential is larger for some powertrain
technologies, compared to conventional ICE equivalents.
These assumptions are used to provide an adjustment to the
test-cycle based calculations as the final stage in the analysis,
so that a closer view can be obtained on the relative
performance of different powertrain options.
There is also some evidence that there is a larger differential
between test-cycle and real-world performance for the most
efficient vehicles within a given powertrain type. Additional
scaling factors are therefore also be applied on top of these
basic factors to account for a further widening of differentials
after additional efficiency technologies have been applied.
ICE sizing as % max power
This is defined in the analysis calculations as the percentage
of the vehicle’s total combined peak/maximum power (i.e. ICE
kW + electric motor kW) provided by the internal combustion
engine. It is therefore the opposite of the degree of
hybridisation (defined as the percentage of the total vehicle
peak power provided by the electric motor).
It is used to calculate the required ICE and electric motor
power sizing in the calculation of their capital costs.
Battery usable SOC for electric range This is the safety margin built into battery sizing calculations
for a given range requirement and vehicle efficiency (in MJ/km)
to take into account battery degradation over the usual working
life of the vehicle and provide a buffer for hybrid operation use.
This is used in the calculation of the battery capital costs.
Ratio fuel cell size to max power This is the reduction in fuel cell size/power rating possible for
H2FC REEV powertrains due to the different operational
requirements versus a regular H2FC vehicle (i.e. in an REEV
the fuel cell doesn’t have to provide the full peak power).
This is used in the calculation of the fuel cell capital costs.
% Max power ICE for electrified
drivetrains (by powertrain type)
This represents the total combined peak power output of the
electric motor and ICE (where relevant) as a proportion of the
peak power of the comparable conventional ICE vehicle. For
HEVs, PHEVs and REEVs the proportions are typically: 125%
for HEV/PHEV and 138% for REEVs. For fuel cell and battery
electric vehicles, there is a reduced electric engine peak power
rating (typically 85-90%) needed to achieve comparable
performance to a conventional ICE. Most electric motors
deliver full torque over a wide RPM range, so the performance
per kW peak power is not equivalent to an ICE, which has a
limited torque curve.
This is used in the scaling of electric motor and ICE power
ratings for the calculation of the capital costs.
# New vehicles/year Approximate figures on the numbers of new vehicles and the
total UK fleet size for a given vehicle category are used in the
estimation of future capital cost reductions of fuel efficiency
technology based on their levels of deployment into the new
vehicle fleet using learning rate methodologies.
# Fleet vehicles
2.3.2.1 Light Duty Vehicles and Motorcycles
The following section provides a summary of the data sources for assumptions used to
define the key technology specific attributes used in the calculations for light duty vehicles

and motorcycles. A summary of these sources is provided in Table 2.10 below. Full details
on the specific values for the period 2010-2050 used in the analysis for light duty vehicles
and motorcycles is provided in Appendix 1.
In general data has been sourced from EE (2011) or TNO (2011) as the most current
relevant data sources for passenger cars. It is assumed in the absence of conflicting specific
information that in most cases the technology specific characteristics are analogous for vans
(there is good evidence for this in other related studies considering both cars and vans by
TNO and AEA) and for motorcycles (where there is very little or no information available in
the literature at all).
In general there is little detailed information on real-world uplift factors used to convert test-
cycle based fuel consumption into real-world values, so the figures used are generally
approximations based on a number of sources. This subject is discussed in more detail in
Section 3.2.3.
Table 2.10: Summary of key data sources for the technology specific characteristics used in
the analysis of light duty vehicles and motorcycles
Element Cars Vans Motorcycles
ICE range (km)
EE (2011), JEC
(2011), NGVA (2012)
NGVA (2012) AEA estimate
Hydrogen range (km)
EE (2011) Assume similar to
cars
AEA estimate
Electric range (km)
cars
AEA estimate
cars
N/A
(1) (1) (2)
Based on TNO (2011) Assume similar to
cars
Assume similar to
cars
Battery usable SOC for electric
range
(3) Assume similar to
cars
Assume similar to
cars
Ratio fuel cell size to max power EE (2011) Assume similar to
cars
N/A
Estimate based on
TNO (2011)
Assume similar to
cars
Assume similar to
cars
# New vehicles/year DfT vehicle licensing statistics (2011)
# Fleet vehicles Estimated based on # new vehicles and average vehicle lifetime.
Notes:
(1) ICE = from TNO (2011); HEV and BEV = AEA estimates based on LowCVP (2011) and Cenex (2012);
PHEV/REEV = assume average of HEV and BEV; H2FC = assume similar to BEV.
(2) Based on the differential between the test-cycle based CO2 emission factor from the UK NAEI and the
average real-world based emission factor from DCF (2011).
(3) Figure of 70% from TNO (2011) for 2010, which is assumed to increase after 2020 to reach 90% by 2050.
2.3.2.2 Heavy Duty Vehicles
The following section provides a summary of the data sources for assumptions used to
define the key technology specific attributes used in the calculations for light duty vehicles
and motorcycles. A summary of these sources is provided in Table 2.10 below. Full details
on the specific values for the period 2010-2050 used in the analysis for heavy duty vehicles
is provided in Appendix 1.

In general, where data is absent, assumptions have been developed using those for light
duty vehicle technologies as a basis.
No specific information on real-world uplift factors used to convert test-cycle based fuel
consumption into real-world values for heavy duty vehicles, so the base ICE figures used are
generally approximations based on calculations of test-cycle based estimates (from speed-
emission curve calculations used in the AEA-Ricardo, 2011) and comparison with real-world
fuel consumption estimates from DfT. This subject is discussed in more detail in Section
3.2.3.
Table 2.11: Summary of key data basis/sources for the technology specific characteristics
used in the analysis of heavy duty vehicle
Element Heavy Duty Trucks Buses/Coaches
Small Rigid Large Rigid Articulated Construction Bus Coach
ICE range (km)
Based on data from NGVA (2012)
Hydrogen range (km)
Assume similar range to ICE
Electric range (km)
Assume as
for cars
HEVs – as
for cars
HEVs – as
for cars
HEVs – as
for cars
Assume as
for cars
HEVs – as
for cars
Average distance using natural gas for dual-fuel trucks is based on AEA-Ricardo
(2011) and NREL (2000) , with some scaling to account for different usage in
different duty cycles (i.e. substitution at the high end of the range for articulated
trucks, and at the low end for small rigid trucks and buses in mainly urban duty
cycles).
(1), (2) (1), (2) (1), (2) (1), (2) (2), (3) (4)
Assume similar to cars technologies
Battery usable SOC for electric
range
Ratio fuel cell size to max
power
N/A N/A N/A N/A N/A N/A
# New vehicles/year Approximation based on # fleet vehicles and average vehicle lifetime.
# Fleet vehicles Estimated based on DfT Vehicle Licensing Statistics (2011)
Notes:
(1) ICE = Calculated by comparing test-cycle based fuel consumption to DfT fuel consumption statistics (2011).
(2) HEV / BEV = based on 25% of the increase for cars. FHV / HHV = assume similar to HEV. H2FC = assume
similar to BEV.
(3) ICE = Calculated from test-cycle based basic fuel efficiency figure relative to Bus Service Operators Grant
(BSOG) fuel consumption calculations from DfT (2011) sourced for the updates to DCF (2011).
(4) Assume uplifts for coaches are similar to those for large rigid trucks.

2.4 Efficiency Improvement Technologies
This section provides a summary of the specific efficiency improvement technologies
identified and used in the analysis and their classification into five different categories. The
assumed efficiency improvements, capital costs and deployment rates of the different
technologies are detailed in the corresponding Chapter 3, Chapter 4 and Chapter 5,
respectively. The categorisation for the energy improvement technologies is as follows:
Category Abbreviation Coverage
Powertrain efficiency PtrainE All engine, transmission and other
driveline efficiency technologies
Aerodynamics Aero Technologies focussing on reducing
aerodynamic drag
Weight reduction Weight Technical options for weight reduction
Rolling resistance Rres Technologies aimed at reducing rolling
resistance
Other options Other Technologies that do not readily fit into
the other categories
The following Table 2.9 provides a summary of the technological options identified and used
in the analysis for cars and vans. These options are generally consistent with those
identified and used in the most recent analysis for the European Commission on car
regulatory CO2 emissions targets (TNO, 2011) and also for related previous (AEA-TNO,
2009) and ongoing work for vans. This study provides a comprehensive update to previous
work carried for the EC in this area (TNO, 2006), which was used as the basis for the
assumptions in CCC’s Marginal Abatement Cost-Curve Model (AEA, 2009). The review of
the available literature as part of this study has confirmed this is the most comprehensive,
up-to-date and relevant dataset available that covers all the major technological options that
are being developed for near-medium term application in light duty vehicles. A short
description/definition of the technical option is provided in Table 2.9 where it is not
immediately obvious from the name what it covers. A specific definition of the different
technology options was not in general provided in TNO (2011).
Table 2.12: Summary of the efficiency improvement technologies included in the car and van
analysis
Efficiency Improvement Technology Category # Additional description/notes
Petrol - low friction design and materials PtrainE 1 Includes a range of options/low friction
components for reducing friction in the
engine and transmission, e.g. low
tension piston rings, low friction coatings,
improved lubricants.
Petrol - gas-wall heat transfer reduction PtrainE 2 Includes a range of technical options,
such as: charge motion systems
(decreased combustion duration), fast
warm-up, insulation (coolant) and
variable compression ratios.
Petrol - direct injection (homogeneous) PtrainE 3 A variant of fuel injection where the fuel
is highly pressurised and injected directly
into the combustion chamber of each
cylinder, as opposed to conventional
multi-point fuel injection that happens in
the intake tract, or cylinder port.
Petrol - direct injection (stratified charge) PtrainE 4 In some applications, gasoline direct
injection (GDI) enables a stratified fuel
charge (ultra lean burn) combustion for
improved fuel efficiency, and reduced
emission levels at low load.

Petrol - thermodynamic cycle improvements PtrainE 5 Includes multi-port injection such as
HCCI (homogeneous charge
compression ignition) technologies.
HCCI has characteristics of both
homogeneous charge spark ignition
(gasoline engines) and stratified charge
compression ignition (diesel engines).
Petrol - cam-phasing PtrainE 6 VVA, also known as variable valve timing
(VVT), is a generalized term used to
describe any mechanism or method that
can alter the shape or timing of a valve
lift event within an internal combustion
engine (Wiki, 2012a). Cam-phasing is
the simplest form of VVT, with more
sophisticated systems also providing
variable lift to further improve efficiency.
Petrol - Variable valve actuation and lift PtrainE 7
Diesel - Variable valve actuation and lift PtrainE 8
Diesel - combustion improvements PtrainE 9 Further non-specific technology
improvements to the combustion
efficiency of diesel engines.
Mild downsizing (15% cylinder content reduction) PtrainE 10 Reduced cylinder size, with additional
boost (via turbo- or super- charging) to
reach a similar peak power output.
Medium downsizing (30% cylinder content reduction) PtrainE 11
Strong downsizing (≥45% cylinder content reduction) PtrainE 12
Reduced driveline friction PtrainE 13 General improvements made to the
whole driveline to reduce friction.
Optimising gearbox ratios / down-speeding PtrainE 14 Changing gearbox ratios to be more
optimised towards fuel efficiency by
using longer gear ratios leading to lower
engine operation speeds
(downspeeding). This shifts engine
operation into the map area of highest
efficiency, improving fuel economy.
Automated manual transmission (AMT) PtrainE 15 An automated transmission based on a
manual, which has mechanical efficiency
similar to a manual transmission but with
automated gear shifts to optimise engine
speed.
Dual clutch transmission PtrainE 16 A type of semi-automatic or automated
manual transmission that utilises two
separate clutches for odd and even gear
sets. Many also have the ability to allow
the driver to manually shift gears, albeit
still carried out by the transmission's
electro-hydraulics.
Start-stop hybridisation PtrainE 17 A start-stop (or stop-start) system
automatically shuts down and restarts
the engine to reduce the time the engine
spends idling, thereby reducing fuel
consumption and emissions.
Regenerative breaking (smart alternator) PtrainE 18 This system adds regenerative braking
to a start-stop system to recover
additional energy from braking using a
smart alternator, which can be stored in
a battery in order to provide electrical
power to auxiliaries. This reduces the
need for energy to be collected directly
from the engine operation, improving
overall system efficiency.

Aerodynamics improvement Aero 1 Improvement to the vehicles overall
aerodynamics through improvements to
its aerodynamic profile (/shape) as well
as using other options, such as smoother
undercarriages, aerodynamic hub
caps/wheels, wheel well farings, etc.
Low rolling resistance tyres Rres 1 Tyres designed to minimise rolling
resistance whilst still maintaining the
required levels of grip.
Mild weight reduction (~10% reduction in BIW*) Weight 1 Reduction in BIW* through, for example:
use of AHSS (advanced high strength
steels), aluminium, or composite
materials (in the future).
Medium weight reduction(~25% reduction in BIW*) Weight 2
Strong weight reduction (~40% reduction in BIW*) Weight 3
Lightweight components other than BIW* Weight 4 Using lightweighting in areas other than
those included in BIW*.
Thermo-electric waste heat recovery Other 1 This is the conversion of waste heat
energy to electricity using a thermo-
electric substance, where a change in
temperature across a semiconductor
material creates a voltage.
Secondary heat recovery cycle Other 2 Secondary heat (i.e. waste heat) can be
recovered, for example using the organic
Rankine cycle, where an organic
substance is used as working medium
instead of water (i.e. steam).
Auxiliary systems efficiency improvement Other 3 Includes improving air conditioning and
other energy using auxiliary systems.
Thermal management Other 4 Closed-loop control of the coolant
circuits for instantaneous adaptation to
current operating conditions.
Notes: * Body in white or BIW refers to the stage in automotive design or automobile manufacturing in which a car
body's sheet metal components have been welded together - but before moving parts (doors, hoods, and deck
lids as well as fenders) the motor, chassis sub-assemblies, or trim (glass, seats, upholstery, electronics, etc.)
have been added and before painting. (Wiki, 2012)
Regulatory interest in GHG reductions from motorcycles and mopeds has yet to significantly
develop on this mode, since it comprises a very small proportion of overall energy
consumption and emissions. Therefore, information is much less readily available in the
public domain on the technical possibilities to improve their energy efficiency. The following
Table 2.13 provides a summary of the options identified based on information available in the
public domain (e.g. in reports from the IEA (2009) and ICCT (2011)), or through drawing
parallels with the options available for passenger cars. As for car and van technologies, a
short description/definition of the technical option is provided in where it is not immediately
obvious from the name what it covers.

Table 2.13: Summary of the efficiency improvement technologies included in the motorcycle
analysis
Air assisted direct injection for 2-stroke engines PtrainE 1 Similar to technology for cars and vans
Electronic port fuel injection for 4-stroke engines PtrainE 2 Similar to technology for cars and vans
Swirl control valve PtrainE 3
While idling and during low engine speed
operation, the swirl control valve closes.
Thus the velocity of the air in the intake
passage increases, promoting the
vaporization of the fuel and producing a
swirl in the combustion chamber.
Because of this operation, this system
tends to increase the burning speed of
the gas mixture, improve fuel
consumption, and increase the stability
in running conditions.
Variable ignition timing PtrainE 4
Using a throttle sensor position and the
engine speed sensor, the load can be
estimated and the spark timing adjusted
for better fuel economy (ICCT, 2011).
Engine friction reduction PtrainE 5 Assume similar to cars and vans
Optimising transmission systems PtrainE 6 Similar to cars and vans
Start-stop hybridisation PtrainE 7 Similar to technology for cars and vans
Aerodynamics improvement Aero 1
More challenging than cars and vans
due to reduced possibilities to reduce
drag from the rider without the use of
encasing shells (unlikely to be popular).
Low rolling resistance tyres Rres 1
Friction levels per unit weight are twice
as high as those used on cars - but high
friction is important for safety (IEA,
2009). Improvements are therefore
limited in scope.
Light weighting Weight 1 Similar to cars and vans
Thermo-electric waste heat recovery Other 1 Assume similar to cars and vans
For heavy duty vehicles there have been a range of recent studies investigating the technical
options for improving efficiency and reducing GHG emissions. As for passenger cars there
are several specific studies that have been completed recently relevant to the UK/European
situation that provide a comprehensive list of the different technology options being
developed, which include AEA-Ricardo (2011), Ricardo (2009) and TIAX (2011). These
sources have therefore been used as a basis for the list of technological options presented in
Table 2.14. As for light duty vehicles and motorcycles, a short description/definition of the
technical option is provided in where it is less obvious from the name what it covers, and a
more detailed technical description of each technology is also available in AEA-Ricardo
(2011).

Table 2.14: Summary of the efficiency improvement technologies included in the heavy duty
vehicle analysis
General improvements PtrainE 1 General incremental improvements to vehicle and
powertrain (other than the headline technologies listed in
this table), and impacts of additional AQ pollutant control
(as defined by Ricardo in AEA-Ricardo (2011)*. It is
assumed that all the underlying technologies are utilised
fully by 2030 (and also no further Euro standards) -
hence no further efficiency improvement after then.
Mechanical Turbocompound PtrainE 2 Exhaust gas energy recovery with additional exhaust
turbine, which is linked to a gear drive and transfers the
energy on to the crankshaft providing extra torque.
Electrical Turbocompound PtrainE 3 Exhaust turbine in combination with an electric generator
/ motor to recover exhaust energy: (i) Recovered energy
can be stored or used by other electrical devices; (ii)
Motor during transients to accelerate.
Heat Recovery (Bottoming Cycles) PtrainE 4 Exhaust gas energy recovery with heat exchangers.
Sometimes called “bottoming cycles”, this concept uses
exhaust gas heat in an exchanger to drive an additional
power turbine to generate energy. Similar to the
secondary heat recovery cycle for light duty vehicles.
Controllable Air Compressor PtrainE 5 Air compressor with electric / air actuated clutch to de-
connect compressor in idle status or when compressor
not required.
Automated Transmission PtrainE 6 Replacement of manual transmissions with automated
transmission based on a manual (AMT) which has similar
mechanical efficiency to a manual transmission but
automated gear shifts to optimise engine speed.
Stop / Start System PtrainE 7 System uses a high-voltage e-motor mounted to the
crankshaft to operate stop / start, i.e. stopping the engine
running whenever the vehicle is stationary, along with
regenerative braking.
Pneumatic Booster – Air Hybrid PtrainE 8 Compressed air from vehicle braking system is injected
rapidly into the air path and allows a faster vehicle
acceleration, which allows an earlier gear shift (short
shifting), resulting in the engine operating more in an
efficient engine speed / load range.
Aerodynamic Fairings Aero 1 Additional add-ons to cabs that help reduce
aerodynamics drag and improve fuel consumption.
Includes cab deflectors and cab collars and can be
added as aftermarket additions.
Spray Reduction Mud Flaps Aero 2 The mud flap separates the water from the air through a
series of vertical passages created by vanes which
makes the spray change direction a number of times
eliminating the water.
Aerodynamic Trailers / Bodies Aero 3 Trailers / bodies designed to improve vehicle
aerodynamics, e.g.: teardrop shapes, or those integrating
multiple aerodynamic features into a complete package.
Aerodynamics (Irregular Body Type) Aero 4 A package of more limited set of aerodynamic options
can be applied to certain vehicle types that are limited by
their specific functionality/shape (e.g. tankers and
container carriers) or variable loads (e.g. flat bed).
Active Aero Aero 5 Active aerodynamics to reduce vehicle drag where air is
blown from trailer/body trailing edge and over trailer/body
roof to reduce drag caused by low pressure region
behind trailer/body.
Low Rolling Resistance Tyres Rres 1 Tyres designed to minimise rolling resistance whilst still
maintaining the required levels of grip.

Single Wide Tyres Rres 2 Replacement of dual tyres on an axle with a lower aspect
ratio single wide tyre.
Automatic Tyre Pressure Adjustment Rres 3 Automatic Tyre Monitoring/Pressure Adjustment systems
use the air compressor on the vehicle to automatically
monitor and adjust tyre pressures to optimum levels for
load and terrain conditions.
Light weighting Weight 1 Apply aluminium alloys intensively in tractor chassis and
body, trailer and powertrain. Use of aluminium alloy may
achieve total combined unit weight savings of up to
2,000kg –estimate for tractor body and chassis ~900 kg.
Predictive Cruise Control Other 1 Development of systems that use electronic horizon data
to improve the fuel efficiency of vehicles. Combining
GPS with Cruise Control to better understand the road
ahead for optimal speed control. Based on lower cost
and energy savings estimates from TIAX (2011).
Smart Alternator, Battery Sensor &
AGM Battery
Other 2 Control alternator voltage to that required for the current
battery condition and vehicle mode to maximise overall
electrical generation efficiency: Typically, an absorbent
glass mat (AGM) battery is used to decouple alternator
and vehicle electrical loads with State of Charge (SOC)
varying between 50-75% according to vehicle mode. In
overrun, a high alternator voltage and fast charging is
used to maximise brake energy regeneration. To reduce
engine load in acceleration, the alternator voltage is
reduced below that required for the current battery
condition such that discharge occurs.
Alternative Fuel Bodies
Other 3
Replacement of existing power sources for vehicle
bodies which use diesel for power. For body types with
high auxiliary requirements like RCVs, refrigerated
transport (and some construction vehicles), additional
efficiency gains can be achieve by powering these
systems via electric battery storage, rather than off the
ICE. This option is therefore only suitable to vehicles
with high-powered electrical systems, i.e. HEVs / BEVs.
Advanced Predictive Cruise Control
Other 4
Based on a more sophisticated system with higher costs
and energy savings from AEA-Ricardo (2011)
Source: Descriptive text/notes taken directly from AEA-Ricardo (2011) for the most part.
Notes: * Business as usual scenario of fuel consumption of new vehicles - assuming no incentives or
legislative CO2 for HDV:
(a) All: Natural p.a. improvement in powertrain efficiency includes transmission and engine auxiliaries;
(b) Long-haul: Significant additional impacts of using vehicle aids by 2030
(c) Coaches/Regional Distribution: Some additional aero improvements and weight reduction
(d) Buses: Forecast reduction in vehicle mass to increase fuel economy of vehicles
(e) All: 3% penalty from increasing emissions legislation in 2013 and then potential Euro VII at ~2018.

3 Efficiency Assumptions
This chapter provides a more detailed summary of the methodological approach,
calculations, data sources and assumptions used in the development of the study dataset for
technology efficiencies.
3.1 Calculation Methodology
The study methodology estimates two classes of efficiency improvements over time: first
non-specific general improvements to powertrain technologies (such as improvements to
electric engine or fuel cell efficiency) that impact on the overall efficiency of the basic
powertrain. The methodology used for these technologies are discussed in Section 3.1.1.
The second are the specific technologies identified that can improve the efficiency of a range
of aspects of the vehicles performance (including the powertrain). The methodologies for
these technologies are dealt with separately in Section 3.1.2. Finally, the key assumptions
and data sources used in the calculations are detailed in Section 3.2
3.1.1 Basic Components
This section provides a summary of the methodology for calculation of the change in the
efficiency of powertrains from 2010-2050, providing the specific formulas used for calculating
this in the study analysis.
The basic 2010 energy consumption (in MJ/km) of individual vehicle powertrains is
calculated using the powertrain efficiency improvement relative to the reference powertrain,
which is defined as:
Petrol ICE: for all petrol-based powertrains, NG ICE powertrains, and all motorcycle
technologies;
Diesel ICE: for all diesel-based powertrains, DNG ICE powertrains and all other
powertrain technologies, except for motorcycle powertrains and NG ICE (as above);
The assumptions used for these 2010 basic powertrain efficiencies is provided in Section 0.
The basic improvement to the powertrain efficiency from 2010 – 2050 is calculated based on
the net change in the efficiency of its component parts. For a given year, an approximation
for the overall powertrain efficiency is calculated as follows, by powertrain type:
Powertrain Summary calculation for powertrain efficiency (for a given year)
ICE / FHV /
HHV / HEV
Total Efficiency (%) = ICE Eff.(%) x Fuel tank Eff.(%)
x Hybrid system improvement % (where appropriate)
BEV Total Efficiency (%) = Elec motor Eff.(%) x Elec drivetrain Eff.(%) x Battery Eff.(%)
H2FC Total Efficiency (%) = Fuel cell Eff.(%) x Elec motor Eff.(%) x Elec drivetrain Eff.(%)
x Battery Eff.(%) x H2 storage Eff.(%)
Petrol/Diesel/H2
PHEV / REEV
Total Efficiency (%) = ( Elec drive Eff.(%) x %km in Elec drive )
x ( NonElec drive Eff.(%) x (1 - %km in Elec drive ) )
Where:
Elec drive Eff.(%) = as for BEV Eff.(%) ;
NonElec drive Eff.(%) = as for HEV Eff.(%) OR H2FC Eff.(%)

The overall efficiency improvement of the basic powertrain is then calculated from:
Total Efficiency Improvement (%) [2010-20XX]
= Powertrain Eff.(%) [20XX] / Powertrain Eff.(%) [2010]
3.1.2 Efficiency Improvement Technologies
The methodology employed for combining individual effects of technologies via individual
technology performance and deployment levels 2010-2050 is summarised in this section.
Individual efficiency improvement technology (EIT) efficiencies are calculated in a
multiplicative way based on the net increased percentage penetration/deployment in the new
vehicle fleet versus 2010 levels from 2010 - 2050 (see Section 5.2 on the assumptions for
this aspect). Total efficiency improvement for EITs are therefore calculated as follows:
Total EIT Eff.Saving (%) = 1 - ( ( 1 - EIT[1] Eff.Saving(%) x % Deployment EIT[1] )
x ( 1 - EIT[2] Eff.Saving(%) x % Deployment EIT[2] )
x ( 1 - EIT[3] Eff.Saving(%) x % Deployment EIT[3] )
x etc )
Where:
EIT[1] Eff.Saving(%) = Total efficiency saving of efficiency improvement technology 1
% Deployment EIT[1] = Net increased percentage deployment of EIT 1 versus 2010
For PHEV and REEV powertrain types, the efficiency savings for the energy consumption of
the vehicle running on either battery electric mode or other mode (i.e. using petrol ICE /
diesel ICE or H2FC) are tracked separately. This is because some technologies (e.g. those
improving the efficiency of ICEs) will not improve the efficiency of the battery electric mode.
The overall vehicle efficiency is calculated at the end using the relative % distances travelled
in electric/other drive modes.
3.2 Key Sources and Assumptions
3.2.1 Basic components
This section provides a summary of sources and assumptions for the performance of basic
component elements which are generally consistent across modes and powertrains.
However, there are also mode specific powertrain efficiency factors also detailed in this
section.
3.2.1.1 Mode independent basic components
The study analysis assumptions for the basic component efficiencies are presented in Table
3.1. These technology efficiencies are either indicative estimates or have been sourced from
a range of other sources (summarised in the table). As described in the previous section
3.2.1, these are used primarily to estimate future improvements at the basic powertrain level.

Table 3.1: Summary of basic component efficiency assumptions used in the study analysis
Area Category Unit 2010 2020 2030 2040 2050 Source/Notes
Battery (all types) Best % 86% 87% 88% 89% 90% AEA (2008)
Low % 86% 86% 86% 86% 86% AEA indicative estimate
High % 86% 87.5% 89% 90.5% 92% AEA indicative estimate
H2 storage All % 99% 99% 99% 99% 99% Science (2003)
CNG storage All % 99% 99% 99% 99% 99% Science (2003)
Petrol ICE powertrain All % 22% 22% 22% 22% 22% Indicative estimate
NG ICE powertrain All % 22% 22% 22% 22% 22% As for petrol
NG ICE (HDV) All % 22% 22% 22% 22% 22% As for NG ICE
Diesel ICE powertrain All % 25% 25% 25% 25% 25% Indicative estimate
Diesel ICE (HDV) All % 25% 25% 25% 25% 25% As for Diesel ICE
DNG ICE All % 25% 25% 25% 25% 25% As for heavy duty diesel
Electric motor Best % 92.0% 92.8% 93.5% 94.3% 95.0% JEC (2011), AEA (2008)
Low % 92.0% 92.5% 93.0% 93.5% 94.0% AEA indicative estimate
High % 92.0% 93.0% 94.0% 95.0% 96.0% AEA indicative estimate
Fuel cell Best % 54.0% 56.5% 59.0% 61.5% 64.0% AEA (2008)
Low % 54.0% 55.5% 57.0% 58.5% 60.0% AEA indicative estimate
High % 54.0% 57.5% 61.0% 64.0% 64.0% AEA indicative estimate
Electric powertrain LDV % 95% 95% 95% 95% 95% Indicative estimate based
on IEA (2012,
forthcoming).
HDV % 95% 95% 95% 95% 95%
Motorcycle % 95% 95% 95% 95% 95%
Battery charger LDV % 87.2% 90.0% 92.7% 95.3% 97.8% Calculated from efficiency
HDV % 87.2% 90.0% 92.7% 95.3% 97.8% of Battery and Battery +
Motorcycle % 87.2% 90.0% 92.7% 95.3% 97.8% Charger
Battery + Charger LDV % 75.0% 78.8% 82.5% 86.3% 90.0% Cenex (2012), AEA (2008)
HDV % 75.0% 78.8% 82.5% 86.3% 90.0% As for LDVs
Motorcycle % 75.0% 78.8% 82.5% 86.3% 90.0% As for LDVs
3.2.1.2 Mode specific basic components
The mode specific basic component efficiencies include the overall efficiency assumptions
for individual powertrain types, which are presented Table 3.3 for light duty vehicles and
motorcycles, and Table 3.5 for heavy duty vehicles.
The figures for LDVs are based primarily on TNO (2011) and AEA-TNO (2009), with the
exception of H2 fuel cell vehicles, which were estimated based on a range of values
available in the literature (Honda, 2012; EERE, 2010). The savings listed in the table for
LDVs indicate efficiencies relative to the base technology – which is petrol ICE for petrol-
based technologies, and diesel ICE for everything else. Efficiencies of diesel ICE versus
petrol ICE are around 25%, calculated from the relative energy consumption (in MJ/km) of
petrol and diesel C+D class vehicles (see Section 2.3 on vehicle characteristics). The
efficiency improvements listed for PHEV and REEVs are combined average efficiencies
based on relative % distance travelled in ICE/fuel cell mode and battery electric mode. In the
model analysis the efficiencies of running on full battery electric mode and the alternative
mode are tracked separately, with efficiency benefits assumed to be in line with BEVs and
HEVs (or H2FCs as appropriate) respectively.

Table 3.2: Summary of the light duty vehicle and motorcycle powertrain efficiency
assumptions used in the study analysis
Component Type T# 2010 Efficiency improvements over the base technology*, %
Cars Vans Motorcycles
Petrol ICE Ptrain 1 0.0% 0.0% 0.0%
Diesel ICE Ptrain 2 0.0% 0.0% N/A
Petrol HEV Ptrain 3 25.0% 25.0% 25.0%
Diesel HEV Ptrain 4 22.0% 18.0% N/A
Petrol PHEV** Ptrain 5 46.9% 46.9% N/A
Diesel PHEV** Ptrain 6 45.2% 45.2% N/A
Petrol REEV** Ptrain 7 56.6% 56.6% N/A
Diesel REEV** Ptrain 8 55.5% 55.5% N/A
BEV Ptrain 9 76.0% 76.0% 76.0%
H2FC Ptrain 10 53.7% 53.7% 53.7%
H2FC-PHEV** Ptrain 11 63.3% 63.3% N/A
H2FC-REEV** Ptrain 12 67.5% 67.5% N/A
NG ICE Ptrain 13 -24.6% -15.0% N/A
Sources: Calculated from on data provided in TNO (2011) and AEA-TNO (2009), except for H2FC, which is
based on a range of estimates available in the literature on the current efficiency of fuel cell vehicles.
Notes: * Base technology referenced against is petrol for petrol fuelled vehicles and diesel for all other
powertrain types.
** Efficiency improvements listed for PHEV and REEVs are combined average efficiencies based on
relative % distance travelled in ICE mode (= HEV efficiency assumed for petrol/diesel, and H2FC
efficiency assumed for H2FC PHEV/REEV) and battery electric mode (= BEV efficiency assumed).
The figures for HDVs in Table 3.5 are based primarily on AEA-Ricardo (2011), with the
exception of H2 fuel cell vehicles, which were assumed to be similar to the LDV estimates.
Table 3.3: Summary of the heavy duty vehicle powertrain efficiency assumptions used in the
study analysis
Component Type T# 2010 Efficiency improvements over the base technology, %
Small Rigid
Truck
Large rigid
Truck
Articulated
Truck
Construction
Truck Bus Coach
Diesel ICE Ptrain 1 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
Diesel FHV Ptrain 2 15.0% 7.5% 5.0% 6.3% 20.0% 7.5%
Diesel HHV Ptrain 3 10.0% 5.0% 3.3% 4.3% 15.0% 5.6%
Diesel HEV Ptrain 4 20.0% 10.0% 7.0% 8.5% 30.0% 10.0%
BEV Ptrain 5 70.0% N/A N/A N/A 70.0% N/A
H2FC Ptrain 6 53.7% 53.7% 53.7% 53.7% 53.7% 53.7%
DNG ICE
(2)
Ptrain 7 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
NG ICE
(2)
Ptrain 8 -15.0% -15.0% -15.0% -15.0% -15.0% -15.0%
Sources: Based on efficiencies provided in AEA-Ricardo (2011), except for H2FC, which is based on a range of
estimates available in the literature on the current efficiency of fuel cell vehicles.
This section provides a summary of the sources and assumptions for the performance of
mode specific efficiency improvement technologies.
The study analysis assumptions for the efficiency improvement technology costs for cars and
vans are presented in Table 3.4. These technology costs have been sourced primarily from
TNO (2011) and AEA-TNO (2009). There are a range of sources available in the literature
that have been reviewed and considered (see Section 8 of this report), providing estimates
on technology cost and performance. However, these two sources were selected for use in
this study as the most up-to-date, relevant (i.e. specific to the European market and

conditions), comprehensive (covering all the significant technologies/types being
considered/developed) and consistent.
Ideally it would also be desirable to account for greater benefits of measures resulting in
improved rolling resistance (including weight reduction) for electric powertrains/vehicles3
: For
conventional ICE powertrains, most of the energy losses over the NEDC are due to the ICE
- around 70% of total losses, with rolling resistance around 5-6% of total losses. However,
for electric vehicles the drivetrain is highly efficient, so that rolling resistance losses are a
much higher proportion of the total (maybe over 50% of all losses). Hence, since weight
reduction essentially acts to reduce rolling resistance losses it has a proportionally higher
impact in reducing electric powertrain energy consumption. However, it was not possible to
take this to be taken it into account within the study’s analysis – partly due to a lack of
suitable data for quantification, and partly due to the significant additional complexity that this
would have entailed in the study calculation framework.
Table 3.4: Summary of the technology efficiency assumptions for the car and van analysis
Efficiency Improvement Technology Category # Efficiency Improvement, %
Cars
(1)
Vans
(2)
Petrol - low friction design and materials PtrainE 1 2.0% 1.5%
Petrol - gas-wall heat transfer reduction PtrainE 2 3.0% 3.0%
Petrol - direct injection (homogeneous) PtrainE 3 5.3% 5.5%
Petrol - direct injection (stratified charge) PtrainE 4 9.3% 9.5%
Petrol - thermodynamic cycle improvements PtrainE 5 14.5% 15.0%
Petrol - cam-phasing PtrainE 6 4.0% 4.0%
Petrol - Variable valve actuation and lift PtrainE 7 10.5% 11.0%
Diesel - Variable valve actuation and lift PtrainE 8 1.0% 1.0%
Diesel - combustion improvements PtrainE 9 2.0% 1.5%
Mild downsizing (15% cylinder content reduction) PtrainE 10 5.5% 2.7%
Medium downsizing (30% cylinder content reduction) PtrainE 11 8.5% 9.3%
Strong downsizing (≥45% cylinder content reduction) PtrainE 12 17.5% 18.5%
Reduced driveline friction PtrainE 13 1.0% 1.0%
Optimising gearbox ratios / down-speeding PtrainE 14 4.0% 2.7%
Automated manual transmission PtrainE 15 5.0% 4.0%
Dual clutch transmission PtrainE 16 6.0% 5.0%
Start-stop hybridisation PtrainE 17 5.0% 4.0%
Regenerative breaking (smart alternator) PtrainE 18 7.0% 6.0%
Aerodynamics improvement Aero 1 1.8% 1.1%
Low rolling resistance tyres Rres 1 3.0% 3.0%
Mild weight reduction (~10% reduction in BIW*) Weight 1 2.0% 1.5%
Medium weight reduction(~25% reduction in BIW*) Weight 2 6.0% 6.1%
Strong weight reduction (~40% reduction in BIW*) Weight 3 12.0% 11.0%
Lightweight components other than BIW Weight 4 2.0% 1.5%
Thermo-electric waste heat recovery Other 1 2.0% 2.0%
Secondary heat recovery cycle Other 2 2.0% 2.0%
Auxiliary systems efficiency improvement Other 3 12.0% 11.0%
Thermal management Other 4 2.5% 2.5%
Notes:
(1) Data based on average of C and D class cars from TNO (2011)
(2) Data from TNO (2011) for large cars was used to update the older/previous assumptions for vans by scaling
data from AEA-TNO (2009) – which contained comparable data for both car and van technologies..
3
Personal communication with Angela Johnson of Ricardo, following the workshop held on 2 February 2012 at CCC’s offices.

The study analysis assumptions for the efficiency improvement technology performance for
motorcycles and mopeds are presented in Table 3.5. These have been sourced from a
limited range of identified literature including principally publications by the IEA (2009) and
ICCT (2011), together with indicative AEA estimates for technologies based on those for
petrol cars where no suitable data in the literature could be identified.
Table 3.5: Summary of the technology efficiency assumptions for the motorcycle analysis
Efficiency Improvement Technology Category # Efficiency Improvement, %
Air assisted direct injection for 2-stroke engines
(1)
PtrainE 1 30.0%
Electronic port fuel injection for 4-stroke engines
(1)
PtrainE 2 20.0%
Swirl control valve
(1)
PtrainE 3 7.0%
Variable ignition timing
(2)
PtrainE 4 8.5%
Engine friction reduction
(2)
PtrainE 5 4.0%
Optimising transmission systems
(2)
PtrainE 6 0.5%
Start-stop hybridisation
(2)
PtrainE 7 5.0%
Aerodynamics improvement
(2)
Aero 1 0.9%
Low rolling resistance tyres
(2)
Rres 1 1.5%
Light weighting
(2)
Weight 1 2.0%
Thermo-electric waste heat recovery
(3)
Other 1 2.0%
Source: (1) ICCT (2011); (2) Estimates by AEA based on data for cars for measures without efficiency values
identified by IEA (2009), ICCT (2011) and other sources. (3) AEA estimate based on car option.
The study analysis assumptions for the efficiency improvement technology costs for heavy
duty vehicles are presented in Table 3.6. These have generally been sourced from recent
work carried out by AEA and Ricardo for the European Commission (AEA-Ricardo, 2011),
which further updated analysis Ricardo had previously carried out for DfT (Ricardo, 2009).
For the purposes of this studies analysis, the categories provided in AEA 2011 have been
mapped onto the vehicle categories used in this study as follows:
AEA-Ricardo (2011) category Study category
Urban Delivery = Small rigid truck
Regional Delivery = Large rigid truck
Long haul = Articulated
Construction = Construction
TIAX recently carried out a review of the AEA-Ricardo (2011) study assumptions and
modelling results on behalf of ICCT, which was completed in December 2011 (TIAX, 2011).
This study utilised the efficiency and cost assumptions TIAX developed for the recent US
review of heavy duty vehicles (NAS, 2010) that fed into the recent development of
regulations aimed at significantly improving HDV efficiency in the US. The TIAX (2011) study
findings were generally in close consistency with the AEA-Ricardo (2011) work – within the
range of the analysis uncertainty4
. However, there were significant variations between the
costs and performance of predictive cruise control technology between the two studies.
For predictive cruise control, it appears that there may be differences in the sophistications of
the systems evaluated in AEA-Ricardo (2011) and TIAX (2011), since there were also
significant deviations in capital costs (the more efficient system being significantly higher
costs)5
. Therefore, two levels of predictive cruise control have been used in this study’s
analysis. In addition, there were no figures available in AEA-Ricardo (2011) for mechanical
turbocompounding, therefore estimates were made based on the difference between
mechanical and electrical turbocompounding from ICCT (2009).
4
Confirmed in personal communications between AEA and the lead author of the TIAX (2011) study in December 2011.
5
Based on personal communications between AEA and the TIAX (2011) lead author. As part of their work for NAS (2010) study, which fed into
the work for ICCT, they gathered data from a wide range of US manufacturers, upon which their study costs and efficiencies are largely based.

Table 3.6: Summary of the technology efficiency assumptions for the HDV analysis
Technology Cat. # Efficiency Improvement, %
Small
Rigid
Large
Rigid
Articulated
Truck
Construction
Truck Bus Coach
General improvements (2010-2020)* PtrainE 1 -3.0% 0.2% 2.3% -3.0% 0.9% 0.2%
General improvements (2020-2030)* PtrainE 1 3.0% 6.2% 8.3% 3.0% 5.1% 6.2%
Mechanical Turbocompound PtrainE 2 0.7% 0.7% 2.0% 1.8% 0.7% 1.7%
Electrical Turbocompound PtrainE 3 1.0% 2.5% 3.0% 2.8% 1.0% 2.5%
Heat Recovery (Bottoming Cycles) PtrainE 4 1.5% 2.5% 5.0% 3.8% 1.5% 2.5%
Controllable Air Compressor PtrainE 5 0.0% 1.0% 1.5% 1.3% 0.0% 1.0%
Automated Transmission PtrainE 6 5.0% 1.5% 1.5% 1.5% 5.0% 1.5%
Stop / Start System PtrainE 7 6.0% 3.0% 1.0% 2.0% 4.0% 3.0%
Pneumatic Booster – Air Hybrid PtrainE 8 1.5% 1.5% 3.5% 2.5% 0.0% 1.5%
Aerodynamic Fairings Aero 1 0.0% 1.0% 0.4% 0.7% 0.0% 1.0%
Spray Reduction Mud Flaps Aero 2 1.0% 2.0% 3.5% 2.8% 1.0% 2.0%
Aerodynamic Trailers / Bodies Aero 3 1.0% 11.0% 11.0% 5.8% N/A 4.1%
Aerodynamics (Irregular Body Type) Aero 4 1.0% 6.5% 5.0% 2.8% N/A N/A
Active Aero Aero 5 1.0% 5.0% 8.0% 0.0% 1.0% 5.0%
Low Rolling Resistance Tyres Rres 1 1.0% 3.0% 5.0% 4.0% 1.0% 3.0%
Single Wide Tyres Rres 2 4.0% 6.0% 5.0% 5.5% 4.0% 6.0%
Automatic Tyre Pressure Adjustment Rres 3 1.0% 2.0% 3.0% 2.5% 1.0% 2.0%
Light weighting Weight 1 4.0% 2.2% 2.2% 0.3% 6.0% 2.2%
Predictive Cruise Control Other 1 N/A 1.5% 1.5% 1.5% N/A 1.5%
AGM Battery
Other 2 1.5% 1.5% 1.5% 1.5% 1.5% 1.5%
Alternative Fuel Bodies * Other 3 15.0% 15.0% 15.0% 7.5% N/A N/A
Advanced Predictive Cruise Control Other 4 N/A 5.0% 5.0% 5.0% N/A 5.0%
Source: Based on datasets from AEA-Ricardo (2011), ICCT (2009) and TIAX (2011).
Notes: * These are general incremental improvements to vehicle and powertrain (other than the headline
technologies listed in this table), and impacts of more AQ pollutant control (as defined in AEA-Ricardo,
2011). The % improvements quoted are net per decade (unlike other technology options). However it is
assumed that all the underlying technologies are utilised fully by 2030 (and no further Euro standards
after 2020) - hence there is no further change after then. For some vehicle types the impacts of
additional AQ pollutant control on fuel consumption outweigh the efficiency improvements.
** AEA estimate for saving for alternative fuel bodies for construction trucks (mostly tippers) 50% of
other truck types (including RCVs and refrigerated vehicles, where auxiliary loads are estimated to be
significantly higher on average).
3.2.3 Real-World Efficiencies
It is currently widely acknowledged that there is a significant (and potentially widening)
differential between the performance of road vehicles on regulatory or other test-cycles and
in real-world conditions of use. This differential is due to a range of factors including the use
of accessories (air con, lights, heaters etc), vehicle payload (most significant for vans and
heavy duty trucks), poor maintenance (tyre under inflation, maladjusted tracking, etc),
gradients (tests effectively assume a level road), weather, more aggressive/harsher driving
style, differences in operational usage cycles, etc. (DCF, 2011a). Real-world uplifts in the
order of 15-18% for existing vehicle fleets are currently factored into most major modelling
exercises (DCF, 2011a). However, there is some evidence that the size of the discrepancy
between test-cycles and real-world performance is increasing for LDVs (LowCVP, 2011).
A review of the evidence available has not provided very significant amount of new
information in this area. TNO (2011) uses an uplift of 19.5% on new car efficiencies, which is,
if anything on the low side when compared to a recent review by Ricardo for LowCVP (2011).
A summary of the results of LowCVP (2011) are summarised in Table 3.7. In this study,
Ricardo compared the NEDC reported gCO2/km performance for 2-3 of the most popular
/representative vehicle models in each market segment with those from Autocar magazine
tests. However, such magazine tests may not be truly representative of the average driving

behaviour on UK roads. Therefore the TNO (2011) factor has been selected for the
purposes of this study’s analysis to represent most appropriate real-world uplift to apply to
2010 new passenger cars (and vans) for conventional ICE technologies.
Table 3.7: Summary of the differences found between gCO2/km values from NEDC and
Autocar Magazine tests
Market Segment Av. Difference NEDC to Autocar Test 2010 Registrations*
% Number, # Proportion, %
A: Mini 27.3% 53,269 2.7%
B: Small 20.7% 739,615 36.8%
C: Lower Medium 24.5% 532,881 26.5%
D: Upper Medium 17.5% 253,584 12.6%
E: Executive 22.5% 98,872 4.9%
F: Luxury 23.7% 7,655 0.4%
G: Sports 18.0% 44,679 2.2%
SUV 21.3% 153,220 7.6%
MPV 32.7% 124,206 6.2%
Weighted Average 22.3% 2,007,981 100%
Sources: Calculations based on data from LowCVP (2011). Vehicle registrations data from SMMT (2011).
Notes: Comparisons were made by Ricardo between NEDC and Autocar test results for 2-3 of the most
popular /representative vehicle models in each category.
In general it does appear from limited information available (e.g. LowCVP, 2011) that the
real-world performance is further away from the NEDC figures for the most efficient vehicles.
For non-conventional powertrain technologies, there is little evidence available on real-world
differences. Other than the LowCVP (2011) study (which also considered a limited range of
hybrid and electric vehicles) and recent work by Cenex (2009), there is mostly anecdotal
information that uplifts are generally greater than for conventional technologies. However,
analysis by Cenex of data from the current UK trials of electric vehicles (funded by TSB) has
indicated that the average differential between manufacturer claims on typical range, and
those achieve during the trials was almost 25%. We have used this figure, together with what
other little information is available to estimate modified uplifts for the purposes of this study
for passenger cars, which are presented in Table 3.8.
A further consideration is that there are losses associated with charging the batteries of
hybrid and electric vehicles. For regular HEVs, these losses are fully accounted for within the
vehicle’s operation. However, for PHEVs, REEVs and BEVs these losses are not accounted
for by simple comparison of the stored energy capacity of the battery and the observed
vehicle range, although they directly affect the actual amount of energy consumed.
According to Cenex, the best combined charging and battery efficiencies they have observed
on modern EVs is in the low 80%s, more typically around 75% and occasionally even lower.6
These losses are not accounted for in the real-world uplifts, since they are not on the vehicle-
side. However they should be accounted for separately in any overall analysis carried out
involving such vehicles – potentially through incorporation into the efficiencies of delivering
electricity to plug-in vehicles (i.e. in addition to electricity grid transmission and distribution
losses), such as within/on top of electricity GHG emission factors utilised.
For other road transport modes, there is also very little or no information available on
differences between test-cycle based data and the real world. This is a particular problem for
HDVs, where regulatory tests are on the engine only. Full-body test-cycle methodologies are
currently still under development at a European level (partly driven by the desire to more
effectively assess and potentially regulate HDV energy consumption/GHG emissions) with
likely introduction towards the end of the decade (AEA-Ricardo, 2011). The situation is also
complicated by the huge diversity of HDVs in both their configurations and duty cycles.
6
Personal communication between AEA and Steve Carol (Cenex), i.e. these figures include all the ‘lost’ energy that comes out of the wall socket
but doesn’t end up coming out of the battery to power the vehicle.

However, test-cycle assessments have been carried out on road transport vehicles recently
by TRL in order to develop/update the speed-emission curves (see DfT, 2009) that are used
in the compilation of the UK’s National Atmospheric Emissions Inventory (NAEI). These
include fuel consumption and CO2 emission functions used in the GHG inventory which is
part of the NAEI. Similar speed-emission/fuel consumption functions are also used in road
transport emissions modelling outside of the UK (e.g. in the COPERT model, widely used in
Europe to help countries compile their road transport emissions inventories).
Therefore, in order to develop suitable ‘real-world’ uplifts for this study’s analysis, we have
compared such speed-fuel consumption curve developed estimates (from AEA-Ricardo,
2011) for the UK situation with available data (mainly from DfT statistics) on actual real-world
fuel consumption of vehicles in different categories. The results of this analysis are also
presented in Table 3.8, with a summary of the main sources and assumptions for the
different real-world uplift factors provided in the accompanying Table 3.9.
Note: The test-cycle based vehicle efficiencies for trucks are based on average truck activity
on urban/rural/motorway roads. In reality there are very significant operational/mission
characteristics for different types of truck which have a very marked impact on their fuel
consumption (e.g. predominantly urban driving for smaller trucks). The actual real-world fuel
consumption data used to generate the ‘real-world’ uplifts intrinsically includes these
operational differences for different sizes of vehicles, which in some cases results in very
significant or very small real-world uplifts. The real-world uplift figures for HDVs are therefore
indicative for the purposes of the modelling and not equivalent to those for cars and vans.
Table 3.8: Summary of the basic real-world efficiency uplift assumptions used in the study
analysis for different vehicle powertrains
Type 2010 Basic Real-world efficiency uplifts, %
Car Van Motor-
cycle
Small Rigid
Truck
Large rigid
Truck
Articulated
Truck
Construction
Truck Bus Coach
ICE 19.5% 19.5% 30.2% 41.3% 9.0% 0.0% 9.0% 8.8% 9.0%
FHV 42.5% 10.1% 1.1% 10.1% 9.9% 10.1%
HHV 42.5% 10.1% 1.1% 10.1% 9.9% 10.1%
HEV 21.7% 21.7% 30.2% 42.5% 10.1% 1.1% 10.1% 9.9% 10.1%
PHEV** 22.6% 22.6%
REEV** 23.5% 23.5%
BEV 24.6% 24.6% 24.6% 43.9% 11.4%
H2FC 24.6% 24.6% 24.6% 43.9% 11.5% 2.6% 11.5% 11.4% 11.5%
Sources: Summarised in Table 3.9.
Table 3.9: Summary of the sources/methods used to estimate the basic real-world efficiency
uplift assumptions used in the study analysis for different vehicle powertrains
Type Sources and assumptions for 2010 Basic Real-world efficiency uplifts, %
Car Van Motor-
cycle
Small Rigid
Truck
Large rigid
Truck
Articulated
Truck
Construction
Truck Bus Coach
ICE (1) (1) (2) (3) (3) (3) (3) (4) (5)
FHV (6) (6) (6) (6) (6) (6)
HHV (6) (6) (6) (6) (6) (6)
HEV (7) (7) (7) (8) (8) (8) (8) (8) (8)
PHEV** (9) (9) (9)
REEV** (9) (9) (9)
BEV (7) (7) (7) (8) (8)
H2FC (10) (10) (10) (10) (11) (11) (11) (10) (11)
Sources:
(1) TNO (2011), for new cars
(2) AEA estimate based on NAEI data vs DCF (2011) (reported fuel consumption data)
(3) Estimated by comparing test-cycle based efficiencies relative to DfT fuel consumption stats (2011)
(4) Estimated by comparing test-cycle based efficiencies relative to BSOG fuel consumption calculations
from DfT (2011)
(5) Assumed to be the same as for large rigid trucks (similar duty cycle)

(6) Assumed to be similar to HEVs
(7) AEA estimate based on limited survey of current passenger car models from LowCVP (2011), and
information on average EVs from Cenex (2012) based on the UK’s current EV trials.
(8) AEA estimate based on 50% of the differential between car ICE and HEV increase from LowCVP (2011),
since small rigid trucks and buses are used in urban cycles where hybrid/BEV is optimal and other
vehicle types have already reduced efficiency assumptions generally based on real-world performance
assumptions for hybrids from AEA-Ricardo (2011).
(9) Indicative estimate based on the corresponding figures for HEVs and BEVs weighted by the % km
distance travelled in ICE mode and full electric mode respectively.
(10) Assumed to be similar to the uplift for BEVs.
(11) Assumed to be similar to the uplift for hybrid vehicles.
In addition to the development/understanding of test-cycle and real-world differences for
existing vehicles and technologies, CCC have indicated they wish to get a better
understanding on whether these differentials are likely to get larger as vehicles of a given
powertrain type get more efficient.
As already indicated, and illustrated in Table 3.8, the most efficient technologies appear to
have the largest differentials. Recent work by Cenex (2009) assessing the performance of
current electric vehicles has provided some insights as to some of the reasons for the
discrepancy. The findings from this work suggests that a significant proportion of the
additional energy consumption is due to the hotel power requirements (energy consumption)
of auxiliaries – particularly air conditioning/heating – which are not included in current test-
procedures for light-duty vehicles. The hotel power requirements for EVs, HEVs and pure
ICEs will remain essentially similar i.e. independent of drive-train technology. They will
therefore account for larger proportion of the total energy consumption of more efficient
vehicles. Improvements in the efficiency of such auxiliaries might therefore be expected to
reduce this differential in the future. Such issues are likely to be less important for HDVs.
In addition, there are certain flexibilities in the existing NEDC test procedures for LDVs that if
taken advantage of by manufacturers, could potentially lead to apparently greater levels of
improvements than are found in real-world conditions. This topic is currently the subject of
investigation in work recently started for the EC that AEA is contributing to.
In order to provide an estimate for the increasing gap between test-cycle and real-world
performance as additional efficiency improvement technologies are utilised, we have
developed the percentage reduction factors presented in Table 3.10, based on data from
TNO (2011). These figures represent the percentage by which the total improvement in
energy efficiency calculated from the combination of individual technologies should be
reduced to account for both overlap in their impacts (where effects are on the same source of
energy loss) and real-world performance. They are applied as in the following example:
Real world improvement through application of technologies A–C =
Total estimated test-cycle improvement from A-C (%) x (1 – RWF)
Where:
RWF = Real-world reduction factor from Table 3.10.
Table 3.10: Additional real-world correction factors used in the study analysis
Area RW safety margin, % reduction in overall vehicle efficiency improvement
2010 2020 2030 2040 2050
Petrol technologies* 3.0% 6.0% 9.0% 12.0% 15.0%
Diesel technologies** 1.0% 2.0% 3.0% 4.0% 5.0%
Sources: Based on the estimate of the safety margin for the end of the cost-curves from TNO (2011).
Notes: These figures represent the percentage by which the total improvement in energy efficiency calculated
from the combination of individual technologies should be reduced to account for both overlap in their
impacts and real-world performance.
* The petrol margin also used for ‘other’ LDV technologies (i.e. BEV, H2FC, and the electric operation
of PHEV/REEV) since it is higher and therefore is assumed to better reflect the greater discrepancy to
real-world performance for the most efficient light duty vehicles.
** The diesel margin is used for all HDV technologies, reflecting anticipated lower significance for them.

4 Capital Cost Assumptions
This chapter provides a more detailed summary of the methodological approach,
calculations, data sources and assumptions used in the development of the study dataset for
technology capital costs.
4.1 Calculation Methodology
4.1.1 Methodologies for projecting capital costs
The future costs associated with technological options for reducing CO2 will evolve as a
direct consequence of the scale of application (willingness to pay, economies of scale and
learning effects) and time (innovation). Decreases in the costs of technology arise due to a
combination of direct and indirect factors including:
Direct factors
Improvements in production processes (i.e. over time, and with increases in the scale
of manufacturing, production processes can become more efficient, thereby reducing
costs);
Technological or manufacturing innovations may lead to reductions in unit costs (e.g.
new materials or production processes);
A move to mass production from low-volume production;
Indirect factors
Subsidies and financial incentives to promote technology (e.g. electric car discount or
0% VED on green cars); and
Greater demand or change in consumer behaviour (mainly influenced by
environmental policies).
To capture the impact of the above factors a number of different methods, theories,
estimates and assumptions have been used in the literature (Table 4.1).
When considering a purchase, capital cost is always the primary factor for the mass market.
However, the potential for lower running costs is largely ignored by the mass market i.e. life
cycle costing (or Net Present Value) is rarely considered at the point of purchase. The
challenge for OEMs is to achieve manufacturing efficiencies with alternative powertrain by
bringing down the capital cost, for example the cost of batteries in electric powertrains.
Decreases in the marginal costs of technology due to the factors above can be quantitatively
described using learning rate theory. Learning rate theory enables changes in the costs
associated with a particular technology to be quantified in relation to levels of deployment in
the market. The application of learning rate theory is not straightforward in practice, because
there are a number of unknown factors that cannot be foreseen with absolute accuracy. In
particular, it is difficult to predict future innovations and, with respect to vehicle technologies,
the rate at which costs will decrease is highly dependent on the levels of activity and
investment in research and development made by vehicle manufacturers.

Table 4.1: Summary of alternative potential options for forward projecting capital costs
Methods, estimates,
theories &
Assumptions
Description Impact on capital cost
Penetration rates A measure of the amount
of sales or adoption of a
product or service
compared to the total
market for that product or
service.
High (or faster) penetration rates can bring down
capital cost, as higher sales volumes drive cost
reduction due to economies of scale and
development of the product to reduce cost. Can be
used in conjunction with learning rates
Economies of scale Cost advantages due to
expansion of production
or market
Greater economies of scale can reduce capital
costs
Cost of sub-
components
Available information on
capital costs of sub-
components
Possible to estimate total product cost from cost of
sub-components and estimates of other cost
components, e.g. margin and assembly costs
Cost trajectories based
on learning rates
Decreases in the
marginal costs of
technology due to
innovation, economies of
scale and improvement in
production process over
time is defined as
learning rate
Learning rate methods assume that production
costs fall in a logarithmic trajectory with total
cumulative production volumes (i.e. when
cumulative production volumes double, costs fall by
a given proportion). This has been observed in
practice with many technologies as they reach
maturity, but is less representative of very early
stages where innovation is more significant in cost
reduction than accumulated learning.
Moreover, the drivers of cost reduction mentioned
above go beyond pure economies of scale and
some take time in order to be realised. In other
words there is an implicit time dimension in
learning rate theory that needs to be taken into
account when applying learning rate equations, as
very rapid growth in production volumes (which in
itself may be unlikely due to logistic and market
reasons) may not be accompanied by a quick
reduction in cost.
In developing a dataset on technology capital cost projections for this study, a pragmatic
approach has been used on the basis of a combination of (i) limitations of data/information in
the literature, (ii) the short study period, and (iii) study resources in relation to the wide range
of technologies and vehicle types/combinations covered. The approach utilises some of the
different methods, theories, estimates and assumptions described in Table 4.1 above. The
core of the methodology revolves around developing an estimate of the total production cost
using the costs of sub-components. In general, two broad approaches have been adopted
dependant on the type of technology to estimate the future costs of these sub-components.
These are defined as follows, with a summary of the rational used for the approach taken:
A. Basic Components: These are the mostly mode-independent costs for powertrain-
level technologies (i.e. for conventional ICEs, HEVs, PHEVs, BEVs, H2FC vehicles,
etc.). For example they include cost per kW power required for engines, electric
motors, fuel cells, and cost per kWh for batteries, natural gas or H2 storage:
The available information from the literature for these technologies generally
provides estimates based on current prices and future cost reductions (with built
in assumptions on various levels of deployment/innovation or meeting future
research targets). Figures typically have a wide range of values in the literature
and depend on a lot of assumptions / different sources, methodologies.

Since this study was not developing a dataset of powertrain penetration levels, it
is not possible to directly model potential cost reductions using learning rates –
also this has to an extent already been done by others in the literature. Therefore
we have used existing estimates for these costs (often with upper/lower values)
and their projections from 2010-2030/2050. These have been mainly sourced
from the Element Energy report for LowCVP (EE, 2011), which carried out a
comprehensive review of the literature and developed datasets to 2030. These
have been extrapolated out to 2050 using other available information sources.
Generally overall technology costs for a given vehicle class are calculated by
scaling to the vehicle characteristics – i.e. required peak power rating (for
ICE/motors, etc), MJ/km and range (for energy storage), though some are
essentially fixed values for a particular mode category (e.g. after treatment costs
for LDV, HDV, motorcycles). This is discussed further in the Section 4.1.2.
B. Efficiency Improvement Technologies: These are mode-specific technology costs
applied below/onto different powertrain options (i.e. as indicated above):
For these, information from the literature does not usually provide very much
detail on the basis for cost estimates developed, though a few of the key sources
referenced provide higher-level details. Generally capital cost estimates from the
literature have been put together assuming mass deployment (though with further
cost reduction possible in the future at a lower rate). Even so it is not always clear
whether data are on a consistent basis even within the same study.
Since assumptions/estimates are being made for this study on levels of
deployment of technologies within modes technology learning rates have been
used in combination with indicative figures for ‘mass-deployment’ to estimate
more modest future cost reductions trajectories. This is discussed further in the
Section 4.1.3.
Total vehicle costs are calculated according to the following formula:
Total Vehicle Cost (£) = Powertrain cost (£) + Energy storage cost (£) + Glider cost (£)
+ Total efficiency improvement technology costs (£)
The methodology for calculating the individual component costs is outlined in the following
sections.
Where the figures that have been sourced were in US$ or Euro currencies, the following
exchange rates have been applied:
Pound, £ Euro, € USD, $
Pound, £ 1.25 1.8
Euro, € 0.800 1.440
USD, $ 0.556 0.694
4.1.2 Basic Components
This section provides a summary of the methodology of calculation of change in costs of
basic powertrains from 2010-2050, providing the specific formulas used for calculating the
capital cost of the powertrain, energy storage and glider.

4.1.2.1 Powertrain costs
Powertrain costs for different vehicle types and powertrains are calculated mainly from
mode-independent costs for powertrain-level technologies as follows by powertrain type:
Powertrain Summary calculation for powertrain cost (for a given year)
Petrol ICE
/Diesel ICE
Total Cost (£) = Power (kW) x ICE cost (£/kW)* + ICE aftertreatment cost (£)**
DNG ICE As for ICE + Dual fuel system cost (£)***
NG ICE As for ICE + NG system premium***
FHV Total Cost (£) = Power (kW) x FHV ICE kW size (%) x ICE cost (£/kW)*
+ ICE aftertreatment cost (£)** + Flywheel hybrid system cost (£)***
HHV Similar format to FHV
HEV Total Cost (£) = Power (kW) x HEV ICE kW size (%) x ICE cost (£/kW)*
+ ( Power (kW) x (1 - HEV ICE kW size(%) ) x HEV Motor kW size (%)
x Electric motor cost (£/kW) )
+ ICE aftertreatment cost (£)** + Electric drivetrain cost (£)**
PHEV Similar format to HEV, but with PHEV ICE kW size (%)
REEV Similar format to HEV, but with REEV ICE kW size (%)
BEV Total Cost (£) = Power (kW) x BEV Motor kW size (%) x Electric motor cost (£/kW)
+ Electric drivetrain cost (£)**
H2FC Total Cost (£) = Power (kW) x H2FC Motor kW size (%) x Electric motor cost (£/kW)
+ Electric drivetrain cost (£)**
H2FC
PHEV /REEV
As for H2FC
Notes: * Separate costs for petrol/natural gas ICE and for diesel/dual-fuel ICE; also different costs for LDV and
for HDV diesel ICE and NG ICE engine costs
** Separate fixed costs for LDVs and for Motorcycles; HDV scaled to kerb weight. -50% aftertreatment
costs for petrol vehicles or natural gas fuelled vehicles
*** By HDV vehicle type
4.1.2.2 Energy storage costs
Energy storage costs for different vehicle types and powertrains are calculated mainly from
vehicle range and energy efficiency assumptions using mode-independent costs for energy
storage technologies, as follows by powertrain type:
Powertrain Summary calculation for energy storage cost (for a given year)
Petrol ICE
/Diesel ICE
Total Cost (£) = ICE Range (km) x Vehicle efficiency (MJ/km) x Fuel tank cost (£/MJ)
DNG ICE Total Cost (£) = ICE Range (km) x Vehicle efficiency (MJ/km) x Diesel tank cost (£/MJ)
+ ICE Range (km) x Vehicle efficiency (MJ/km) x NGas tank cost (£/MJ)*
NG ICE Total Cost (£) = ICE Range (km) x Vehicle efficiency (MJ/km) x NGas tank cost (£/MJ)*
FHV As for Petrol/Diesel ICE
HHV As for Petrol/Diesel ICE
HEV Total Cost (£) = ICE Range (km) x Vehicle efficiency (MJ/km) x Fuel tank cost (£/MJ)
+ ( Electric Range (km) x Vehicle efficiency (MJ/km) x Battery cost (£/MJ)*** )
/ Battery usable SOC for electric range (%)

PHEV
/ REEV
Total Cost (£) = ICE Range (km) x Vehicle efficiency (MJ/km) x Fuel tank cost (£/MJ)
+ Battery charger (£)**
BEV Total Cost (£) =
( Electric Range (km) x Vehicle efficiency (MJ/km) x Battery cost (£/MJ)*** )
+ Battery charger (£)**
H2FC,
H2FC
PHEV /REEV
Total Cost (£) = ICE Range (km) x Vehicle efficiency (MJ/km) x H2 storage cost (£/MJ)
+ Battery charger (£)
Notes: * Similar costs assumed for CNG or LNG storage per MJ fuel
** Separate fixed costs for an LDV charger, for an HDV charger and for a Motorcycle charger.
*** Using different costs for (a) BEV (car/motorcycle), BEV (van/HDV), Non-BEV (car/motorcycle), Non-
BEV (van/HDV). Where: ‘Non-BEV’ includes all HEV, H2FC, PHEV and REEV.
**** Vehicle efficiency (MJ/km) is based on the calculated real-world value/basis
4.1.2.3 Glider cost
The glider cost is simply calculated according to the following formula:
Glider Cost (£) = Basic capital cost (£) – Powertrain cost (£) – Energy storage cost (£)
Where:
Basic capital cost (£) = Basic capital price (£) / ( 1 + Total manuf.+ dealer margin )
The methodologies employed for calculating the effects of multiple technologies and
estimating cost reduction 2010-2050 are summarised in the following sub-sections.
4.1.3.1 Combining individual technology costs
Individual efficiency improvement technology (EIT) costs are calculated in an additive way
based on the net increased percentage penetration/deployment in the new vehicle fleet
versus 2010 levels from 2010 - 2050 (see Section 5.2 on the assumptions for this aspect).
Total costs for efficiency improvement technologies are therefore calculated as follows:
Total EIT costs (£) = EIT[1] Cost (£) x % Deployment EIT[1]
+ EIT[2] Cost (£) x % Deployment EIT[2]
+ EIT[3] Cost (£) x % Deployment EIT[3]
+ etc
Where:
EIT[1] Cost (£) = Total cost of efficiency improvement technology 1
% Deployment EIT[1] = Net increased percentage deployment of EIT 1 versus 2010
4.1.3.2 Estimating future technology cost reduction
The future cost reductions of individual efficiency improvement technologies from 2010 -
2050 is estimated using a learning rate methodology. This method allows the calculation of
cost reductions based on assumptions on the deployment of a particular technology, the
learning rate and initial level of deployment. Box 4.1 provides a summary of learning rate
methodology, as applied in the CCC’s transport MACC model (AEA, 2009).

Box 4.1: The utilisation of learning rates in CCC’s transport MACC model (AEA, 2009)*
Learning rates in the existing transport MACC model are presented as decimal numbers between 0
and 1. A learning rate of 1 indicates a technology is in mass manufacture and there will not be further
decrease in cost with increases in production levels. A learning rate of 0.95 for a particular
technology indicates that the marginal cost of a vehicle equipped with that technology will reduce by
5% every time cumulative production levels double; a learning rate of 0.60 indicates that the marginal
cost will decrease by 40% each time cumulative production levels double. The formula used to
calculate the learning rates is as follows:
b
o
t
ot
M
M
CC and
2ln
ln PR
b
Where C0 = Marginal capital cost in year 0 (starting year)
Ct = Marginal capital cost at mass manufacture
M0 = Estimate of cumulative production volumes in year 0 (starting year)
Mt = Estimate of cumulative production volumes at mass production
PR = Estimate of the learning rate
ln = Natural log
The more mature technologies such as conventional petrol engines are at or close to mass-
production, and so the range of learning rates will be small. In contrast, EVs and PHEVs are some
way from mass production so there is far more uncertainty regarding the rate at which costs will
reduce. As a result the range of learning rates will be significantly greater.
Finally it should be noted that technology learning is a global phenomenon that is ultimately linked to
global production volumes. For reasons of expediency in the existing transport MACC model learning
rates are however related to UK sales. This implicitly assumes that the increase in UK sales is a
proportionate reflection of global trends, and may also quickly and implicitly result in an extension of
learning rates to large global production volumes (if for instance a UK production volume of 100,000
corresponds to a global production volume which is likely to be an order of magnitude bigger).
Notes: Corrected from the original report wording to reflect cumulative production basis of calculations.
A similar methodology has been applied in this study’s analysis, with a 2010 base. In
general, where cost estimates have been provided on the basis of mass deployment - as is
the case for the TNO (2011) dataset used as a basis for cars and vans – installed capacity
has been set at a higher level for consistency, to ensure lower rates of future learning. The
specific assumptions on learning rates and initial stock are presented in Section 4.2.2. The
technology deployment assumptions are summarised in Chapter 5.
4.2 Key Sources and Assumptions
4.2.1 Basic components
This section provides a summary of sources and assumptions for basic component elements
which are generally consistent across modes and powertrains.
The study analysis assumptions for the basic component capital costs are presented in Table
4.3. These technology costs have been sourced primarily from Element Energy’s recent
report for the LowCVP (EE, 2011), which carried out a comprehensive review of the material
in this area available from the literature, and from TNO (2011) for a selection of specific
elements. The figures provided for batteries are based mainly on a separate study
commissioned by CCC investigating the anticipated future potential in this area that was
recently completed (CCC/EE, 2012), supplemented with data from EE(2011).

In addition to these mode-independent component costs, there were a number of powertrain
technologies for heavy duty vehicles that were not fully definable using these generic figures
and vehicle characteristics. These are presented instead in the following Table 4.2 and are
based primarily on AEA-Ricardo (2011) with some scaling of technology costs to different
vehicle sizes.
Note there appears to currently be a significant degree of uncertainty in the expected cost of
flywheel hybrid vehicle (FHV) technology at mass deployment. The current values provided
in Table 4.2 are therefore indicative estimates based on a weighed (70:30) average of low
estimates based on AEA-Ricardo (2011) which are for mass-deployment, and high estimates
based on ETI (2012), which are for current conversions of London buses and are likely to be
higher than the costs expected at mass deployment.
Table 4.2: Summary of the additional heavy duty powertrain technology capital cost
assumptions used in the study analysis
Component Type T# 2010 Cost
Small Rigid
Truck
Large rigid
Truck
Articulated
Truck
Construction
Truck Bus Coach
Diesel ICE Ptrain 1 (1) (1) (1) (1) (1) (1)
Diesel FHV
(2)
Ptrain 2 £8,089 £9,898 £13,636 £11,767 £9,898 £9,898
Diesel HHV Ptrain 3 £8,630 £8,630 £14,547 £11,588 £10,560 £10,560
Diesel HEV Ptrain 4 (1) (1) (1) (1) (1) (1)
BEV Ptrain 5 (1) N/A N/A N/A (1) N/A
H2FC Ptrain 6 (1) (1) (1) (1) (1) (1)
DNG ICE
(3)
Ptrain 7 £16,998 £20,800 £28,688 £24,601 £14,160 £20,800
NG ICE
(3)
Ptrain 8 £16,998 £20,800 £28,688 £24,601 £14,160 £20,800
Sources: Based on costs provided in AEA-Ricardo (2011), scaled to different vehicle sizes.
Notes: (1) Calculated entirely based on individual components – see Section 4.1.
(2) Based on a weighted average of low figure based on AEA-Ricardo (2011) and high figure based on
data supplied by ETI (2012) for conversions of London buses by Williams Hybrid Power.
(3) Partially calculated based on individual components – see Section 4.1.

Table 4.3: Summary of the basic component technology capital cost assumptions used in the study analysis
Area Category Unit 2010 2020 2030 2040 2050 Sources and other notes
Battery (BEV)
(car/motorcycle)
Best £/kWh 403.6 176.9 117.8 103.3 88.9 From CCC/EE (2012) to 2030, interpolate to DfT figure at 2050
Low £/kWh 342.0 176.9 100.0 94.4 88.9 EE (2011) to 2030, interpolate to DfT figure at 2050
High £/kWh 1369.0 833.0 530.0 465.0 400.0 EE (2011) to 2030, extrapolate based on best case
Battery (PHEV, other)
(car/motorcycle)
Best £/kWh 737.3 300.4 234.7 205.9 177.1 From CCC/EE (2012) to 2030, interpolate to DfT figure at 2050
Low £/kWh 624.7 300.4 199.3 188.2 177.1 Indicative estimate relative to differentials for EV batteries for cars
High £/kWh 1369.0 833.0 530.0 465.0 400.0 Indicative estimate relative to differentials for EV batteries for cars
Battery (BEV)
(van/HDV)
Best £/kWh 325.9 140.5 95.1 92.0 88.9 From CCC/EE (2012) interpolate relative to DfT figure at 2050 for cars
Battery (PHEV, other)
(van)
Best £/kWh 414.2 183.3 146.3 128.4 110.4 From CCC/EE (2012) interpolate relative to DfT figure at 2050 for cars
H2 storage Best £/kWh 47.0 17.0 8.0 8.0 8.0 EE (2011) to 2030, assume flat 2030
Low £/kWh 35 10 5 5 5 EE (2011) to 2030, assume flat 2030
High £/kWh 59 16 10 10 10 EE (2011) to 2030, assume flat 2030
CNG storage Best £/kWh 3.7 3.7 3.7 3.7 3.7 Based on JEC WTW (2005)
Low £/kWh 3.7 3.7 3.7 3.7 3.7
High £/kWh 3.7 3.7 3.7 3.7 3.7
Petrol ICE Best £/kW 23.3 23.3 23.3 23.3 23.3 TNO (2011), flat 2020-2050
Low £/kW 22.6 22.6 22.6 22.6 22.6 TNO (2011), flat 2020-2050
High £/kW 28 28 28 28 28 EE (2011) to 2030, assume flat 2030-2050
NG ICE Best £/kW 23.3 23.3 23.3 23.3 23.3 As for Petrol
Low £/kW 22.6 22.6 22.6 22.6 22.6 As for Petrol
High £/kW 28 28 28 28 28 As for Petrol
NG ICE (heavy duty) Best £/kW 45.1 45.1 45.1 45.1 45.1 Scaled up from NG ICE using differential between Diesel ICE and Diesel
ICE (heavy duty)Low £/kW 38.7 38.7 38.7 38.7 38.7
High £/kW 60.3 60.3 60.3 60.3 60.3
Diesel ICE Best £/kW 28.9 28.9 28.9 28.9 28.9 TNO (2011) to 2020, flat 2020-2050
Low £/kW 28 28 28 28 28 TNO (2011) to 2020, flat 2020-2050
High £/kW 29.7 29.7 29.7 29.7 29.7 TNO (2011) to 2020, flat 2020-2050

Area Category Unit 2010 2020 2030 2040 2050 Sources and other notes
Diesel ICE
(heavy duty)
Best £/kW 56.0 56.0 56.0 56.0 56.0 Based on information provided by Ricardo (2012)
Low £/kW 48.0 48.0 48.0 48.0 48.0
High £/kW 64.0 64.0 64.0 64.0 64.0
DNG ICE Best £/kW 56.0 56.0 56.0 56.0 56.0 As for Diesel (heavy duty)
Low £/kW 48.0 48.0 48.0 48.0 48.0 As for Diesel (heavy duty)
High £/kW 64.0 64.0 64.0 64.0 64.0 As for Diesel (heavy duty)
Electric motor Best £/kW 33.0 17.7 11.4 11.4 11.4 EE(2011), TNO (2011) flat 2030-2050
Low £/kW 22 10 5 5 5 TNO (2011), EE (2011)
High £/kW 53 25 25 25 25 EE (2011) to 2030, assume flat 2030-2050
Fuel cell Best £/kW 811 250 75 53 48
EE (2011), amended to reflect a more gradual rate of cost reduction
2010-2050: E (2011) 2020 estimates moved to 2030, 2030 estimates
moved to 2040 and then extrapolated to 2050 to reflect expected further
cost reduction. New figures for 2020 are approximately interpolated.
Low £/kW 391 100 35 34 34
High £/kW 902 300 99 70 64
ICE aftertreatment LDV £/vehicle £706 £686 £667 £648 EE (2011) extrapolated 2030-2050 (assume for diesel, with petrol ~50%)
HDV £/tonne kerb weight £0.502 £0.488 £0.474 £0.460 Calculated based on EE (2011), extrapolated 2030-2050
Motorcycle £/vehicle £177 £172 £167 £162 Assume 25% LDV
Electric powertrain LDV £/vehicle £1,350 £1,080 £864 £691 £553 TNO (2011) to 2030, extrapolated 2030-2050
HDV £/tonne kerb weight £0.959 £0.768 £0.614 £0.491 £0.393 Calculated based on TNO (2011) to 2030, extrapolated 2030-2050
Motorcycle £/vehicle £338 £270 £216 £173 £138 Assume 25% of LDV figure
Battery charger LDV £/vehicle £279 £279 £227 £227 £227 EE (2011) to 2030, assume flat 2030-2050
HDV £/vehicle £558 £558 £454 £454 £454 Assume 200% of LDV
Motorcycle £/vehicle £140 £140 £114 £114 £114 Assume 50% of LDV

This section provides a summary of the sources and assumptions for mode specific
efficiency improvement technologies.
The study analysis assumptions for the efficiency improvement technology costs for cars and
vans are presented in Table 4.4. As for the corresponding efficiency figures, these
technology costs have been sourced primarily from TNO (2011) and AEA-TNO (2009).
There are a range of sources available in the literature that have been reviewed and
considered (see Section 8 of this report), providing estimates on technology cost and
performance. However, these two sources were selected for use in this study as the most
up-to-date, relevant (i.e. specific to the European market and conditions), comprehensive
(covering all the significant technologies/types being considered/developed) and consistent.
Table 4.4: Summary of the technology cost assumptions for the car and van analysis
Efficiency Improvement Technology Category # Capital Cost, £ Learning Rate
(3),(4)
Cars
(1)
Vans
(2)
Cars and Vans
Petrol - low friction design and materials PtrainE 1 £28 £22 0.95
Petrol - gas-wall heat transfer reduction PtrainE 2 £40 £54 0.95
Petrol - direct injection (homogeneous) PtrainE 3 £144 £120 0.95
Petrol - direct injection (stratified charge) PtrainE 4 £440 £385 0.95
Petrol - thermodynamic cycle improvements PtrainE 5 £390 £321 0.95
Petrol - cam-phasing PtrainE 6 £64 £64 0.95
Petrol - Variable valve actuation and lift PtrainE 7 £224 £191 0.95
Diesel - Variable valve actuation and lift PtrainE 8 £224 £224 0.95
Diesel - combustion improvements PtrainE 9 £40 £36 0.95
Mild downsizing (15% cylinder content reduction) PtrainE 10 £220 £36 0.95
Medium downsizing (30% cylinder content reduction) PtrainE 11 £378 £479 0.95
Strong downsizing (≥45% cylinder content reduction) PtrainE 12 £520 £626 0.95
Reduced driveline friction PtrainE 13 £40 £36 0.95
Optimising gearbox ratios / down-speeding PtrainE 14 £48 £40 0.95
Automated manual transmission PtrainE 15 £240 £222 0.95
Dual clutch transmission PtrainE 16 £580 £528 0.95
Start-stop hybridisation PtrainE 17 £170 £170 0.95
Regenerative breaking (smart alternator) PtrainE 18 £320 £326 0.90
Aerodynamics improvement Aero 1 £44 £48 0.95
Low rolling resistance tyres Rres 1 £30 £29 0.95
Mild weight reduction (~10% reduction in BIW*) Weight 1 £141 £136 0.95
Medium weight reduction(~25% reduction in BIW*) Weight 2 £352 £323 0.95
Strong weight reduction (~40% reduction in BIW*) Weight 3 £880 £764 0.90
Lightweight components other than BIW Weight 4 £132 £125 0.95
Thermo-electric waste heat recovery Other 1 £800 £1,078 0.85
Secondary heat recovery cycle Other 2 £160 £216 0.90
Auxiliary systems efficiency improvement Other 3 £360 £496 0.95
Thermal management Other 4 £120 £162 0.95
Notes:
(3) Data based on average of C and D class cars from TNO (2011)
(4) Data from TNO (2011) for large cars was used to update the older/previous assumptions for vans by scaling
data from AEA-TNO (2009) – which contained comparable data for both car and van technologies..
(5) Initial stock estimates were set to 200,000 for cars and 100,000 for vans to
(6) Learning rates applied in a consistent way to those developed in AEA (2009).

The study analysis assumptions for the efficiency improvement technology costs for
motorcycles and mopes are presented in Table 4.5. These have been sourced from a limited
range of identified literature including principally publications by the IEA (2009) and ICCT
(2011), together with indicative AEA estimates for technologies based on those for petrol
cars where no suitable data in the literature could be identified.
Table 4.5: Summary of the technology cost assumptions for the motorcycle analysis
Efficiency Improvement Technology Category # Capital Cost, £ Learning Rate
Air assisted direct injection for 2-stroke engines
(1)
PtrainE 1 £22 0.95
Electronic port fuel injection for 4-stroke engines
(1)
PtrainE 2 £71 0.95
Swirl control valve
(1)
PtrainE 3 £20 0.95
Variable ignition timing
(2)
PtrainE 4 £189 0.95
Engine friction reduction
(2)
PtrainE 5 £64 0.95
Optimising transmission systems
(2)
PtrainE 6 £20 0.95
Start-stop hybridisation
(2)
PtrainE 7 £290 0.95
Aerodynamics improvement
(2)
Aero 1 £44 1.00
Low rolling resistance tyres
(2)
Rres 1 £15 0.95
Light weighting
(2)
Weight 1 £176 0.95
Thermo-electric waste heat recovery
(3)
Other 1 £400 0.95
Source: (1) ICCT (2011); (2) Estimates by AEA based on data for cars for measures without cost values
identified by IEA (2009), ICCT (2011) and other sources. (3) AEA estimate based on car option.
The study analysis assumptions for the efficiency improvement technology costs for heavy
duty vehicles are presented in Table 2.14. As for the corresponding efficiency figures, these
have generally been sourced from recent work carried out by AEA and Ricardo for the
European Commission (AEA-Ricardo, 2011). However, in some cases scaling has been
applied where technologies had the same costs across widely different sizes/types of
vehicle. As indicated in the earlier section on vehicle efficiencies, for the purposes of this
studies analysis, the categories provided in AEA-Ricardo (2011) have been mapped onto the
vehicle categories used in this study as follows:
AEA-Ricardo (2011) category Study category
Urban Delivery = Small rigid truck
Regional Delivery = Large rigid truck
Long haul = Articulated
Construction = Construction
TIAX recently carried out a review of the AEA-Ricardo (2011) study assumptions and
modelling results on behalf of ICCT, which was completed in December 2011 (TIAX, 2011).
This study utilised the efficiency and cost assumptions TIAX developed for the recent US
review of heavy duty vehicles (NAS, 2010) that fed into the recent development of
regulations aimed at significantly improving HDV efficiency in the US. The TIAX (2011) study
findings were generally in close consistency with the AEA-Ricardo (2011) work – within the
range of the analysis uncertainty7
. However, there were significant variations between the
costs for light-weighting and for predictive cruise control between the two studies.
For the purposes of the study analysis we have utilised the costs based on the TIAX (2011)
report for light-weighting, since the AEA-Ricardo (2011) estimates were extremely low
7
Confirmed in personal communications between AEA and the lead author of the TIAX (2011) study in December 2011: “the biggest differences
are most likely a result the differences in vehicle size and not in technology costs”.

compared to other estimates in the literature on the cost of light-weighting. For predictive
cruise control, it appears that there may be differences in the sophistications of the systems
evaluated in AEA-Ricardo (2011) and TIAX (2011), since there were also significant
deviations in efficiency (the more efficient system providing greater savings)8
. Therefore, two
levels of predictive cruise control have been used in this study’s analysis. In addition, there
were no figures available in AEA-Ricardo (2011) for mechanical turbocompounding,
therefore estimates were made based on the difference between mechanical and electrical
turbocompounding from ICCT (2009).
Table 4.6: Summary of the technology cost assumptions for the heavy duty vehicle analysis
Technology Cat. # Capital Cost, £ LR
Small
Rigid
Large
Rigid
Articulated
Truck
Construction
Truck Bus Coach
General improvements PtrainE 1 £583 £583 £583 £583 £583 £583 0.95
Mechanical Turbocompound PtrainE 2 £1,824 £1,824 £2,639 £2,435 £2,232 £2,232 0.95
Electrical Turbocompound PtrainE 3 £4,576 £5,600 £6,623 £6,112 £5,600 £5,600 0.95
Heat Recovery (Bottoming Cycles) PtrainE 4 £7,564 £9,256 £10,948 £10,102 £9,256 £9,256 0.95
Controllable Air Compressor PtrainE 5 £112 £112 £152 £132 £112 £112 0.95
Automated Transmission PtrainE 6 £2,800 £2,800 £3,773 £3,286 £2,800 £2,800 0.95
Stop / Start System PtrainE 7 £418 £512 £752 £632 £512 £512 0.95
Pneumatic Booster – Air Hybrid PtrainE 8 £523 £640 £757 £698 £640 £640 0.95
Aerodynamic Fairings Aero 1 £771 £944 £944 £944 £280 £280 0.95
Spray Reduction Mud Flaps Aero 2 £11 £11 £11 £2,800 £11 £11 0.95
Aerodynamic Trailers / Bodies Aero 3 £1,200 £2,800 £2,800 £704 N/A £2,800 0.95
Aerodynamics (Irregular Body Type) Aero 4 £320 £704 £704 £11 N/A N/A 0.95
Active Aero* Aero 5 £817 £1000 £1000 £1000 £1000 £1000 0.90
Low Rolling Resistance Tyres Rres 1 £200 £280 £280 £280 £280 £280 0.95
Single Wide Tyres Rres 2 £660 £660 £1,040 £850 £660 £660 0.95
Automatic Tyre Pressure Adjustment Rres 3 £7,708 £9,432 £11,156 £10,294 £9,432 £9,432 0.90
Light weighting Weight 1 £577 £1,845 £1,826 £1,836 £9,223 £5,534 0.95
Predictive Cruise Control Other 1 N/A £62 £62 £62 N/A £62 0.95
AGM Battery
Other 2 £418 £512 £752 £632 £512 £512 0.95
Alternative Fuel Bodies
Other 3
£9,153 £11,20
0
£13,247 £12,223 N/A N/A 0.95
Advanced Predictive Cruise Control Other 4 N/A £1,120 £1,120 £1,120 N/A £1,120 0.90
Source: Based on datasets from AEA-Ricardo (2011), ICCT (2009) and TIAX (2011).
* Active aero cost based on estimate provided by SMMT (2012).
Notes: The initial deployment rate of most technical measures were set at lower rates compared to LDVs,
reflecting the different basis of the AEA-Ricardo (2011) cost estimates and the greater potential for cost
reduction through a combination learning and mass deployment. Measures such as aerodynamic
farings and low rolling resistance tyres were set at slightly higher initial deployment reflecting their
already significant presence in the marketplace and fewer opportunities for cost reduction.
8
Based on personal communications between AEA and the TIAX (2011) lead author. As part of their work for NAS (2010) study, which fed into
the work for ICCT, they gathered data from a wide range of US manufacturers, upon which their study costs and efficiencies are largely based.

5 Technology Compatibility and
Deployment Assumptions
This chapter provides a more detailed summary of the methodological approach and
assumptions used in the development of the study dataset for technology deployment.
5.1 Compatibility and Stackability
In developing the study calculation methodology it was important to factor in the following two
elements:
1) Compatibility: The ability for a particular powertrain type to utilise a particular
technical option (e.g. technologies improving the efficiency of the ICE or conventional
transmission are not compatible/relevant to BEVs);
2) Stackability:
a) The ability for two or more technologies to be simultaneously applied to a vehicle
at all (e.g. cannot simultaneously apply two direct injection technologies);
b) Even if the technologies can technically be simultaneously applied, whether their
effects overlap in a significant way so as to substantially reduce their combined
overall efficiency benefit versus simple combination of their basic impacts.
It was therefore important to consider both of these issues in developing the technology
deployment/penetration assumptions that form the basis for the efficiency and capital cost
calculations.
The following Table 5.1, Table 5.2 and Table 5.3 provide summary of matrices of
compatibility/stackability assumptions for LDV, HDV and motorcycle technologies utilised in
the study calculations. These have been developed from a consideration of the above
effects in order to provide a reasonable safety margin in the over-estimation of potential
efficiency improvements. The assumptions have also been discussed with/checked by
Duncan Kay, a senior consultant at AEA who was previously an automotive engineer working
in fuel economy technologies at Ford.

Table 5.1: Summary of Light Duty Vehicle (car and van) technology compatibility / stackability
# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 1 2 3 4 5 6 7 8
# Type T#
Technology Name
Petrol-lowfrictiondesignandmaterials
Petrol-gas-wallheattransferreduction
Petrol-directinjection(homogeneous)
Petrol-directinjection(stratifiedcharge)
Petrol-thermodynamiccycleimporvements
Petrol-cam-phasing
Petrol-variablevalveactuationandlift
Diesel-variablevalveactuationandlift
Diesel-combustionimprovements
Milddownsizing(15%cylindercontentreduction)
Mediumdownsizing(30%cylindercontentreduction)
Strongdownsizing(>=45%cylindercontentreduction)
Reduceddrivelinefriction
Optimisinggearboxratios/downspeeding
Automatedmanualtransmission
Dualclutchtransmission
Start-stophybridisation
Regenerativebraking(smartalternator)
Aerodynamicsimprovement
Lowrollingresistancetyres
Mildweightreduction
Mediumweightreduction
Strongweightreduction
LightweightcomponentsotherthanBIW
Thermo-electricwasteheatrecovery
Secondaryheatrecoverycycle
Auxiliarysystemsefficiencyimprovement
Thermalmanagement
PetrolICE
DieselICE
PetrolHEV
DieselHEV
PetrolPHEV
DieselPHEV
PetrolREEV
DieselREEV
1 PtrainE 1 Petrol - low friction design and materials X X X X X X X
2 PtrainE 2 Petrol - gas-wall heat transfer reduction X X X X X X X X X X X
3 PtrainE 3 Petrol - direct injection (homogeneous) X X X X X X X X X X X X
4 PtrainE 4 Petrol - direct injection (stratified charge) X X X X X X X X X X X X
5 PtrainE 5 Petrol - thermodynamic cycle imporvements X X X X X X X X X X X
6 PtrainE 6 Petrol - cam-phasing X X X X X X X
7 PtrainE 7 Petrol - variable valve actuation and lift X X X X X X X X X X X
8 PtrainE 8 Diesel - variable valve actuation and lift X X X X X X X X X X X X
9 PtrainE 9 Diesel - combustion improvements X X X X X X X X X X X X
10 PtrainE 10 Mild downsizing (15% cylinder content reduction) X X X
11 PtrainE 11 Medium downsizing (30% cylinder content reduction) X X X
12 PtrainE 12 Strong downsizing (>=45% cylinder content reduction) X X X
13 PtrainE 13 Reduced driveline friction X
14 PtrainE 14 Optimising gearbox ratios / downspeeding X X X X X X X X X
15 PtrainE 15 Automated manual transmission X X X X X X X X X
16 PtrainE 16 Dual clutch transmission X X X X X X X X X
17 PtrainE 17 Start-stop hybridisation X X X X X X X X X
18 PtrainE 18 Regenerative braking (smart alternator) X X X X X X X
19 Aero 1 Aerodynamics improvement X
20 Rres 1 Low rolling resistance tyres X
21 Weight 1 Mild weight reduction X X X
22 Weight 2 Medium weight reduction X X X
23 Weight 3 Strong weight reduction X X X
24 Weight 4 Lightweight components other than BIW X
25 Other 1 Thermo-electric waste heat recovery X
26 Other 2 Secondary heat recovery cycle X
27 Other 3 Auxiliary systems efficiency improvement X
28 Other 4 Thermal management X
Notes: X = not compatible/stackable

Table 5.2: Summary of Heavy Duty Vehicle (all trucks, buses and coaches) technology compatibility / stackability
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 1 2 3 4 5 6 7 8
# Type T#
Technology Name
Generalimprovements(+impactAQemissioncontrol)
MechanicalTurbocompound
ElectricalTurbocompound
HeatRecovery(BottomingCycles)
ControllableAirCompressor
AutomatedTransmission
Stop/StartSystem
PneumaticBooster–AirHybrid
AerodynamicFairings
SprayReductionMudFlaps
AerodynamicTrailers/Bodies
Aerodynamics(IrregularBodyType)
ActiveAero
LowRollingResistanceTyres
SingleWideTyres
AutomaticTyrePressureAdjustment(ATPA)
Lightweighting
PredictiveCruiseControl
SmartAlternator,BatterySensor&AGMBattery
AlternativeFuelBodies(forRCV/Refrigeration/Tipper)
AdvancedPredictiveCruiseControl
DieselICE
DieselFHV
DieselHHV
DieselHEV
BEV
H2FC
DNGICE
NGICE
1 PtrainE 1 General improvements (+ impact AQ emission control) X X X
2 PtrainE 2 Mechanical Turbocompound X X X X X
3 PtrainE 3 Electrical Turbocompound X X X X
4 PtrainE 4 Heat Recovery (Bottoming Cycles) X X X
5 PtrainE 5 Controllable Air Compressor X X X
6 PtrainE 6 Automated Transmission X X X X
7 PtrainE 7 Stop / Start System X X X X X X X
8 PtrainE 8 Pneumatic Booster – Air Hybrid X X X X X X X
9 Aero 1 Aerodynamic Fairings X
10 Aero 2 Spray Reduction Mud Flaps X
11 Aero 3 Aerodynamic Trailers / Bodies X X
12 Aero 4 Aerodynamics (Irregular Body Type) X X
13 Aero 5 Active Aero X
14 Rres 1 Low Rolling Resistance Tyres X
15 Rres 2 Single Wide Tyres X
16 Rres 3 Automatic Tyre Pressure Adjustment (ATPA) X
17 Weight 1 Light weighting X
18 Other 1 Predictive Cruise Control X X
19 Other 2 Smart Alternator, Battery Sensor & AGM Battery X X X
20 Other 3 Alternative Fuel Bodies (for RCV /Refrigeration /Tipper) X X X X X
21 Other 4 Advanced Predictive Cruise Control X X

Table 5.3: Summary of motorcycle and moped technology compatibility / stackability
1 2 3 4 5 6 7 8 9 10 11 1 2 3 4
# Type T#
Technology Name
Airassisteddirectinjectionfor2-strokeengines
Electronicportfuelinjectionfor4-strokeengines
Swirlcontrolvalve
Variableignitiontiming
Enginefrictionreduction
Optimisingtransmissionsystems
Start-stophybridisation
Aerodynamicsimprovement
Lowrollingresistancetyres
Lightweighting
Thermo-electricwasteheatrecovery
PetrolICE
PetrolHEV
BEV
H2FC
1 PtrainE 1 Air assisted direct injection for 2-stroke engines X X X X
2 PtrainE 2 Electronic port fuel injection for 4-stroke engines X X X X
3 PtrainE 3 Swirl control valve X X X
4 PtrainE 4 Variable ignition timing X X X
5 PtrainE 5 Engine friction reduction X X X
6 PtrainE 6 Optimising transmission systems X X X X
7 PtrainE 7 Start-stop hybridisation X X X X
8 Aero 1 Aerodynamics improvement X
9 Rres 1 Low rolling resistance tyres X
10 Weight 1 Light weighting X
11 Other 1 Thermo-electric waste heat recovery X

5.2 Deployment
This section summarises the basis for development/source of the technology deployment
rates used in the development of the efficiency and cost trajectories. This used a
combination of existing estimates (e.g. AEA-Ricardo, 2011 for HDVs) and/or estimates based
on combination of current regulatory targets, expert judgement on potential/likely rates of
technology deployment.
One of the basic starting assumptions for the analysis is that (where they are compatible with
the powertrain) individual efficiency technologies are deployed at the same average rate
across all powertrains. For example, if VVTL (variable valve timing and lift) is taken up at a
rate of 20% in petrol ICE cars, it will also be applied in the calculations at the same 20% rate
for HEVs, and not at all for BEVs (where it is incompatible). In reality there may be some
strategising in the application of such technologies between different compatible powertrain
technologies, but it is impossible to accurately predict what might be the result. Using this
simplified assumption also facilitates comparisons of the potential efficiency improvements
between different powertrain types.
Three other principal considerations were also applied in developing the deployment
trajectories:
1) Maximising reductions in GHG/energy consumption: One of the stipulations in the study
specification was to develop trajectories that should be consistent with the goal of
progressively reducing new vehicle CO2 as far as is practicable by 2050.
2) Cost-effectiveness: In general it is assumed that those technological options that are the
most cost-effective (e.g. in £ per % reduction in energy consumption) will be applied
first/at a higher rate, with less cost-effective technologies being applied later in order to
achieve greater levels of reduction in later periods.
3) Maximum deployment: Besides the issues of compatibility and stackability of
technologies, for some technologies it is not possible to deploy them at 100% rate across
the entirety of the vehicle category due to some practical limitations. For example, the
‘Alternative Fuel Bodies’ technology for heavy duty vehicles is only relevant/applicable for
RCVs, refrigerated vehicles and some construction trucks. Since these have a finite
share in the truck fleet, this forms an upper boundary above which the technology cannot
be applied.
The following subsections provide more details on the specific assumptions utilised for light
duty vehicles, motorcycles and heavy duty vehicles.
5.2.1 Light duty vehicles and motorcycles
For passenger cars, the assumptions on efficiency improvement technology deployment are
presented in Table 5.4. The initial technology deployment rates have been developed in line
with the expectation that the EU-wide 2020 regulatory target (95gCO2/km) is likely to be
largely be achieved by improvements to conventional vehicles, rather than significant
introduction of increasingly electrified powertrains (i.e. HEVs, PHEVs, REEVs and BEVs) on
the basis of their current relative cost-effectiveness (TNO, 2011). It is currently anticipated
that most of the benefits will be achieve through a combination engine and transmission
improvements including direct injection (for petrol engines), engine downsizing + boost and
stop-start, as well as others (Bosch, 2010). The UK currently has new car CO2 emissions
higher than the UK average and this seems unlikely to change in the short-term under the
existing arrangements.
In terms of the likely rate of introduction of efficiency improvement technologies into the new
vehicle fleet, the following Box 5.1 provides an extract from TNO (2011) summarising their
findings in this area.

Box 5.1: Extract from TNO (2011) on the timing for vehicle and engine platform changes
For the OEMs, selected vehicle models and engine platforms analysed in this study the following
conclusions could be drawn:
On average vehicle models have a platform change every 6 – 8 years and are refreshed with a
face-lift between 2-4 years after a platform change.
Engine platforms have a long lifespan, typically 10 – 15 years but during that time will have minor
or major upgrades and additional variants added. There is no typical timing pattern for the
introduction of new variants or upgrades (it is dependent on the OEM and engine platform) but in
general minor upgrades/variants to engine platforms are added fairly frequently (e.g. higher power
variant) and major upgrades/variants added less frequently occurring anywhere from 3 to 7 years
(e.g. a turbocharged variant of a naturally aspirated gasoline engine).
Vehicle platform changes / facelifts and engine variants / upgrades are staggered so that changes
to all vehicle models or all engine platforms are not all made within the same year.
For vans, the assumptions on efficiency improvement technology deployment are presented
in Table 5.5. It is assumed that technologies are deployed in a similar way to the passenger
car sector, but with a degree of lag in technological uptake reflecting the greater
conservatism of this sector (i.e. uptake in vans follows confirmation of success in cars). The
resulting trajectory in gCO2/km (test-cycle) from 2010-2020 has been developed to be
broadly in line with the EU-wide regulatory requirements (i.e. 147gCO2/km).
For motorcycles, there is very limited information available in the literature, and currently no
significant regulatory investigation/action on improving their fuel efficiency, since they only
comprise a very limited proportion of overall energy consumption/GHG from road transport.
However, it is anticipated that this situation will change and there will some benefits also to
be transferred from technology development from petrol LDVs in certain areas. The
technology deployment assumptions developed for motorcycles, presented in Table 5.6,
therefore reflect the longer term goal of maximising efficiency improvement, but with
somewhat lower ambition on improvements in the short term compared to other modes.
The deployment of the direct injection and port fuel injection technologies are limited by the
proportion of motorcycles and mopeds with 2-stroke or 4-stroke engines in the fleet. 2-stroke
engines are only generally used in some low-powered mopeds/scooters. In the absence of
an alternative suitable source from the literature an assumption is used in the study
calculations that 25% of all motorcycle engines are 2-stroke and 75% are 4-stroke.

Table 5.4: Deployment assumptions for passenger car efficiency improvement technologies
Type Sub-component Type T# Total Deployment Max %
£/%
Eff
Additional Deployment
(above 2010 levels)
2010 2020 2030 2040 2050 2020 2030 2040 2050
Car Petrol - low friction design and materials PtrainE 1 10.0% 50.0% 90.0% 100.0% 100.0% 100% £14 40.0% 80.0% 90.0% 90.0%
Car Petrol - gas-wall heat transfer reduction PtrainE 2 10.0% 40.0% 80.0% 100.0% 100.0% 100% £13 30.0% 70.0% 90.0% 90.0%
Car Petrol - direct injection (homogeneous) PtrainE 3 10.0% 55.0% 45.0% 40.0% 30.0% 100% £27 45.0% 35.0% 30.0% 20.0%
Car Petrol - direct injection (stratified charge) PtrainE 4 5.0% 10.0% 15.0% 20.0% 100% £48 5.0% 10.0% 15.0% 20.0%
Car Petrol - thermodynamic cycle improvements PtrainE 5 0.0% 5.0% 10.0% 15.0% 100% £27 0.0% 5.0% 10.0% 15.0%
Car Petrol - cam-phasing PtrainE 6 10.0% 40.0% 25.0% 10.0% 0.0% 100% £16 30.0% 15.0% 0.0% -10.0%
Car Petrol - variable valve actuation and lift PtrainE 7 5.0% 20.0% 35.0% 45.0% 55.0% 100% £21 15.0% 30.0% 40.0% 50.0%
Car Diesel - variable valve actuation and lift PtrainE 8 10.0% 30.0% 70.0% 100.0% 100% £224 10.0% 30.0% 70.0% 100.0%
Car Diesel - combustion improvements PtrainE 9 10.0% 60.0% 90.0% 100.0% 100.0% 100% £20 50.0% 80.0% 90.0% 90.0%
Car Mild downsizing (15% cylinder content reduction) PtrainE 10 25.0% 60.0% 25.0% 5.0% 0.0% 100% £40 35.0% 0.0% -20.0% -25.0%
Car Medium downsizing (30% cylinder content reduction) PtrainE 11 15.0% 30.0% 50.0% 55.0% 25.0% 100% £44 15.0% 35.0% 40.0% 10.0%
Car Strong downsizing (>=45% cylinder content reduction) PtrainE 12 5.0% 10.0% 25.0% 40.0% 75.0% 100% £30 5.0% 20.0% 35.0% 70.0%
Car Reduced driveline friction PtrainE 13 5.0% 40.0% 80.0% 100.0% 100.0% 100% £40 35.0% 75.0% 95.0% 95.0%
Car Optimising gearbox ratios / downspeeding PtrainE 14 10.0% 60.0% 90.0% 100.0% 100.0% 100% £12 50.0% 80.0% 90.0% 90.0%
Car Automated manual transmission PtrainE 15 5.0% 30.0% 50.0% 40.0% 20.0% 100% £48 25.0% 45.0% 35.0% 15.0%
Car Dual clutch transmission PtrainE 16 1.0% 20.0% 40.0% 60.0% 80.0% 100% £97 19.0% 39.0% 59.0% 79.0%
Car Start-stop hybridisation PtrainE 17 5.0% 75.0% 100.0% 100.0% 100.0% 100% £34 70.0% 95.0% 95.0% 95.0%
Car Regenerative braking (smart alternator) PtrainE 18 1.0% 25.0% 60.0% 100.0% 100.0% 100% £46 24.0% 59.0% 99.0% 99.0%
Car Aerodynamics improvement Aero 1 5.0% 30.0% 70.0% 90.0% 100.0% 100% £25 25.0% 65.0% 85.0% 95.0%
Car Low rolling resistance tyres Rres 1 20.0% 100.0% 100.0% 100.0% 100.0% 100% £10 80.0% 80.0% 80.0% 80.0%
Car Mild weight reduction Weight 1 5.0% 65.0% 60.0% 30.0% 0.0% 100% £70 60.0% 55.0% 25.0% -5.0%
Car Medium weight reduction Weight 2 3.0% 10.0% 25.0% 50.0% 50.0% 100% £59 7.0% 22.0% 47.0% 47.0%
Car Strong weight reduction Weight 3 2.0% 3.0% 7.0% 20.0% 50.0% 100% £73 1.0% 5.0% 18.0% 48.0%
Car Lightweight components other than BIW Weight 4 2.0% 10.0% 40.0% 90.0% 100% £66 2.0% 10.0% 40.0% 90.0%
Car Thermo-electric waste heat recovery Other 1 0.0% 5.0% 20.0% 30.0% 100% £400 0.0% 5.0% 20.0% 30.0%
Car Secondary heat recovery cycle Other 2 1.0% 5.0% 20.0% 30.0% 100% £80 1.0% 5.0% 20.0% 30.0%
Car Auxiliary systems efficiency improvement Other 3 30.0% 60.0% 100.0% 100.0% 100.0% 100% £30 30.0% 70.0% 70.0% 70.0%
Car Thermal management Other 4 25.0% 50.0% 75.0% 100.0% 100.0% 100% £48 25.0% 50.0% 75.0% 75.0%
Notes: 2010 deployment levels partly informed by information supplied by SMMT following the presentation of draft project results in February 2012.

Table 5.5: Deployment assumptions for van efficiency improvement technologies
Type Sub-component Type T# Total Deployment
Max
%
£/%
Eff
(above 2010 levels)
2010 2020 2030 2040 2050 2020 2030 2040 2050
Van Petrol - low friction design and materials PtrainE 1 5.0% 45.0% 80.0% 90.0% 90.0% 100% £15 40.0% 75.0% 85.0% 85.0%
Van Petrol - gas-wall heat transfer reduction PtrainE 2 5.0% 30.0% 65.0% 85.0% 85.0% 100% £18 25.0% 60.0% 80.0% 80.0%
Van Petrol - direct injection (homogeneous) PtrainE 3 5.0% 45.0% 30.0% 25.0% 15.0% 100% £22 40.0% 25.0% 20.0% 10.0%
Van Petrol - direct injection (stratified charge) PtrainE 4 0.0% 0.0% 0.0% 5.0% 10.0% 100% £41 0.0% 0.0% 5.0% 10.0%
Van Petrol - thermodynamic cycle improvements PtrainE 5 0.0% 0.0% 0.0% 5.0% 10.0% 100% £21 0.0% 0.0% 5.0% 10.0%
Van Petrol - cam-phasing PtrainE 6 5.0% 35.0% 15.0% 0.0% 0.0% 100% £16 30.0% 10.0% -5.0% -5.0%
Van Petrol - variable valve actuation and lift PtrainE 7 0.0% 10.0% 20.0% 30.0% 40.0% 100% £17 10.0% 20.0% 30.0% 40.0%
Van Diesel - variable valve actuation and lift PtrainE 8 0.0% 0.0% 15.0% 55.0% 85.0% 100% £224 0.0% 15.0% 55.0% 85.0%
Van Diesel - combustion improvements PtrainE 9 5.0% 50.0% 75.0% 85.0% 85.0% 100% £24 45.0% 70.0% 80.0% 80.0%
Van Mild downsizing (15% cylinder content reduction) PtrainE 10 20.0% 50.0% 10.0% 0.0% 0.0% 100% £13 30.0% -10.0% -20.0% -20.0%
Van Medium downsizing (30% cylinder content reduction) PtrainE 11 10.0% 20.0% 35.0% 40.0% 10.0% 100% £51 10.0% 25.0% 30.0% 0.0%
Van Strong downsizing (>=45% cylinder content reduction) PtrainE 12 0.0% 0.0% 10.0% 25.0% 60.0% 100% £34 0.0% 10.0% 25.0% 60.0%
Van Reduced driveline friction PtrainE 13 0.0% 30.0% 65.0% 85.0% 85.0% 100% £36 30.0% 65.0% 85.0% 85.0%
Van Optimising gearbox ratios / downspeeding PtrainE 14 5.0% 55.0% 80.0% 90.0% 90.0% 100% £15 50.0% 75.0% 85.0% 85.0%
Van Automated manual transmission PtrainE 15 0.0% 20.0% 35.0% 25.0% 5.0% 100% £55 20.0% 35.0% 25.0% 5.0%
Van Dual clutch transmission PtrainE 16 0.0% 10.0% 25.0% 45.0% 65.0% 100% £106 10.0% 25.0% 45.0% 65.0%
Van Start-stop hybridisation PtrainE 17 0.0% 65.0% 85.0% 85.0% 85.0% 100% £42 65.0% 85.0% 85.0% 85.0%
Van Regenerative braking (smart alternator) PtrainE 18 0.0% 15.0% 45.0% 85.0% 85.0% 100% £54 15.0% 45.0% 85.0% 85.0%
Van Aerodynamics improvement Aero 1 0.0% 20.0% 55.0% 75.0% 85.0% 100% £43 20.0% 55.0% 75.0% 85.0%
Van Low rolling resistance tyres Rres 1 15.0% 90.0% 85.0% 85.0% 85.0% 100% £10 75.0% 70.0% 70.0% 70.0%
Van Mild weight reduction Weight 1 0.0% 5.0% 45.0% 15.0% 0.0% 100% £90 5.0% 45.0% 15.0% 0.0%
Van Medium weight reduction Weight 2 0.0% 5.0% 10.0% 35.0% 35.0% 100% £53 5.0% 10.0% 35.0% 35.0%
Van Strong weight reduction Weight 3 0.0% 5.0% 0.0% 13.0% 43.0% 100% £69 5.0% 0.0% 13.0% 43.0%
Van Lightweight components other than BIW Weight 4 0.0% 5.0% 0.0% 30.0% 80.0% 100% £82 5.0% 0.0% 30.0% 80.0%
Van Thermo-electric waste heat recovery Other 1 0.0% 0.0% 0.0% 15.0% 25.0% 100% £539 0.0% 0.0% 15.0% 25.0%
Van Secondary heat recovery cycle Other 2 0.0% 0.0% 0.0% 15.0% 25.0% 100% £108 0.0% 0.0% 15.0% 25.0%
Van Auxiliary systems efficiency improvement Other 3 25.0% 50.0% 85.0% 85.0% 85.0% 100% £45 25.0% 60.0% 60.0% 60.0%
Van Thermal management Other 4 20.0% 40.0% 60.0% 85.0% 85.0% 100% £65 20.0% 40.0% 65.0% 65.0%
Notes: 2010 deployment levels partly informed by information supplied by SMMT following the presentation of draft project results in February 2012.

Table 5.6: Deployment assumptions for motorcycle efficiency improvement technologies
Type Sub-component Type T# Total Deployment
Max
%
£/%
Eff
(above 2010 levels)
2010 2020 2030 2040 2050 2020 2030 2040 2050
Motorcycle Air assisted direct injection for 2-stroke engines * PtrainE 1 10.0% 20.0% 25.0% 25.0% 25% £1 10.0% 20.0% 25.0% 25.0%
Motorcycle Electronic port fuel injection for 4-stroke engines * PtrainE 2 10.0% 20.0% 35.0% 75.0% 75% £4 10.0% 20.0% 35.0% 75.0%
Motorcycle Swirl control valve PtrainE 3 40.0% 80.0% 100% 100% 100% £3 40.0% 80.0% 100% 100%
Motorcycle Variable ignition timing PtrainE 4 20.0% 30.0% 40.0% 50.0% 100% £22 20.0% 30.0% 40.0% 50.0%
Motorcycle Engine friction reduction PtrainE 5 20.0% 50.0% 100% 100% 100% £16 20.0% 50.0% 100% 100%
Motorcycle Optimising transmission systems PtrainE 6 5.0% 15.0% 40.0% 80.0% 100% £40 5.0% 15.0% 40.0% 80.0%
Motorcycle Start-stop hybridisation PtrainE 7 10.0% 50.0% 100% 100% 100% £58 10.0% 50.0% 100% 100%
Motorcycle Aerodynamics improvement Aero 1 1.0% 10.0% 30.0% 50.0% 100% £50 1.0% 10.0% 30.0% 50.0%
Motorcycle Low rolling resistance tyres Rres 1 10.0% 75.0% 100% 100% 100% £10 10.0% 75.0% 100% 100%
Motorcycle Light weighting Weight 1 5.0% 10.0% 20.0% 40.0% 100% £88 5.0% 10.0% 20.0% 40.0%
Motorcycle Thermo-electric waste heat recovery Other 1 1.0% 5.0% 10.0% 20.0% 100% £200 1.0% 5.0% 10.0% 20.0%
Notes: * Deployment limited by proportion of motorcycles and mopeds with 2-stroke or 4-stroke engines in the fleet. 2-stroke engines are only generally used in low-powered
mopeds/scooters. In the absence of an alternative suitable source from the literature an assumption is made that 25% of all motorcycle engines are 2-stroke.

5.2.2 Heavy duty vehicles
The assumptions for deployment of heavy duty vehicle technologies have been based
primarily on those developed by AEA and Ricardo for recent work on GHG reduction from
HDVs for the European Commission (AEA-Ricardo, 2011), which ran up to 2030, together
with consideration of alternative deployment scenarios from TIAX (2011) and ICCT (2009).
In this project two sets of assumptions were developed – ‘Cost-Effective’ and ‘Challenge’
scenarios. The assumptions used for this study’s analysis are based on the ‘Challenge’
scenario, with some modifications to factor in changes to some of the core technology
assumptions for this study (i.e. mainly capital costs, detailed in section 4.1.3). This is in line
with the overall study objective for estimating the possibilities for reducing GHG as far as
possible by 2050. Extrapolations on these scenarios from 2030-2050 were developed also
based on this over-arching ethos and the other principal considerations mentioned earlier.
In terms of setting maximum limits for the deployment of certain technical options, the main
options that are:
Aerodynamic bodies for regular body types: limited by the % share of regular bodies
of the whole new vehicle fleet.
Aerodynamic bodies for regular body types: limited by the % share of irregular bodies
of the whole new vehicle fleet.
Alternative fuel bodies: assumed to be limited by the share of refrigerated
vehicles/RCVs for regular trucks and by the share of tippers/concrete mixers for
construction trucks.
The maximum deployment assumptions developed for these technologies were estimated
according to statistics on different body types from DfT vehicle licensing statistics for rigid
trucks, and from data from CLEAR (2010) on semi-trailer registrations sourced for the AEA-
Ricardo (2011) project. These are presented in Table 5.7 below.
Table 5.7: Rigid truck and articulated trailer body types
Small rigid
trucks
<15 t GVW
Large rigid
trucks
>15 t GVW
Construction
(3) Articulated
trucks
(4)
Number
Regular body
(1)
94.28 41.57 0 128.13
Irregular body
(2)
40.19 50.09 73.61 42.24
Refrigerated/RCV/Street Cleansing 14.9 20.6 0 27.29
Tipper/Concrete Mixer 0 0 58.9 0
Total 134.5 91.7 73.61 170.37
Percentage
Regular body
(1)
70% 45% 0% 75%
Irregular body
(2)
30% 55% 100% 25%
Refrigerated/RCV/Street Cleansing 11% 22% 0% 16%
Tipper/Concrete Mixer 0% 0% 80% 0%
Sources: Rigid trucks – based on DfT licensing statistics (2011) - Table VEH0522; Semi-trailers % split based on
data on trailer registrations for the UK from CLEAR (2010) sourced for AEA-Ricardo (2011), and
numbers calculated from total fleet of articulated trucks from DfT statistics.
Notes: (1) Sum of categories: box van, curtain sided, insulated van, goods, panel van, tower wagon, Luton
van, van and truck; (2) All other categories, except construction; (3) Tipper, skip-loader or concrete
mixer. (4) Excluding tipper truck trailer types – estimated at 8.3% based on CLEAR (2010).

Table 5.8: Deployment assumptions for small rigid truck efficiency improvement technologies
Type
Sub-component Type T# Total Deployment Max % £/% Eff
(above 2010 levels)
2010 2020 2030 2040 2050 2020 2030 2040 2050
Small rigid General improvements (+ impact AQ emission control) PtrainE 1 100% 100% 100% 100% 100% £254 100% 100% 100% 100%
Small rigid Mechanical Turbocompound PtrainE 2 0.0% 10.0% 30.0% 40.0% 100% £2,735 0.0% 10.0% 30.0% 40.0%
Small rigid Electrical Turbocompound PtrainE 3 0.0% 1.0% 15.0% 30.0% 100% £4,576 0.0% 1.0% 15.0% 30.0%
Small rigid Heat Recovery (Bottoming Cycles) PtrainE 4 0.0% 0.0% 5.0% 20.0% 100% £5,043 0.0% 0.0% 5.0% 20.0%
Small rigid Controllable Air Compressor PtrainE 5 0.0% 0.0% 0.0% 0.0% 100% £- 0.0% 0.0% 0.0% 0.0%
Small rigid Automated Transmission PtrainE 6 20.0% 50.0% 100% 100% 100% £560 20.0% 50.0% 100% 100%
Small rigid Stop / Start System PtrainE 7 100% 100% 100% 100% 100% £70 100% 100% 100% 100%
Small rigid Pneumatic Booster – Air Hybrid PtrainE 8 0.0% 0.0% 0.0% 0.0% 100% £349 0.0% 0.0% 0.0% 0.0%
Small rigid Aerodynamic Fairings Aero 1 0.0% 0.0% 20.0% 20.0% £- 0.0% 0.0% 20.0% 20.0%
Small rigid Spray Reduction Mud Flaps Aero 2 2.5% 10.0% 50.0% 100% 100% £11 2.5% 10.0% 50.0% 100%
Small rigid Aerodynamic Trailers / Bodies Aero 3 0.0% 0.0% 30.0% 70.0% 70% £1,200 0.0% 0.0% 30.0% 70.0%
Small rigid Aerodynamics (Irregular Body Type) Aero 4 0.0% 0.0% 10.0% 30.0% 30% £320 0.0% 0.0% 10.0% 30.0%
Small rigid Active Aero Aero 5 0.0% 20.0% 50.0% 70.0% 70% £817 0.0% 20.0% 50.0% 70.0%
Small rigid Low Rolling Resistance Tyres Rres 1 50.0% 75.0% 50.0% 25.0% 100% £200 50.0% 75.0% 50.0% 25.0%
Small rigid Single Wide Tyres Rres 2 0.0% 25.0% 50.0% 75.0% 100% £165 0.0% 25.0% 50.0% 75.0%
Small rigid Automatic Tyre Pressure Adjustment (ATPA) Rres 3 50.0% 100% 100% 100% 100% £7,708 50.0% 100% 100% 100%
Small rigid Light weighting Weight 1 4.0% 30.0% 60.0% 100% 100% £144 4.0% 30.0% 60.0% 100%
Small rigid Predictive Cruise Control Other 1 0.0% 0.0% 20.0% 20.0% 100% £- 0.0% 0.0% 20.0% 20.0%
Small rigid Smart Alternator, Battery Sensor & AGM Battery Other 2 20.0% 60.0% 100% 100% 100% £279 20.0% 60.0% 100% 100%
Small rigid Alternative Fuel Bodies (for RCV /Refrigeration /Tipper) Other 3 0.0% 5.0% 11.0% 11.0% 11% £610 0.0% 5.0% 11.0% 11.0%
Small rigid Advanced Predictive Cruise Control Other 4 0.0% 0.0% 0.0% 0.0% 100% £- 0.0% 0.0% 0.0% 0.0%

Table 5.9: Deployment assumptions for large rigid truck efficiency improvement technologies
Type Sub-component Type T# Total Deployment Max % £/% Eff
(above 2010 levels)
2010 2020 2030 2040 2050 Max % 2020 2030 2040 2050
Large rigid General improvements (+ impact AQ emission control) PtrainE 1 100% 100% 100% 100% 100% £254 100% 100% 100% 100%
Large rigid Mechanical Turbocompound PtrainE 2 0.0% 10.0% 20.0% 30.0% 100% £2,735 0.0% 10.0% 20.0% 30.0%
Large rigid Electrical Turbocompound PtrainE 3 0.1% 1.0% 25.0% 50.0% 100% £2,240 0.1% 1.0% 25.0% 50.0%
Large rigid Heat Recovery (Bottoming Cycles) PtrainE 4 0.1% 1.0% 10.0% 30.0% 100% £3,702 0.1% 1.0% 10.0% 30.0%
Large rigid Controllable Air Compressor PtrainE 5 0.0% 20.0% 50.0% 100% 100% £112 0.0% 20.0% 50.0% 100%
Large rigid Automated Transmission PtrainE 6 20.0% 50.0% 100% 100% 100% £1,867 20.0% 50.0% 100% 100%
Large rigid Stop / Start System PtrainE 7 100% 100% 100% 100% 100% £171 100% 100% 100% 100%
Large rigid Pneumatic Booster – Air Hybrid PtrainE 8 0.0% 0.0% 0.0% 0.0% 100% £427 0.0% 0.0% 0.0% 0.0%
Large rigid Aerodynamic Fairings Aero 1 95.0% 100% 100% 100% 100% £944 95.0% 100% 100% 100%
Large rigid Spray Reduction Mud Flaps Aero 2 5.0% 20.0% 80.0% 100% 100% £6 5.0% 20.0% 80.0% 100%
Large rigid Aerodynamic Trailers / Bodies Aero 3 7.0% 40.0% 45.0% 45.0% 45% £255 7.0% 40.0% 45.0% 45.0%
Large rigid Aerodynamics (Irregular Body Type) Aero 4 1.0% 20.0% 35.0% 55.0% 55% £108 1.0% 20.0% 35.0% 55.0%
Large rigid Active Aero Aero 5 7.0% 40.0% 45.0% 45.0% 45% £200 7.0% 40.0% 45.0% 45.0%
Large rigid Low Rolling Resistance Tyres Rres 1 95.0% 90.0% 60.0% 10.0% 100% £93 95.0% 90.0% 60.0% 10.0%
Large rigid Single Wide Tyres Rres 2 5.0% 10.0% 40.0% 90.0% 100% £110 5.0% 10.0% 40.0% 90.0%
Large rigid Automatic Tyre Pressure Adjustment (ATPA) Rres 3 50.0% 100% 100% 100% 100% £4,716 50.0% 100% 100% 100%
Large rigid Light weighting Weight 1 4.0% 30.0% 60.0% 100% 100% £839 4.0% 30.0% 60.0% 100%
Large rigid Predictive Cruise Control Other 1 50.0% 70.0% 20.0% 0.0% 100% £41 50.0% 70.0% 20.0% 0.0%
Large rigid Smart Alternator, Battery Sensor & AGM Battery Other 2 30.0% 90.0% 100% 100% 100% £341 30.0% 90.0% 100% 100%
Large rigid Alternative Fuel Bodies (for RCV /Refrigeration /Tipper) Other 3 5.0% 10.0% 22.0% 22.0% 22% £747 5.0% 10.0% 22.0% 22.0%
Large rigid Advanced Predictive Cruise Control Other 4 5.0% 30.0% 80.0% 100% 100% £224 5.0% 30.0% 80.0% 100%

Table 5.10: Deployment assumptions for articulated truck efficiency improvement technologies
(above 2010 levels)
2010 2020 2030 2040 2050 2020 2030 2040 2050
Articulated General improvements (+ impact AQ emission control) PtrainE 1 100% 100% 100% 100% 100% £254 100% 100% 100% 100%
Articulated Mechanical Turbocompound PtrainE 2 10.0% 20.0% 50.0% 20.0% 100% £1,320 10.0% 20.0% 50.0% 20.0%
Articulated Electrical Turbocompound PtrainE 3 1.0% 10.0% 40.0% 80.0% 100% £2,208 1.0% 10.0% 40.0% 80.0%
Articulated Heat Recovery (Bottoming Cycles) PtrainE 4 1.0% 10.0% 30.0% 40.0% 100% £2,190 1.0% 10.0% 30.0% 40.0%
Articulated Controllable Air Compressor PtrainE 5 20.0% 50.0% 100% 100% 100% £101 20.0% 50.0% 100% 100%
Articulated Automated Transmission PtrainE 6 100% 100% 100% 100% 100% £2,515 100% 100% 100% 100%
Articulated Stop / Start System PtrainE 7 90.0% 50.0% 20.0% 10.0% 100% £752 90.0% 50.0% 20.0% 10.0%
Articulated Pneumatic Booster – Air Hybrid PtrainE 8 10.0% 50.0% 80.0% 90.0% 100% £216 10.0% 50.0% 80.0% 90.0%
Articulated Aerodynamic Fairings Aero 1 95.0% 100% 100% 100% 100% £2,360 95.0% 100% 100% 100%
Articulated Spray Reduction Mud Flaps Aero 2 20.0% 80.0% 100% 100% 100% £3 20.0% 80.0% 100% 100%
Articulated Aerodynamic Trailers / Bodies Aero 3 12.0% 50.0% 75.0% 75.0% 75% £255 12.0% 50.0% 75.0% 75.0%
Articulated Aerodynamics (Irregular Body Type) Aero 4 10.0% 20.0% 25.0% 25.0% 25% £141 10.0% 20.0% 25.0% 25.0%
Articulated Active Aero Aero 5 8.0% 50.0% 75.0% 75.0% 75% £125 8.0% 50.0% 75.0% 75.0%
Articulated Low Rolling Resistance Tyres Rres 1 95.0% 90.0% 40.0% 0.0% 100% £56 95.0% 90.0% 40.0% 0.0%
Articulated Single Wide Tyres Rres 2 5.0% 10.0% 60.0% 100% 100% £208 5.0% 10.0% 60.0% 100%
Articulated Automatic Tyre Pressure Adjustment (ATPA) Rres 3 50.0% 100% 100% 100% 100% £3,719 50.0% 100% 100% 100%
Articulated Light weighting Weight 1 4.0% 30.0% 60.0% 100% 100% £830 4.0% 30.0% 60.0% 100%
Articulated Predictive Cruise Control Other 1 75.0% 50.0% 20.0% 0.0% 100% £41 75.0% 50.0% 20.0% 0.0%
Articulated Smart Alternator, Battery Sensor & AGM Battery Other 2 45.0% 50.0% 70.0% 100% 100% £501 45.0% 50.0% 70.0% 100%
Articulated Alternative Fuel Bodies (for RCV /Refrigeration /Tipper) Other 3 5.0% 10.0% 16.0% 16.0% 16% £883 5.0% 10.0% 16.0% 16.0%
Articulated Advanced Predictive Cruise Control Other 4 5.0% 50.0% 80.0% 100% 100% £224 5.0% 50.0% 80.0% 100%

Table 5.11: Deployment assumptions for construction truck efficiency improvement technologies
(above 2010 levels)
2010 2020 2030 2040 2050 2020 2030 2040 2050
Construction General improvements (+ impact AQ emission control) PtrainE 1 100% 100% 100% 100% 100% £658 100% 100% 100% 100%
Construction Mechanical Turbocompound PtrainE 2 7.5% 15.0% 30.0% 40.0% 100% £1,328 7.5% 15.0% 30.0% 40.0%
Construction Electrical Turbocompound PtrainE 3 0.6% 5.5% 30.0% 50.0% 100% £2,222 0.6% 5.5% 30.0% 50.0%
Construction Heat Recovery (Bottoming Cycles) PtrainE 4 0.6% 5.5% 10.0% 20.0% 100% £2,694 0.6% 5.5% 10.0% 20.0%
Construction Controllable Air Compressor PtrainE 5 10.0% 35.0% 75.0% 100% 100% £106 10.0% 35.0% 75.0% 100%
Construction Automated Transmission PtrainE 6 60.0% 75.0% 100% 100% 100% £2,191 60.0% 75.0% 100% 100%
Construction Stop / Start System PtrainE 7 95.0% 50.0% 20.0% 10.0% 100% £316 95.0% 50.0% 20.0% 10.0%
Construction Pneumatic Booster – Air Hybrid PtrainE 8 10.0% 50.0% 80.0% 90.0% 100% £279 10.0% 50.0% 80.0% 90.0%
Construction Aerodynamic Fairings Aero 1 95.0% 100% 100% 100% 100% £1,349 95.0% 100% 100% 100%
Construction Spray Reduction Mud Flaps Aero 2 2.5% 10.0% 50.0% 100% 100% £1,018 2.5% 10.0% 50.0% 100%
Construction Aerodynamic Trailers / Bodies Aero 3 0.0% 0.0% 0.0% 0.0% 0% £122 0.0% 0.0% 0.0% 0.0%
Construction Aerodynamics (Irregular Body Type) Aero 4 12.5% 40.0% 80.0% 100% 100% £4 12.5% 40.0% 80.0% 100%
Construction Active Aero Aero 5 0.0% 0.0% 0.0% 0.0% 0% £- 0.0% 0.0% 0.0% 0.0%
Construction Low Rolling Resistance Tyres Rres 1 95.0% 90.0% 40.0% 0.0% 100% £70 95.0% 90.0% 40.0% 0.0%
Construction Single Wide Tyres Rres 2 5.0% 10.0% 60.0% 100% 100% £155 5.0% 10.0% 60.0% 100%
Construction Automatic Tyre Pressure Adjustment (ATPA) Rres 3 50.0% 100% 100% 100% 100% £4,118 50.0% 100% 100% 100%
Construction Light weighting Weight 1 1.0% 20.0% 50.0% 100% 100% £6,119 1.0% 20.0% 50.0% 100%
Construction Predictive Cruise Control Other 1 62.5% 60.0% 20.0% 0.0% 100% £41 62.5% 60.0% 20.0% 0.0%
Construction Smart Alternator, Battery Sensor & AGM Battery Other 2 35.0% 75.0% 80.0% 100% 100% £421 35.0% 75.0% 80.0% 100%
Construction Alternative Fuel Bodies (for RCV /Refrigeration /Tipper) Other 3 5.0% 10.0% 40.0% 80.0% 80% £815 5.0% 10.0% 40.0% 80.0%
Construction Advanced Predictive Cruise Control Other 4 5.0% 40.0% 80.0% 100% 100% £224 5.0% 40.0% 80.0% 100%

Table 5.12: Deployment assumptions for bus efficiency improvement technologies
(above 2010 levels)
2010 2020 2030 2040 2050 Max % 2020 2030 2040 2050
Bus General improvements (+ impact AQ emission control) PtrainE 1 100% 100% 100% 100% 100% £254 100% 100% 100% 100%
Bus Mechanical Turbocompound PtrainE 2 0.0% 10.0% 30.0% 40.0% 100% £3,347 0.0% 10.0% 30.0% 40.0%
Bus Electrical Turbocompound PtrainE 3 0.0% 1.0% 15.0% 30.0% 100% £5,600 0.0% 1.0% 15.0% 30.0%
Bus Heat Recovery (Bottoming Cycles) PtrainE 4 0.0% 0.0% 5.0% 20.0% 100% £6,171 0.0% 0.0% 5.0% 20.0%
Bus Controllable Air Compressor PtrainE 5 0.0% 0.0% 0.0% 0.0% 100% £- 0.0% 0.0% 0.0% 0.0%
Bus Automated Transmission PtrainE 6 30.0% 80.0% 100% 100% 100% £560 30.0% 80.0% 100% 100%
Bus Stop / Start System PtrainE 7 90.0% 100% 100% 100% 100% £128 90.0% 100% 100% 100%
Bus Pneumatic Booster – Air Hybrid PtrainE 8 0.0% 0.0% 0.0% 0.0% £- 0.0% 0.0% 0.0% 0.0%
Bus Aerodynamic Fairings Aero 1 0.0% 0.0% 0.0% 0.0% 100% £- 0.0% 0.0% 0.0% 0.0%
Bus Spray Reduction Mud Flaps Aero 2 50.0% 100% 100% 100% 100% £11 50.0% 100% 100% 100%
Bus Aerodynamic Trailers / Bodies Aero 3 0.0% 0.0% 0.0% 0.0% 100% £- 0.0% 0.0% 0.0% 0.0%
Bus Aerodynamics (Irregular Body Type) Aero 4 0.0% 0.0% 0.0% 0.0% 0% £- 0.0% 0.0% 0.0% 0.0%
Bus Active Aero Aero 5 0.0% 20.0% 50.0% 70.0% 100% £1000 0.0% 20.0% 50.0% 70.0%
Bus Low Rolling Resistance Tyres Rres 1 25.0% 50.0% 80.0% 60.0% 100% £280 25.0% 50.0% 80.0% 60.0%
Bus Single Wide Tyres Rres 2 0.0% 10.0% 20.0% 40.0% 100% £165 0.0% 10.0% 20.0% 40.0%
Bus Automatic Tyre Pressure Adjustment (ATPA) Rres 3 50.0% 100% 100% 100% 100% £9,432 50.0% 100% 100% 100%
Bus Light weighting Weight 1 4.0% 30.0% 60.0% 100% 100% £1,537 4.0% 30.0% 60.0% 100%
Bus Predictive Cruise Control Other 1 0.0% 0.0% 20.0% 20.0% 100% £- 0.0% 0.0% 20.0% 20.0%
Bus Smart Alternator, Battery Sensor & AGM Battery Other 2 15.0% 35.0% 70.0% 100% 100% £341 15.0% 35.0% 70.0% 100%
Bus Alternative Fuel Bodies (for RCV /Refrigeration /Tipper) Other 3 0.0% 0.0% 0.0% 0.0% 0% £- 0.0% 0.0% 0.0% 0.0%
Bus Advanced Predictive Cruise Control Other 4 0.0% 0.0% 0.0% 0.0% 100% £- 0.0% 0.0% 0.0% 0.0%

Table 5.13: Deployment assumptions for coach efficiency improvement technologies
(above 2010 levels)
2010 2020 2030 2040 2050 Max % 2020 2030 2040 2050
Coach General improvements (+ impact AQ emission control) PtrainE 1 100% 100% 100% 100% 100% £254 100% 100% 100% 100%
Coach Mechanical Turbocompound PtrainE 2 5.0% 10.0% 25.0% 40.0% 100% £1,339 5.0% 10.0% 25.0% 40.0%
Coach Electrical Turbocompound PtrainE 3 0.1% 1.0% 25.0% 50.0% 100% £2,240 0.1% 1.0% 25.0% 50.0%
Coach Heat Recovery (Bottoming Cycles) PtrainE 4 0.1% 1.0% 10.0% 20.0% 100% £3,702 0.1% 1.0% 10.0% 20.0%
Coach Controllable Air Compressor PtrainE 5 0.0% 20.0% 50.0% 100% 100% £112 0.0% 20.0% 50.0% 100%
Coach Automated Transmission PtrainE 6 20.0% 50.0% 100% 100% 100% £1,867 20.0% 50.0% 100% 100%
Coach Stop / Start System PtrainE 7 100% 100% 100% 100% 100% £171 100% 100% 100% 100%
Coach Pneumatic Booster – Air Hybrid PtrainE 8 0.0% 0.0% 0.0% 0.0% 100% £427 0.0% 0.0% 0.0% 0.0%
Coach Aerodynamic Fairings Aero 1 0.0% 0.0% 20.0% 60.0% 100% £280 0.0% 0.0% 20.0% 60.0%
Coach Spray Reduction Mud Flaps Aero 2 5.0% 20.0% 80.0% 100% 100% £6 5.0% 20.0% 80.0% 100%
Coach Aerodynamic Trailers / Bodies Aero 3 0.0% 10.0% 30.0% 90.0% 100% £683 0.0% 10.0% 30.0% 90.0%
Coach Aerodynamics (Irregular Body Type) Aero 4 0.0% 0.0% 0.0% 0.0% 0% £- 0.0% 0.0% 0.0% 0.0%
Coach Active Aero Aero 5 0.0% 10.0% 30.0% 90.0% 100% £200 0.0% 10.0% 30.0% 90.0%
Coach Low Rolling Resistance Tyres Rres 1 95.0% 80.0% 40.0% 10.0% 100% £93 95.0% 80.0% 40.0% 10.0%
Coach Single Wide Tyres Rres 2 0.0% 20.0% 60.0% 90.0% 100% £110 0.0% 20.0% 60.0% 90.0%
Coach Automatic Tyre Pressure Adjustment (ATPA) Rres 3 50.0% 100% 100% 100% 100% £4,716 50.0% 100% 100% 100%
Coach Light weighting Weight 1 4.0% 30.0% 60.0% 100% 100% £2,516 4.0% 30.0% 60.0% 100%
Coach Predictive Cruise Control Other 1 50.0% 70.0% 20.0% 0.0% 100% £41 50.0% 70.0% 20.0% 0.0%
Coach Smart Alternator, Battery Sensor & AGM Battery Other 2 30.0% 90.0% 100% 100% 100% £341 30.0% 90.0% 100% 100%
Coach Alternative Fuel Bodies (for RCV /Refrigeration /Tipper) Other 3 0.0% 0.0% 0.0% 0.0% 0% £- 0.0% 0.0% 0.0% 0.0%
Coach Advanced Predictive Cruise Control Other 4 5.0% 30.0% 80.0% 100% 100% £224 5.0% 30.0% 80.0% 100%

6 Results: Cost and Efficiency
Trajectories from 2010 to 2050
Objectives:
The purpose of Task 3 of the project was to utilise the information gathered in the Task 2
literature review to develop fuel efficiency and capital cost trajectories for different vehicle
categories and technologies to 2050. Specific objectives of the task include:
Developing a trajectory of fuel efficiency and capital cost to 2050 for each
technology in each category;
Ensure the trajectory is consistent with the goal of progressively reducing new
vehicle CO2 to as far as is practicable by 2050;
This chapter sets out the results of the analysis in line with these objectives.
Summary of Main Findings
For passenger cars and vans:
• Conventional powertrains have the greatest potential for % improvements in fuel
efficiency in the long term (though being less efficient in absolute terms), versus
increasingly electrified powertrain alternatives. The overall potential reduction in
energy consumption 2010-2050 ranges from 27%-50% depending on powertrain.
• Capital cost differentials are expected to narrow substantially by 2030, with many
alternatives becoming cost-competitive if fuel savings are included (depending on
future tax rates for different fuels). Assumptions on electric driving range and
battery cost reductions are critical factors. Under low cost assumptions BEV cars
become comparable in price to ICEs by 2050, but under high cost assumptions
H2FC variants become the more cost-effective ultra-low GHG option.
• The benefits of additional improvements to the ICE appear to be marginal for
REEVs after 2020. Also the cost of efficiency improvements to BEVs beyond those
to the basic powertrain are extremely high per gCO2e/km abated. Therefore
uptake of these may be more limited than for other powertrains, although the
impacts on battery capacity/costs also need to be factored into the equation.
For motorcycles the reduction potential identified for different powertrain technologies
is lower than cars and vans (10-36%), but may be due to insufficient information in the
literature. BEV and HEV technologies may become cost-competitive with ICE by 2030.
For heavy duty vehicles in predominantly urban cycles (small rigid trucks and buses):
• Efficiency improvement benefits by 2050 are expected to reach 16-28%. These
reductions are predominantly due to powertrain improvements, with lower levels of
benefit from rolling resistance and lightweighting. The greatest benefits are
therefore achieved through switching from conventional ICE to more efficient
alternative powertrains.
• Purely in terms of capital costs, H2FC technology is the lowest cost ultra-low GHG
option for the long-term, however the comparison with BEV changes if fuel costs
are included. Factoring in likely future fuel costs brings most technologies to
overall cost levels comparable with or lower than Diesel ICE by 2030 (depending
on future fuel tax levels).

For heavy duty vehicles with the greatest proportions of their km outside of urban areas
(large rigid, articulated and construction trucks, coaches):
• Efficiency improvement benefits by 2050 are expected to reach 23-43% (depending
on type/powertrain). These reductions are mostly due to improvements in the
powertrain and aerodynamics (except construction trucks), with lower levels of
benefit from rolling resistance, lightweighting and other technologies.
• Capital costs of alternative powertrains drop to within 6-14% of Diesel ICE by 2050
(depending on vehicle type/powertrain). Factoring in likely future fuel costs brings
most technologies to combined cost levels comparable with or lower than Diesel
ICE by 2030 and essentially all by 2050 (depending on future fuel tax levels).
• DNG ICE powertrains appear to offer a cost effective alternative (under current tax
levels) versus alternatives with substantial lifecycle GHG savings in the short-
medium term, which could be further improved through the use of biomethane. In
the long term H2FC offer greater GHG savings at similar capital costs.
This section provides a summary and short discussion of the main results of the analysis. A
variety of charts and tables are included, illustrating the main results. The full details of the
analysis results (including all the figures behind the presented charts) are available in the
calculations spreadsheet supplied alongside this report.
An important factor to considering when viewing the presented charts on efficiency
improvements is that split/allocation of efficiency savings between different technology
categories is a result of the order in which the efficiency benefits are being applied in the
calculations and therefore only indicative. In applying technical options successively, the
observed actual MJ/km benefit per % improvement for each of the subsequently applied
technology option will be smaller than for the one preceding it. Therefore if the options were
applied in a different order the relative savings in MJ/km for each technology category would
appear slightly different. Technologies are applied in the following category order in the study
analysis calculations:
0. Core powertrain technology improvement
1. Powertrain efficiency
2. Aerodynamics
3. Rolling resistance
4. Weight reduction
5. Other measures
In addition to the assumptions presented in the earlier sections of the report, to aid the
analysis of the results indicative assumptions on the future carbon intensity and price of
different fuels were used, as summarised in Table 6.1. Note: the emission factors for
electricity also factor in estimated losses from battery charging, based on earlier Table 3.1.

Table 6.1: Additional assumptions on the trajectory of carbon intensity and price of energy
carriers from 2010-2050, excluding biofuel effects
Fuel 2010 2020 2030 2040 2050 Notes
gCO2/MJ *
Petrol Direct 68.75 68.75 68.75 68.75 68.75
Diesel Direct 71.39 71.39 71.39 71.39 71.39
Natural Gas (CNG) Direct 56.61 56.61 56.61 56.61 56.61
LNG Direct 56.61 56.61 56.61 56.61 56.61
Electricity Direct 0.00 0.00 0.00 0.00 0.00
Hydrogen Direct 0.00 0.00 0.00 0.00 0.00
Petrol Lifecycle 82.03 82.03 82.03 82.03 82.03
Diesel Lifecycle 86.86 86.86 86.86 86.86 86.86
Natural Gas Lifecycle 65.09 65.09 65.09 65.09 65.09
LNG Lifecycle 76.73 76.73 76.73 76.73 76.73
Electricity Lifecycle 149.95 93.27 49.25 10.64 6.42 (2)
Hydrogen Lifecycle 112.74 86.73 60.73 34.72 8.72 (3)
£/MJ 2010 2020 2030 2040 2050
Petrol Price 0.037 0.045 0.046 0.046 0.046 (2)
Diesel Price 0.034 0.043 0.045 0.045 0.045 (2)
Natural Gas (CNG) Price 0.018 0.024 0.025 0.025 0.025 (3)
LNG Price 0.016 0.016 0.016 0.016 0.016 (4)
Electricity Price 0.042 0.061 0.061 0.070 0.070 (2)
Hydrogen Price 0.076 0.098 0.086 0.091 0.088 (2)
Sources: (1) 2011 Defra/DECC GHG Conversion Factors (DCF, 2011); (2) DECC (2011) plus additionally
factoring in estimated losses from battery charging (see Table 3.1); (3) Estimate based on transition of
production predominantly from natural gas in 2010 to 100% by electrolysis of grid electricity in 2050. (3)
NGVA (2011); (4) Assume same as for CNG
Notes: * Excludes any potential impacts of biofuels on either net emissions or fuel prices.
6.1 Light Duty Vehicles and Motorcycles
This section provides a headline summary of some of the key results from the analysis.
6.1.1 Passenger Cars
Figure 6.2 to Figure 6.6 provide a summary of the impacts of the assumptions on deployment
of efficiency improvements to passenger cars on overall vehicle efficiency by powertrain
type, direct gCO2/km (for comparison with regulatory limit values for 2020), and net capital
cost increases attributed to the different technology areas.
There are a number of key points to draw out of these charts under the best case cost
assumptions:
i. The difference between conventional ICE powertrains and increasingly electrified
alternative powertrains is expected to narrow significantly in the future, as there are
still many options for improvements to engines and transmissions. The overall
potential efficiency improvement seen by Petrol ICE between 2010 and 2050 is
estimated at ~50%, compared to the BEV improvement of ~27%. Improvements to
other powertrains are in-between depending on their relative degree of electrification.
ii. The relative costs of different powertrains is anticipated to very substantially narrow in
the next 20 years, with the cost range of ICE, HEV, PHEV and REEV powertrains
narrowing to less than £2800 by 2030 best cost estimates. Under the study
assumptions BEVs and H2FCVs still have substantially reduced in cost, but still have
significantly higher capital costs in 2030 (by a further ~£1000-£5600). There is a

much lower level of cost reduction anticipated by 2050, with these technologies still
being significantly more expensive than other options. The electric range assumption
for BEVs also still has a significant impact here (assumed to reach 320km by 2050).
iii. If the lower price of electricity is factored into the comparisons, BEVs could become
cost-competitive with ICEs much sooner, but this depends also on future taxation
policy for electricity supplied for transport fuels (i.e. tax might be applied in future
years to recover some of the revenue lost from reduced sales of conventional fuels).
iv. For PHEVs the benefits of improved ICE mode driving appear to still be relatively
significant, at least up until 2030. However, for REEVs the benefits are more
marginal due to the greater proportion of electric-only drive. For BEVs, cost in £ per
gCO2/km reduction for non-powertrain improvements is extremely high – especially in
later periods (reaching £846/gCO2e/km versus and average of £17/gCO2e/km for
petrol ICE technologies). This may suggest certain options are unlikely to be
deployed at the same rate in the more carbon-efficient powertrains on this basis.
However, it is also important to factor in the impact of reduced efficiency on the
battery pack sizing and therefore total costs for a given range (i.e. the additional cost
of the efficiency improvement technology is offset by reduced battery costs).
v. The cost-effectiveness of different powertrains versus the base 2010 petrol ICE
technology appears to converge to a significant degree by 2030 and much further by
2050 for most powertrain types, as illustrated in Figure 6.1. The comparison will
further improve when factoring in relative fuel costs.
vi. The alternate base technology cost reduction scenarios presented in Figure 6.6 show
that under low cost assumptions BEVs could reduce in cost to a similar level to
conventional ICE vehicles by 2050. Under high cost assumptions, their costs could be
significantly higher, with H2FCVs providing a more cost-effective ultra-low GHG
option instead.
Figure 6.1: Trajectory for Passenger Car Efficiency improvement cost-effectiveness by
technology, £ per gCO2e/km reduction *
0
5
10
15
20
25
30
35
40
45
50
2010 2020 2030 2040 2050
Cost-Eff£/gCO2/km(BaseT)
Passenger Car
Petrol ICE
Diesel ICE
Petrol HEV
Diesel HEV
Petrol PHEV
Diesel PHEV
Petrol REEV
Diesel REEV
BEV
H2FC
H2FC PHEV
H2FC REEV
NGICE
Notes: * Based on lifecycle GHG emissions estimates for different fuels

Figure 6.2: Trajectory for Passenger Car Efficiency and Costs for Petrol ICE, PHEV and BEV
2.77
2.07
1.69
1.51 1.38
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
2010 2020 2030 2040 2050
Efficiency,MJ/km
Passenger Car (Petrol ICE) (Real-World)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Core
technology
improvement
Overall vehicle £0
£2,000
£4,000
£6,000
£8,000
£10,000
£12,000
£14,000
£16,000
£18,000
2010 2020 2030 2040 2050
CapitalCost,£
Passenger Car (Petrol ICE)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Energy storage
Powertrain
Glider
1.68
1.27
1.13 1.06 0.99
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
2010 2020 2030 2040 2050
Efficiency,MJ/km
Passenger Car (Petrol PHEV) (Real-World)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Core
technology
improvement
Overall vehicle £0
£5,000
£10,000
£15,000
£20,000
£25,000
2010 2020 2030 2040 2050
CapitalCost,£
Passenger Car (Petrol PHEV)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Energy storage
Powertrain
Glider
0.69
0.63
0.57 0.54 0.51
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
2010 2020 2030 2040 2050
Efficiency,MJ/km
Passenger Car (BEV) (Real-World)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Core
technology
improvement
Overall vehicle £0
£5,000
£10,000
£15,000
£20,000
£25,000
£30,000
£35,000
2010 2020 2030 2040 2050
CapitalCost,£
Passenger Car (BEV)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Energy storage
Powertrain
Glider
Notes: Efficiency: The coloured bars above the ‘Overall vehicle’ component represent the reductions in
energy consumption vs the base vehicle (chart total) due to technical efficiency improvements.
Capital Costs: The coloured bars above the ‘Glider’ component represent additional/marginal costs.

Figure 6.3: Trajectory for Passenger Car Efficiency, Direct gCO2/km and Cost by technology
0.0
0.5
1.0
1.5
2.0
2.5
3.0
2010 2020 2030 2040 2050
NetMJ/km(RW)
Passenger Car
Petrol ICE
Diesel ICE
Petrol HEV
Diesel HEV
Petrol PHEV
Diesel PHEV
Petrol REEV
Diesel REEV
BEV
H2FC
H2FC PHEV
H2FC REEV
NGICE
0
20
40
60
80
100
120
140
160
180
2010 2020 2030 2040 2050
NetDirectgCO2/km(TC)
Passenger Car
Petrol ICE
Diesel ICE
Petrol HEV
Diesel HEV
Petrol PHEV
Diesel PHEV
Petrol REEV
Diesel REEV
BEV
H2FC
H2FC PHEV
H2FC REEV
NGICE
Target/Trajectory
14,000
16,000
18,000
20,000
22,000
24,000
26,000
28,000
30,000
2010 2020 2030 2040 2050
NetCost(£2010)
Passenger Car
Petrol ICE
Diesel ICE
Petrol HEV
Diesel HEV
Petrol PHEV
Diesel PHEV
Petrol REEV
Diesel REEV
BEV
H2FC
H2FC PHEV
H2FC REEV
NGICE
Notes: The ‘Target/Trajectory’ marks the 2020 gCO2/km regulatory targets, and a continued indicative
trajectory to 90% reduction in direct gCO2/km by 2050 relative to 2010.

Figure 6.4: Analysis results for Passenger Car Efficiency for 2020, 2030 and 2050
2.07
1.75 1.69
1.49
1.27 1.15 1.05 0.98
0.63
0.94 0.80 0.74
2.07
0.00
0.50
1.00
1.50
2.00
2.50
3.00
PetrolICE
DieselICE
PetrolHEV
DieselHEV
PetrolPHEV
DieselPHEV
PetrolREEV
DieselREEV
BEV
H2FC
H2FCPHEV
H2FCREEV
NGICE
Efficiency,MJ/km
Passenger Car (2020) (Real-World)
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Core technology
improvement
Overall vehicle
1.69
1.46 1.45 1.30 1.13 1.04 0.94 0.89
0.57
0.83 0.71 0.67
1.69
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
PetrolICE
DieselICE
PetrolHEV
DieselHEV
PetrolPHEV
DieselPHEV
PetrolREEV
DieselREEV
BEV
H2FC
H2FCPHEV
H2FCREEV
NGICE
Efficiency,MJ/km
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Core technology
improvement
Overall vehicle
1.38
1.20 1.21 1.09 0.99 0.93 0.83 0.79
0.51
0.70 0.61 0.58
1.38
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
PetrolICE
DieselICE
PetrolHEV
DieselHEV
PetrolPHEV
DieselPHEV
PetrolREEV
DieselREEV
BEV
H2FC
H2FCPHEV
H2FCREEV
NGICE
Efficiency,MJ/km
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Core technology
improvement
Overall vehicle
Notes: The coloured bars above the ‘Overall vehicle’ component represent the reductions in energy
consumption vs the base vehicle (chart total) due to technical efficiency improvements.

Figure 6.5: Analysis results for Passenger Car Capital Costs for 2020, 2030 and 2050
£0
£5,000
£10,000
£15,000
£20,000
£25,000
£30,000
£35,000
£40,000
£45,000
£50,000
PetrolICE
DieselICE
PetrolHEV
DieselHEV
PetrolPHEV
DieselPHEV
PetrolREEV
DieselREEV
BEV
H2FC
H2FCPHEV
H2FCREEV
NGICE
CapitalCost,£
Passenger Car (2020)
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Energy storage
Powertrain
Glider
£0
£5,000
£10,000
£15,000
£20,000
£25,000
PetrolICE
DieselICE
PetrolHEV
DieselHEV
PetrolPHEV
DieselPHEV
PetrolREEV
DieselREEV
BEV
H2FC
H2FCPHEV
H2FCREEV
NGICE
CapitalCost,£
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Energy storage
Powertrain
Glider
£0
£5,000
£10,000
£15,000
£20,000
£25,000
PetrolICE
DieselICE
PetrolHEV
DieselHEV
PetrolPHEV
DieselPHEV
PetrolREEV
DieselREEV
BEV
H2FC
H2FCPHEV
H2FCREEV
NGICE
CapitalCost,£
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Energy storage
Powertrain
Glider
Notes: Coloured bars above the ‘Glider’ component represent additional/marginal technology costs.

Figure 6.6: Analysis results for 2050 Passenger Car Capital Costs for Best, Low and High Cost
assumptions for key vehicle components
Best
Costs
£0
£5,000
£10,000
£15,000
£20,000
£25,000
PetrolICE
DieselICE
PetrolHEV
DieselHEV
PetrolPHEV
DieselPHEV
PetrolREEV
DieselREEV
BEV
H2FC
H2FCPHEV
H2FCREEV
NGICE
CapitalCost,£
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Energy storage
Powertrain
Glider
Low
Costs
£0
£5,000
£10,000
£15,000
£20,000
£25,000
PetrolICE
DieselICE
PetrolHEV
DieselHEV
PetrolPHEV
DieselPHEV
PetrolREEV
DieselREEV
BEV
H2FC
H2FCPHEV
H2FCREEV
NGICE
CapitalCost,£
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Energy storage
Powertrain
Glider
High
Costs
£0
£5,000
£10,000
£15,000
£20,000
£25,000
£30,000
£35,000
PetrolICE
DieselICE
PetrolHEV
DieselHEV
PetrolPHEV
DieselPHEV
PetrolREEV
DieselREEV
BEV
H2FC
H2FCPHEV
H2FCREEV
NGICE
CapitalCost,£
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Energy storage
Powertrain
Glider
Notes: The coloured bars above the ‘Glider’ component represent additional/marginal technology costs.

6.1.2 Vans
Figure 6.7 to Figure 6.10 provide a summary of the impacts of the assumptions on
deployment of efficiency improvements to vans on overall vehicle efficiency by powertrain
type, direct gCO2/km (for comparison with regulatory limit values for 2020), and net capital
cost increases attributed to the different technology areas.
The main key points to draw out of these charts under the best cost assumptions are:
i. Petrol vans have been characterised based on their average characteristics for the
new van fleet, which is significantly skewed towards smaller van categories and are
therefore not comparable with diesel van technologies. Natural gas, BEV and H2FC
technologies have also been sized to diesel vans, since petrol vans only comprise
<2% of the new van fleet.
ii. In general the trends observed for vans are similar to those already discussed for
passenger cars in the previous section. The main difference is the slower rate of
technological penetration assumed results in lesser reductions in overall vehicle
efficiency (23%-41%) when compared to cars (27%-50%).
6.1.3 Motorcycles
deployment of efficiency improvements to vans on overall vehicle efficiency by powertrain
type, lifecycle gCO2/km (for better comparison of net effects in the absence of regulatory
targets), and net capital cost increases attributed to the different technology areas.
The main key points to draw out of these charts under the best cost assumptions are:
i. The overall efficiency improvements and GHG reduction potential within the different
powertrain technologies between 2010 and 2050 are lower than for cars or vans (at
10%-36%). However, this may partly due to the lack of significant information about
efficiency improvement technologies for motorcycles in the available literature.
ii. BEV and HEV powertrain technologies appear to become close to cost-competitive
with ICE powertrains by 2030. However, H2FC motorcycles still have significantly
higher capital costs still by 2050.

Figure 6.7: Trajectory for Van Efficiency and Costs for Diesel ICE, PHEV and BEV
2.90
2.49
2.20
1.96 1.80
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
2010 2020 2030 2040 2050
Efficiency,MJ/km
Van / LCV (Diesel ICE) (Real-World)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Core
technology
improvement
Overall vehicle £0
£2,000
£4,000
£6,000
£8,000
£10,000
£12,000
£14,000
£16,000
2010 2020 2030 2040 2050
CapitalCost,£
Van / LCV (Diesel ICE)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Energy
storage
Powertrain
Glider
1.90
1.55 1.45 1.37 1.30
0.0
0.5
1.0
1.5
2.0
2.5
2010 2020 2030 2040 2050
Efficiency,MJ/km
Van / LCV (Diesel PHEV) (Real-World)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Core
technology
improvement
Overall vehicle £0
£2,000
£4,000
£6,000
£8,000
£10,000
£12,000
£14,000
£16,000
£18,000
£20,000
2010 2020 2030 2040 2050
CapitalCost,£
Van / LCV (Diesel PHEV)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Energy
storage
Powertrain
Glider
0.71
0.66
0.62 0.58 0.55
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
2010 2020 2030 2040 2050
Efficiency,MJ/km
Van / LCV (BEV) (Real-World)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Core
technology
improvement
Overall vehicle £0
£5,000
£10,000
£15,000
£20,000
£25,000
£30,000
2010 2020 2030 2040 2050
CapitalCost,£
Van / LCV (BEV)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Energy
storage
Powertrain
Glider

Figure 6.8: Trajectory for Van Efficiency, Direct gCO2/km and Cost by technology
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
2010 2020 2030 2040 2050
NetMJ/km(RW)
Van / LCV
Petrol ICE
Diesel ICE
Petrol HEV
Diesel HEV
Petrol PHEV
Diesel PHEV
Petrol REEV
Diesel REEV
BEV
H2FC
H2FC PHEV
H2FC REEV
NGICE
0
20
40
60
80
100
120
140
160
180
200
2010 2020 2030 2040 2050
NetDirectgCO2/km(TC)
Van / LCV
Petrol ICE
Diesel ICE
Petrol HEV
Diesel HEV
Petrol PHEV
Diesel PHEV
Petrol REEV
Diesel REEV
BEV
H2FC
H2FC PHEV
H2FC REEV
NGICE
Target/Trajectory
10,000
12,000
14,000
16,000
18,000
20,000
22,000
24,000
26,000
2010 2020 2030 2040 2050
NetCost(£2010)
Van / LCV
Petrol ICE
Diesel ICE
Petrol HEV
Diesel HEV
Petrol PHEV
Diesel PHEV
Petrol REEV
Diesel REEV
BEV
H2FC
H2FC PHEV
H2FC REEV
NGICE
Notes: The ‘Target/Trajectory’ marks the 2020 gCO2/km regulatory targets, and a continued indicative
trajectory to 70% reduction in direct gCO2/km by 2050 relative to 2010.

Figure 6.9: Analysis results for Van Efficiency for 2020, 2030 and 2050
2.33 2.49
1.85
2.16
1.36
1.55
1.13 1.25
0.66
1.25
0.98 0.87
2.73
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
PetrolICE
DieselICE
PetrolHEV
DieselHEV
PetrolPHEV
DieselPHEV
PetrolREEV
DieselREEV
BEV
H2FC
H2FCPHEV
H2FCREEV
NGICE
Efficiency,MJ/km
Van / LCV (2020) (Real-World)
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Core technology
improvement
Overall vehicle
2.06 2.20
1.69
1.97
1.28 1.45
1.06 1.18
0.62
1.14
0.90 0.81
2.42
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
PetrolICE
DieselICE
PetrolHEV
DieselHEV
PetrolPHEV
DieselPHEV
PetrolREEV
DieselREEV
BEV
H2FC
H2FCPHEV
H2FCREEV
NGICE
Efficiency,MJ/km
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Core technology
improvement
Overall vehicle
1.67 1.80
1.39
1.64
1.12 1.30
0.93 1.05
0.55
0.96 0.78 0.70
1.96
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
PetrolICE
DieselICE
PetrolHEV
DieselHEV
PetrolPHEV
DieselPHEV
PetrolREEV
DieselREEV
BEV
H2FC
H2FCPHEV
H2FCREEV
NGICE
Efficiency,MJ/km
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Core technology
improvement
Overall vehicle

Figure 6.10:Analysis results for Van Capital Costs for 2020, 2030 and 2050
£0
£5,000
£10,000
£15,000
£20,000
£25,000
£30,000
£35,000
£40,000
PetrolICE
DieselICE
PetrolHEV
DieselHEV
PetrolPHEV
DieselPHEV
PetrolREEV
DieselREEV
BEV
H2FC
H2FCPHEV
H2FCREEV
NGICE
CapitalCost,£
Van / LCV (2020)
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Energy storage
Powertrain
Glider
£0
£2,000
£4,000
£6,000
£8,000
£10,000
£12,000
£14,000
£16,000
£18,000
£20,000
PetrolICE
DieselICE
PetrolHEV
DieselHEV
PetrolPHEV
DieselPHEV
PetrolREEV
DieselREEV
BEV
H2FC
H2FCPHEV
H2FCREEV
NGICE
CapitalCost,£
Van / LCV (2030)
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Energy storage
Powertrain
Glider
£0
£2,000
£4,000
£6,000
£8,000
£10,000
£12,000
£14,000
£16,000
£18,000
£20,000
PetrolICE
DieselICE
PetrolHEV
DieselHEV
PetrolPHEV
DieselPHEV
PetrolREEV
DieselREEV
BEV
H2FC
H2FCPHEV
H2FCREEV
NGICE
CapitalCost,£
Van / LCV (2050)
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Energy storage
Powertrain
Glider

Figure 6.11:Trajectory for Motorcycle Efficiency and Costs for Petrol ICE, HEV and BEV
1.66
1.49
1.31
1.17
1.07
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
2010 2020 2030 2040 2050
Efficiency,MJ/km
Motorcycle or Moped (Petrol ICE) (Real-
World)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Core
technology
improvement
Overall vehicle £0
£1,000
£2,000
£3,000
£4,000
£5,000
£6,000
£7,000
£8,000
2010 2020 2030 2040 2050
CapitalCost,£
Motorcycle or Moped (Petrol ICE)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Energy storage
Powertrain
Glider
1.24
1.11
0.99
0.90
0.81
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
2010 2020 2030 2040 2050
Efficiency,MJ/km
Motorcycle or Moped (Petrol HEV) (Real-
World)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Core
technology
improvement
Overall vehicle £0
£1,000
£2,000
£3,000
£4,000
£5,000
£6,000
£7,000
£8,000
2010 2020 2030 2040 2050
CapitalCost,£
Motorcycle or Moped (Petrol HEV)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Energy storage
Powertrain
Glider
0.33 0.33 0.32 0.31 0.30
0.0
0.1
0.1
0.2
0.2
0.3
0.3
0.4
0.4
2010 2020 2030 2040 2050
Efficiency,MJ/km
Motorcycle or Moped (BEV) (Real-World)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Core
technology
improvement
Overall vehicle £0
£1,000
£2,000
£3,000
£4,000
£5,000
£6,000
£7,000
£8,000
£9,000
£10,000
2010 2020 2030 2040 2050
CapitalCost,£
Motorcycle or Moped (BEV)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Energy storage
Powertrain
Glider

Figure 6.12:Trajectory for Motorcycle Efficiency, Lifecycle gCO2/km and Cost by technology
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2010 2020 2030 2040 2050
NetMJ/km(RW)
Motorcycle or Moped
Petrol ICE
Petrol HEV
BEV
H2FC
0
20
40
60
80
100
120
140
160
2010 2020 2030 2040 2050
NetLifecyclegCO2e/km(RW)
Motorcycle or Moped
Petrol ICE
Petrol HEV
BEV
H2FC
6,000
7,000
8,000
9,000
10,000
11,000
12,000
2010 2020 2030 2040 2050
NetCost(£2010)
Motorcycle or Moped
Petrol ICE
Petrol HEV
BEV
H2FC
Notes: Lifecycle GHG calculated based on assumptions on the projected GHG intensity of fuels.

Figure 6.13:Analysis results for Motorcycle Efficiency for 2020, 2030 and 2050
1.49
1.11
0.33
0.69
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
1.80
Petrol
ICE
Petrol
HEV
BEV
H2FC
Efficiency,MJ/km
Motorcycle or Moped (2020) (Real-World)
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Core technology
improvement
Overall vehicle
1.31
0.99
0.32
0.65
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
1.80
Petrol
ICE
Petrol
HEV
BEV
H2FC
Efficiency,MJ/km
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Core technology
improvement
Overall vehicle
1.07
0.81
0.30
0.58
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
1.80
Petrol
ICE
Petrol
HEV
BEV
H2FC
Efficiency,MJ/km
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Core technology
improvement
Overall vehicle

Figure 6.14:Analysis results for Motorcycle Capital Costs for 2020, 2030 and 2050
£0
£5,000
£10,000
£15,000
£20,000
£25,000
Petrol
ICE
Petrol
HEV
BEV
H2FC
CapitalCost,£
Motorcycle or Moped (2020)
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Energy storage
Powertrain
Glider
£0
£2,000
£4,000
£6,000
£8,000
£10,000
£12,000
Petrol
ICE
Petrol
HEV
BEV
H2FC
CapitalCost,£
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Energy storage
Powertrain
Glider
£0
£1,000
£2,000
£3,000
£4,000
£5,000
£6,000
£7,000
£8,000
£9,000
£10,000
Petrol
ICE
Petrol
HEV
BEV
H2FC
CapitalCost,£
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Energy storage
Powertrain
Glider
Notes: The coloured bars above the ‘Glider’ component represent additional/marginal costs.

6.2 Heavy Duty Vehicles
6.2.1 Small Rigid Trucks
deployment of efficiency improvements to small rigid trucks on overall vehicle efficiency by
powertrain type, lifecycle gCO2/km (for better comparison of net effects in the absence of
regulatory targets), and net capital cost increases attributed to the different technology areas.
The main key points to draw out of these charts under the best case cost assumptions are:
i. The potential combined fuel consumption benefit of all technologies by 2050 is 16% -
28% depending on the powertrain. These benefits are predominantly due to
improvements in the powertrain with secondary benefits equally divided among rolling
resistance, and lightweighting, with only small contributions from aerodynamics and
other technologies. The greatest benefits are therefore achieved through switching
from conventional ICE to more efficient alternative powertrains.
ii. In contrast to light duty vehicles, purely in terms of capital costs, H2FC technology
appears to be a significantly lower cost ultra-low GHG technology in the longer term
than BEV, with capital costs similar to those of alternatives by 2050 (and potentially
below those of DNG ICE and NG ICE powertrains). Factoring in likely fuel costs into
the equation brings most technologies to overall cost levels similar to, or below those
of conventional diesel ICE by 2030. Estimated overall costs of BEVs are also below
those of H2FCs, however this assessment critically depends on both the relative
prices of hydrogen / electricity and the levels of taxes applied to different fuels in
future periods.
iii. DNG ICE trucks may offer a useful short-medium term alternative with similar net
GHG savings to hybrid powertrains (and better suited to mission profiles with greater
proportions of rural and highway km). However, the total capital costs are expected to
remain higher, partly due to the lack of anticipated reductions in natural gas storage
costs. Combination of DNG ICE with biomethane would, however, offer substantial
further GHG savings beyond those possible with Diesel HEVs. In the long term H2FC
offer greater GHG savings at similar capital costs.
6.2.2 Large Rigid Trucks
deployment of efficiency improvements to large rigid trucks on overall vehicle efficiency by
40% depending on the powertrain. The greatest benefits are due to improvements in
the powertrain and aerodynamics with lower levels of benefits due to rolling
resistance, lightweighting and other technologies.
ii. The capital cost premium of alternative technologies is expected to drop to within a
range of 11% by 2050, with savings in fuel consumption likely to outweigh differences
in capital costs versus Diesel ICE for all technologies except H2FC by 2020 and all by
2040 (depending on future fuel tax levels).
iii. DNG ICE powertrains appear to offer a cost-effective alternative (under current tax
levels) versus alternative powertrains with substantial lifecycle GHG savings in the
short-medium term, which could be further improved through the use of biomethane.
In the long term H2FC offer greater GHG savings at similar capital costs.

6.2.3 Articulated Trucks
deployment of efficiency improvements to articulated trucks on overall vehicle efficiency by
43% depending on the powertrain. As for rigid trucks, the greatest benefits are due to
improvements in the powertrain and aerodynamics, with lower levels of benefits due
to rolling resistance, lightweighting and other technologies.
range of just over 11% by 2050, with savings in fuel consumption likely to outweigh
differences in capital costs versus Diesel ICE for all technologies except H2FC by
2030 (depending on future fuel tax levels). FHV and HHV technologies appear to
offer only marginal cost reductions even by 2050, due to the lower level of savings
they offer for typical articulated truck mission profiles (i.e. long-haul).
6.2.4 Construction Trucks
deployment of efficiency improvements to construction trucks on overall vehicle efficiency by
36% depending on the powertrain. The greatest benefits are from due to
improvements in the powertrain, with slightly lower benefits equally divided among
rolling resistance, aerodynamics and other technologies. Lightweighting only provides
a very small contribution.
ii. Whilst construction trucks have lower potential for aerodynamic improvement benefits
versus large rigid and articulated trucks, those with electrified powertrains have a
greater additional potential for benefits in dual-mode operation (i.e. supporting non-
motive auxiliary loads from tipper mechanisms and other construction specific
equipment).
iii. The capital cost premium of alternative technologies is expected to drop to within a
range of 14% by 2050, with savings in fuel consumption likely to outweigh differences
in capital costs versus Diesel ICE for all technologies by 2030 (depending on future
fuel tax levels).
iv. DNG ICE powertrains appear to offer a cost-effective alternative (under current tax

6.2.5 Buses
deployment of efficiency improvements to buses on overall vehicle efficiency by powertrain
27% depending on the powertrain. These benefits are predominantly due to
improvements in the powertrain with lower benefits from weight reduction and from
rolling resistance. Aerodynamics and other technical options are not expected to
provide very significant contributions. The greatest benefits are therefore achieved
through switching from conventional ICE to more efficient alternative powertrains.
range of 5% by 2050 for all except BEVs (still 14% higher). Savings in fuel
consumption seem likely to outweigh differences in capital costs versus Diesel ICE
for all technologies by 2030 (depending on future fuel tax levels).
6.2.6 Coaches
deployment of efficiency improvements to coaches on overall vehicle efficiency by powertrain
35% depending on the powertrain. The greatest benefits are due to improvements in
the powertrain, aerodynamics and rolling resistance, and with lower levels of benefits
due to lightweighting and other technologies.
range of ~6% by 2050. Savings in fuel consumption seem likely to outweigh
differences in capital costs versus Diesel ICE for all technologies by 2030 (depending
on future fuel tax levels).

Figure 6.15:Trajectory for Small Rigid Truck Efficiency and Costs for Diesel ICE, HEV and H2FC
9.38
8.86
8.17
7.66 7.37
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
2010 2020 2030 2040 2050
Efficiency,MJ/km
Small Rigid Truck (Diesel ICE) (Real-World)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Core
technology
improvement
Overall vehicle £0
£5,000
£10,000
£15,000
£20,000
£25,000
£30,000
£35,000
£40,000
£45,000
2010 2020 2030 2040 2050
CapitalCost,£
Small Rigid Truck (Diesel ICE)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Energy storage
Powertrain
Glider
7.56 7.61
7.01 6.64 6.33
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
2010 2020 2030 2040 2050
Efficiency,MJ/km
Small Rigid Truck (Diesel HEV) (Real-World)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Core
technology
improvement
Overall vehicle £0
£5,000
£10,000
£15,000
£20,000
£25,000
£30,000
£35,000
£40,000
£45,000
2010 2020 2030 2040 2050
CapitalCost,£
Small Rigid Truck (Diesel HEV)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Energy storage
Powertrain
Glider
4.42
4.14
3.79
3.47
3.19
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
2010 2020 2030 2040 2050
Efficiency,MJ/km
Small Rigid Truck (H2FC) (Real-World)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Core
technology
improvement
Overall vehicle £0
£20,000
£40,000
£60,000
£80,000
£100,000
£120,000
£140,000
£160,000
£180,000
£200,000
2010 2020 2030 2040 2050
CapitalCost,£
Small Rigid Truck (H2FC)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Energy storage
Powertrain
Glider

Figure 6.16:Trajectory for Small Rigid Truck Efficiency, Lifecycle gCO2/km and Cost by
technology
0
2
4
6
8
10
12
2010 2020 2030 2040 2050
NetMJ/km(RW)
Small Rigid Truck
Diesel ICE
Diesel FHV
Diesel HHV
Diesel HEV
BEV
H2FC
DNG ICE
NG ICE
0
100
200
300
400
500
600
700
800
900
2010 2020 2030 2040 2050
Small Rigid Truck
Diesel ICE
Diesel FHV
Diesel HHV
Diesel HEV
BEV
H2FC
DNG ICE
NG ICE
25,000
30,000
35,000
40,000
45,000
50,000
55,000
60,000
65,000
70,000
75,000
2010 2020 2030 2040 2050
NetCost(£2010)
Small Rigid Truck
Diesel ICE
Diesel FHV
Diesel HHV
Diesel HEV
BEV
H2FC
DNG ICE
NG ICE
Notes: Lifecycle GHG calculated based on assumptions on the projected GHG intensity of fuels.

Figure 6.17:Analysis results for Small Rigid Truck Efficiency for 2020, 2030 and 2050
8.86 8.07 8.55
7.61
2.78
4.14
8.86
10.19
-2.00
0.00
2.00
4.00
6.00
8.00
10.00
12.00
DieselICE
DieselFHV
DieselHHV
DieselHEV
BEV
H2FC
DNGICE
NGICE
Efficiency,MJ/km
Small Rigid Truck (2020) (Real-World)
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Core technology
improvement
Overall vehicle
8.17
7.43 7.81
7.01
2.62
3.79
8.17
9.39
0.00
2.00
4.00
6.00
8.00
10.00
12.00
Diesel
ICE
Diesel
FHV
Diesel
HHV
Diesel
HEV
BEV
H2FC
DNG
ICE
NGICE
Efficiency,MJ/km
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Core technology
improvement
Overall vehicle
7.37 6.70 6.98 6.33
2.34
3.19
7.37
8.47
0.00
2.00
4.00
6.00
8.00
10.00
12.00
Diesel
ICE
Diesel
FHV
Diesel
HHV
Diesel
HEV
BEV
H2FC
DNG
ICE
NGICE
Efficiency,MJ/km
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Core technology
improvement
Overall vehicle

Figure 6.18:Analysis results for Small Rigid Truck Capital Costs for 2020, 2030 and 2050
£0
£10,000
£20,000
£30,000
£40,000
£50,000
£60,000
£70,000
£80,000
Diesel
ICE
Diesel
FHV
Diesel
HHV
Diesel
HEV
BEV
H2FC
DNG
ICE
NGICE
CapitalCost,£
Small Rigid Truck (2020)
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Energy storage
Powertrain
Glider
£0
£10,000
£20,000
£30,000
£40,000
£50,000
£60,000
Diesel
ICE
Diesel
FHV
Diesel
HHV
Diesel
HEV
BEV
H2FC
DNG
ICE
NGICE
CapitalCost,£
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Energy storage
Powertrain
Glider
£0
£10,000
£20,000
£30,000
£40,000
£50,000
£60,000
Diesel
ICE
Diesel
FHV
Diesel
HHV
Diesel
HEV
BEV
H2FC
DNG
ICE
NGICE
CapitalCost,£
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Energy storage
Powertrain
Glider

Figure 6.19:Trajectory for Large Rigid Truck Efficiency and Costs for Diesel ICE, HEV and H2FC
12.41
11.08
9.27
8.55 8.04
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
2010 2020 2030 2040 2050
Efficiency,MJ/km
Large Rigid Truck (Diesel ICE) (Real-World)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Core
technology
improvement
Overall vehicle £0
£10,000
£20,000
£30,000
£40,000
£50,000
£60,000
£70,000
2010 2020 2030 2040 2050
CapitalCost,£
Large Rigid Truck (Diesel ICE)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Energy
storage
Powertrain
Glider
11.28
10.25
8.49
7.69 7.17
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
2010 2020 2030 2040 2050
Efficiency,MJ/km
Large Rigid Truck (Diesel HEV) (Real-
World)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Core
technology
improvement
Overall vehicle £0
£10,000
£20,000
£30,000
£40,000
£50,000
£60,000
£70,000
2010 2020 2030 2040 2050
CapitalCost,£
Large Rigid Truck (Diesel HEV)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Energy
storage
Powertrain
Glider
5.88
5.14
4.39
3.86
3.51
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
2010 2020 2030 2040 2050
Efficiency,MJ/km
Large Rigid Truck (H2FC) (Real-World)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Core
technology
improvement
Overall vehicle £0
£50,000
£100,000
£150,000
£200,000
£250,000
£300,000
2010 2020 2030 2040 2050
CapitalCost,£
Large Rigid Truck (H2FC)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Energy
storage
Powertrain
Glider

Figure 6.20:Analysis results for Large Rigid Truck Efficiency, Lifecycle gCO2/km and Cost by
technology
0
2
4
6
8
10
12
14
16
2010 2020 2030 2040 2050
NetMJ/km(RW)
Large Rigid Truck
Diesel ICE
Diesel FHV
Diesel HHV
Diesel HEV
H2FC
DNG ICE
NG ICE
0
200
400
600
800
1,000
1,200
2010 2020 2030 2040 2050
Large Rigid Truck
Diesel ICE
Diesel FHV
Diesel HHV
Diesel HEV
H2FC
DNG ICE
NG ICE
45,000
50,000
55,000
60,000
65,000
70,000
75,000
80,000
85,000
2010 2020 2030 2040 2050
NetCost(£2010)
Large Rigid Truck
Diesel ICE
Diesel FHV
Diesel HHV
Diesel HEV
H2FC
DNG ICE
NG ICE

Figure 6.21:Analysis results for Large Rigid Truck Efficiencies for 2020, 2030 and 2050
11.08 10.67 10.88 10.25
5.14
11.08
12.74
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
Diesel
ICE
Diesel
FHV
Diesel
HHV
Diesel
HEV
H2FC
DNG
ICE
NGICE
Efficiency,MJ/km
Large Rigid Truck (2020) (Real-World)
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Core technology
improvement
Overall vehicle
9.27 8.93 9.03 8.49
4.39
9.27
10.66
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
Diesel
ICE
Diesel
FHV
Diesel
HHV
Diesel
HEV
H2FC
DNG
ICE
NGICE
Efficiency,MJ/km
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Core technology
improvement
Overall vehicle
8.04 7.73 7.69 7.17
3.51
8.04
9.24
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
Diesel
ICE
Diesel
FHV
Diesel
HHV
Diesel
HEV
H2FC
DNG
ICE
NGICE
Efficiency,MJ/km
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Core technology
improvement
Overall vehicle

Figure 6.22:Analysis results for Large Rigid Truck Capital Costs for 2020, 2030 and 2050
£0
£20,000
£40,000
£60,000
£80,000
£100,000
£120,000
£140,000
Diesel
ICE
Diesel
FHV
Diesel
HHV
Diesel
HEV
H2FC
DNG
ICE
NGICE
CapitalCost,£
Large Rigid Truck (2020)
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Energy storage
Powertrain
Glider
£0
£10,000
£20,000
£30,000
£40,000
£50,000
£60,000
£70,000
£80,000
Diesel
ICE
Diesel
FHV
Diesel
HHV
Diesel
HEV
H2FC
DNG
ICE
NGICE
CapitalCost,£
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Energy storage
Powertrain
Glider
£0
£10,000
£20,000
£30,000
£40,000
£50,000
£60,000
£70,000
£80,000
Diesel
ICE
Diesel
FHV
Diesel
HHV
Diesel
HEV
H2FC
DNG
ICE
NGICE
CapitalCost,£
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Energy storage
Powertrain
Glider

Figure 6.23:Trajectory for Articulated Truck Efficiency and Costs for Diesel ICE, HEV and H2FC
13.99
11.82
9.41
8.51 7.91
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
2010 2020 2030 2040 2050
Efficiency,MJ/km
Articulated Truck (Diesel ICE) (Real-World)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Core
technology
improvement
Overall vehicle £0
£10,000
£20,000
£30,000
£40,000
£50,000
£60,000
£70,000
£80,000
£90,000
2010 2020 2030 2040 2050
CapitalCost,£
Articulated Truck (Diesel ICE)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Energy
storage
Powertrain
Glider
13.15
11.27
8.93
8.05 7.40
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
2010 2020 2030 2040 2050
Efficiency,MJ/km
Articulated Truck (Diesel HEV) (Real-World)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Core
technology
improvement
Overall vehicle £0
£10,000
£20,000
£30,000
£40,000
£50,000
£60,000
£70,000
£80,000
£90,000
2010 2020 2030 2040 2050
CapitalCost,£ Articulated Truck (Diesel HEV)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Energy
storage
Powertrain
Glider
6.64
5.62
4.70
4.18
3.77
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
2010 2020 2030 2040 2050
Efficiency,MJ/km
Articulated Truck (H2FC) (Real-World)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Core
technology
improvement
Overall vehicle £0
£50,000
£100,000
£150,000
£200,000
£250,000
£300,000
£350,000
£400,000
£450,000
2010 2020 2030 2040 2050
CapitalCost,£
Articulated Truck (H2FC)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Energy
storage
Powertrain
Glider

Figure 6.24:Analysis results for Articulated Truck Efficiency, Lifecycle gCO2/km and Cost by
technology
0
2
4
6
8
10
12
14
16
18
2010 2020 2030 2040 2050
NetMJ/km(RW)
Articulated Truck
Diesel ICE
Diesel FHV
Diesel HHV
Diesel HEV
H2FC
DNG ICE
NG ICE
0
200
400
600
800
1,000
1,200
1,400
2010 2020 2030 2040 2050
Articulated Truck
Diesel ICE
Diesel FHV
Diesel HHV
Diesel HEV
H2FC
DNG ICE
NG ICE
60,000
65,000
70,000
75,000
80,000
85,000
90,000
95,000
100,000
2010 2020 2030 2040 2050
NetCost(£2010)
Articulated Truck
Diesel ICE
Diesel FHV
Diesel HHV
Diesel HEV
H2FC
DNG ICE
NG ICE

Figure 6.25:Analysis results for Articulated Truck Efficiency for 2020, 2030 and 2050
11.82 11.50 11.61 11.27
5.62
11.82
13.60
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
18.00
Diesel
ICE
Diesel
FHV
Diesel
HHV
Diesel
HEV
H2FC
DNG
ICE
NGICE
Efficiency,MJ/km
Articulated Truck (2020) (Real-World)
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Core technology
improvement
Overall vehicle
9.41 9.24 9.26 8.93
4.70
9.41
10.82
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
18.00
Diesel
ICE
Diesel
FHV
Diesel
HHV
Diesel
HEV
H2FC
DNG
ICE
NGICE
Efficiency,MJ/km
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Core technology
improvement
Overall vehicle
7.91 7.84 7.80 7.40
3.77
7.91
9.10
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
18.00
Diesel
ICE
Diesel
FHV
Diesel
HHV
Diesel
HEV
H2FC
DNG
ICE
NGICE
Efficiency,MJ/km
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Core technology
improvement
Overall vehicle

Figure 6.26:Analysis results for Articulated Truck Capital Costs for 2020, 2030 and 2050
£0
£20,000
£40,000
£60,000
£80,000
£100,000
£120,000
£140,000
£160,000
£180,000
Diesel
ICE
Diesel
FHV
Diesel
HHV
Diesel
HEV
H2FC
DNG
ICE
NGICE
CapitalCost,£
Articulated Truck (2020)
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Energy storage
Powertrain
Glider
£0
£10,000
£20,000
£30,000
£40,000
£50,000
£60,000
£70,000
£80,000
£90,000
£100,000
Diesel
ICE
Diesel
FHV
Diesel
HHV
Diesel
HEV
H2FC
DNG
ICE
NGICE
CapitalCost,£
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Energy storage
Powertrain
Glider
£0
£10,000
£20,000
£30,000
£40,000
£50,000
£60,000
£70,000
£80,000
£90,000
£100,000
Diesel
ICE
Diesel
FHV
Diesel
HHV
Diesel
HEV
H2FC
DNG
ICE
NGICE
CapitalCost,£
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Energy storage
Powertrain
Glider

Figure 6.27:Trajectory for Construction Truck Efficiency and Costs for Diesel ICE, HEV and
H2FC
13.16
12.08
11.08
10.40 9.89
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
2010 2020 2030 2040 2050
Efficiency,MJ/km
Construction Truck (Diesel ICE) (Real-
World)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Core
technology
improvement
Overall vehicle £0
£10,000
£20,000
£30,000
£40,000
£50,000
£60,000
£70,000
£80,000
2010 2020 2030 2040 2050
CapitalCost,£
Construction Truck (Diesel ICE)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Energy storage
Powertrain
Glider
12.16 11.39
10.37
9.51 8.73
-2.0
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
2010 2020 2030 2040 2050
Efficiency,MJ/km
Construction Truck (Diesel HEV) (Real-
World)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Core
technology
improvement
Overall vehicle £0
£10,000
£20,000
£30,000
£40,000
£50,000
£60,000
£70,000
£80,000
2010 2020 2030 2040 2050
CapitalCost,£
Construction Truck (Diesel HEV)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Energy storage
Powertrain
Glider
6.24
5.46
4.97
4.43
3.97
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
2010 2020 2030 2040 2050
Efficiency,MJ/km
Construction Truck (H2FC) (Real-World)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Core
technology
improvement
Overall vehicle £0
£50,000
£100,000
£150,000
£200,000
£250,000
£300,000
2010 2020 2030 2040 2050
CapitalCost,£
Construction Truck (H2FC)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Energy storage
Powertrain
Glider

Figure 6.28:Analysis results for Construction Truck Efficiency, Lifecycle gCO2/km and Cost by
technology
0
2
4
6
8
10
12
14
16
2010 2020 2030 2040 2050
NetMJ/km(RW)
Construction Truck
Diesel ICE
Diesel FHV
Diesel HHV
Diesel HEV
H2FC
DNG ICE
NG ICE
0
200
400
600
800
1,000
1,200
1,400
2010 2020 2030 2040 2050
Construction Truck
Diesel ICE
Diesel FHV
Diesel HHV
Diesel HEV
H2FC
DNG ICE
NG ICE
50,000
55,000
60,000
65,000
70,000
75,000
80,000
85,000
90,000
2010 2020 2030 2040 2050
NetCost(£2010)
Construction Truck
Diesel ICE
Diesel FHV
Diesel HHV
Diesel HEV
H2FC
DNG ICE
NG ICE

Figure 6.29:Analysis results for Construction Truck Efficiency for 2020, 2030 and 2050
12.08 11.69 11.90 11.39
5.46
12.08
13.90
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
Diesel
ICE
Diesel
FHV
Diesel
HHV
Diesel
HEV
H2FC
DNG
ICE
NGICE
Efficiency,MJ/km
Construction Truck (2020) (Real-World)
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Core technology
improvement
Overall vehicle
11.08 10.73 10.88 10.37
4.97
11.08
12.75
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
Diesel
ICE
Diesel
FHV
Diesel
HHV
Diesel
HEV
H2FC
DNG
ICE
NGICE
Efficiency,MJ/km
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Core technology
improvement
Overall vehicle
9.89 9.59 9.23 8.73
3.97
9.89
11.38
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
18.00
Diesel
ICE
Diesel
FHV
Diesel
HHV
Diesel
HEV
H2FC
DNG
ICE
NGICE
Efficiency,MJ/km
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Core technology
improvement
Overall vehicle

Figure 6.30:Analysis results for Construction Truck Capital Costs for 2020, 2030 and 2050
£0
£20,000
£40,000
£60,000
£80,000
£100,000
£120,000
£140,000
Diesel
ICE
Diesel
FHV
Diesel
HHV
Diesel
HEV
H2FC
DNG
ICE
NGICE
CapitalCost,£
Construction Truck (2020)
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Energy storage
Powertrain
Glider
£0
£10,000
£20,000
£30,000
£40,000
£50,000
£60,000
£70,000
£80,000
Diesel
ICE
Diesel
FHV
Diesel
HHV
Diesel
HEV
H2FC
DNG
ICE
NGICE
CapitalCost,£
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Energy storage
Powertrain
Glider
£0
£10,000
£20,000
£30,000
£40,000
£50,000
£60,000
£70,000
£80,000
Diesel
ICE
Diesel
FHV
Diesel
HHV
Diesel
HEV
H2FC
DNG
ICE
NGICE
CapitalCost,£
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Energy storage
Powertrain
Glider

Figure 6.31:Trajectory for Bus Efficiency and Costs for Diesel ICE, HEV and H2FC
13.99
13.19
11.73 11.22 10.79
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
2010 2020 2030 2040 2050
Efficiency,MJ/km
Bus (Diesel ICE) (Real-World)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Core
technology
improvement
Overall vehicle £0
£20,000
£40,000
£60,000
£80,000
£100,000
£120,000
£140,000
2010 2020 2030 2040 2050
CapitalCost,£
Bus (Diesel ICE)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Energy storage
Powertrain
Glider
9.90 9.74
8.85 8.49 8.10
-2.0
0.0
2.0
4.0
6.0
8.0
10.0
12.0
2010 2020 2030 2040 2050
Efficiency,MJ/km
Bus (Diesel HEV) (Real-World)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Core
technology
improvement
Overall vehicle £0
£20,000
£40,000
£60,000
£80,000
£100,000
£120,000
£140,000
2010 2020 2030 2040 2050
CapitalCost,£
Bus (Diesel HEV)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Energy storage
Powertrain
Glider
6.63
6.20
5.69
5.27
4.87
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
2010 2020 2030 2040 2050
Efficiency,MJ/km
Bus (H2FC) (Real-World)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Core
technology
improvement
Overall vehicle £0
£50,000
£100,000
£150,000
£200,000
£250,000
£300,000
2010 2020 2030 2040 2050
CapitalCost,£
Bus (H2FC)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Energy storage
Powertrain
Glider

Figure 6.32:Analysis results for Bus Efficiency, Lifecycle gCO2/km and Cost by technology
0
2
4
6
8
10
12
14
16
18
2010 2020 2030 2040 2050
NetMJ/km(RW)
Bus
Diesel ICE
Diesel FHV
Diesel HHV
Diesel HEV
BEV
H2FC
DNG ICE
NG ICE
0
200
400
600
800
1,000
1,200
1,400
2010 2020 2030 2040 2050
Bus
Diesel ICE
Diesel FHV
Diesel HHV
Diesel HEV
BEV
H2FC
DNG ICE
NG ICE
100,000
105,000
110,000
115,000
120,000
125,000
130,000
135,000
140,000
145,000
2010 2020 2030 2040 2050
NetCost(£2010)
Bus
Diesel ICE
Diesel FHV
Diesel HHV
Diesel HEV
BEV
H2FC
DNG ICE
NG ICE

Figure 6.33:Analysis results for Bus Efficiency for 2020, 2030 and 2050
13.19
11.05 11.74
9.74
4.15
6.20
13.19
15.17
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
18.00
Diesel
ICE
Diesel
FHV
Diesel
HHV
Diesel
HEV
BEV
H2FC
DNG
ICE
NGICE
Efficiency,MJ/km
Bus (2020) (Real-World)
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Core technology
improvement
Overall vehicle
11.73
9.87 10.49
8.85
3.94
5.69
11.73
13.50
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
18.00
Diesel
ICE
Diesel
FHV
Diesel
HHV
Diesel
HEV
BEV
H2FC
DNG
ICE
NGICE
Efficiency,MJ/km
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Core technology
improvement
Overall vehicle
10.79
9.07 9.64
8.10
3.57
4.87
10.79
12.41
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
18.00
Diesel
ICE
Diesel
FHV
Diesel
HHV
Diesel
HEV
BEV
H2FC
DNG
ICE
NGICE
Efficiency,MJ/km
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Core technology
improvement
Overall vehicle

Figure 6.34:Analysis results for Bus Capital Costs for 2020, 2030 and 2050
£0
£20,000
£40,000
£60,000
£80,000
£100,000
£120,000
£140,000
£160,000
£180,000
Diesel
ICE
Diesel
FHV
Diesel
HHV
Diesel
HEV
BEV
H2FC
DNG
ICE
NGICE
CapitalCost,£
Bus (2020)
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Energy storage
Powertrain
Glider
£0
£20,000
£40,000
£60,000
£80,000
£100,000
£120,000
£140,000
£160,000
Diesel
ICE
Diesel
FHV
Diesel
HHV
Diesel
HEV
BEV
H2FC
DNG
ICE
NGICE
CapitalCost,£
Bus (2030)
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Energy storage
Powertrain
Glider
£0
£20,000
£40,000
£60,000
£80,000
£100,000
£120,000
£140,000
£160,000
Diesel
ICE
Diesel
FHV
Diesel
HHV
Diesel
HEV
BEV
H2FC
DNG
ICE
NGICE
CapitalCost,£
Bus (2050)
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Energy storage
Powertrain
Glider

Figure 6.35:Trajectory for Coach Efficiency and Costs for Diesel ICE, HEV and H2FC
13.83
12.65
11.15
10.25
9.38
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
2010 2020 2030 2040 2050
Efficiency,MJ/km
Coach (Diesel ICE) (Real-World)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Core
technology
improvement
Overall vehicle £0
£20,000
£40,000
£60,000
£80,000
£100,000
£120,000
£140,000
2010 2020 2030 2040 2050
CapitalCost,£
Coach (Diesel ICE)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Energy
storage
Powertrain
Glider
12.58
11.80
10.37
9.55
8.69
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
2010 2020 2030 2040 2050
Efficiency,MJ/km
Coach (Diesel HEV) (Real-World)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Core
technology
improvement
Overall vehicle £0
£20,000
£40,000
£60,000
£80,000
£100,000
£120,000
£140,000
2010 2020 2030 2040 2050
CapitalCost,£
Coach (Diesel HEV)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Energy
storage
Powertrain
Glider
6.56
5.91
5.36
4.79
4.24
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
2010 2020 2030 2040 2050
Efficiency,MJ/km
Coach (H2FC) (Real-World)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Core
technology
improvement
Overall vehicle £0
£50,000
£100,000
£150,000
£200,000
£250,000
£300,000
£350,000
2010 2020 2030 2040 2050
CapitalCost,£
Coach (H2FC)
Other options
Vehicle weight
Rolling
resistance
Aerodynamics
Powertrain
efficiency
Energy
storage
Powertrain
Glider

Figure 6.36:Analysis results for Coach Efficiency, Lifecycle gCO2/km and Cost by technology
0
2
4
6
8
10
12
14
16
18
2010 2020 2030 2040 2050
NetMJ/km(RW)
Coach
Diesel ICE
Diesel FHV
Diesel HHV
Diesel HEV
H2FC
DNG ICE
NG ICE
0
200
400
600
800
1,000
1,200
1,400
2010 2020 2030 2040 2050
Coach
Diesel ICE
Diesel FHV
Diesel HHV
Diesel HEV
H2FC
DNG ICE
NG ICE
100,000
105,000
110,000
115,000
120,000
125,000
130,000
135,000
140,000
145,000
2010 2020 2030 2040 2050
NetCost(£2010)
Coach
Diesel ICE
Diesel FHV
Diesel HHV
Diesel HEV
H2FC
DNG ICE
NG ICE

Figure 6.37:Analysis results for Coach Efficiency for 2020, 2030 and 2050
12.65 12.18 12.42 11.80
5.91
12.65
14.54
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
18.00
Diesel
ICE
Diesel
FHV
Diesel
HHV
Diesel
HEV
H2FC
DNG
ICE
NGICE
Efficiency,MJ/km
Coach (2020) (Real-World)
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Core technology
improvement
Overall vehicle
11.15 10.74 10.95 10.37
5.36
11.15
12.83
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
18.00
Diesel
ICE
Diesel
FHV
Diesel
HHV
Diesel
HEV
H2FC
DNG
ICE
NGICE
Efficiency,MJ/km
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Core technology
improvement
Overall vehicle
9.38 9.03 9.21 8.69
4.24
9.38
10.79
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
18.00
Diesel
ICE
Diesel
FHV
Diesel
HHV
Diesel
HEV
H2FC
DNG
ICE
NGICE
Efficiency,MJ/km
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Core technology
improvement
Overall vehicle

Figure 6.38:Analysis results for Coach Capital Costs for 2020, 2030 and 2050
£0
£20,000
£40,000
£60,000
£80,000
£100,000
£120,000
£140,000
£160,000
£180,000
Diesel
ICE
Diesel
FHV
Diesel
HHV
Diesel
HEV
H2FC
DNG
ICE
NGICE
CapitalCost,£
Coach (2020)
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Energy storage
Powertrain
Glider
£0
£20,000
£40,000
£60,000
£80,000
£100,000
£120,000
£140,000
Diesel
ICE
Diesel
FHV
Diesel
HHV
Diesel
HEV
H2FC
DNG
ICE
NGICE
CapitalCost,£
Coach (2030)
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Energy storage
Powertrain
Glider
£0
£20,000
£40,000
£60,000
£80,000
£100,000
£120,000
£140,000
Diesel
ICE
Diesel
FHV
Diesel
HHV
Diesel
HEV
H2FC
DNG
ICE
NGICE
CapitalCost,£
Coach (2050)
Other options
Vehicle weight
Rolling resistance
Aerodynamics
Powertrain
efficiency
Energy storage
Powertrain
Glider

7 Evaluation of CCC’s Default
Trajectory Assumptions
Objectives:
The purpose of Task 4 was to: “advise on the validity of the CCC’s default assumption that
in the absence of any government policy to reduce GHG emissions (including existing new
vehicle CO2 regulations), there would be no or minimal change to the fuel efficiency and
capital costs of the dominant vehicle technologies within each vehicle category.”
For passenger cars there is not sufficiently strong evidence to suggest that the
assumption of a flat counterfactual is incorrect and that the CCC should therefore
continue to use this assumption in its modelling work.
For van/light commercial vehicle efficiency there is some evidence to suggest that the
assumption of a flat counterfactual is not valid for vans and it may be more appropriate
for CCC to revise this assumption in its modelling work to reflect a gradual rate of
annual improvement in van efficiency.
For heavy duty truck efficiency there is good evidence to suggest that the assumption
of a flat counterfactual is incorrect for specific sizes of heavy trucks. However, the
general trend of increasing vehicle sizing (presumably in a drive to increase operational
efficiency on a tonne-km basis) means that the fleet as a whole has a trend to
increasing MPG. CCC may therefore wish revise these elements into its modelling
work to reflect annual increases in heavy truck efficiency, but factoring in changes in
relative vehicle sizing affecting actual energy consumption per km.
For buses and coaches there some evidence to suggest that the assumption of a flat
counterfactual for bus and coach efficiency is incorrect and that the CCC should
therefore consider revising this assumption in its modelling work.
For the capital costs of vans, trucks, busses and coaches, there is not sufficiently
strong evidence to suggest that the assumption of a flat counterfactual is incorrect and
that the CCC should therefore continue to use this assumption in its modelling work for
the capital costs of other vehicles.
For motorcycles and mopeds, no evidence has been identified to suggest a change in
the current assumption.
7.1 Assumptions and scope
The objectives above are focussed on fuel efficiency and the capital costs of vehicles – they
do not extend to the GHG emissions from all of road transport. Therefore the following
factors are excluded from this analysis:
The use of (and capability of vehicles to use) biofuels;
The use of alternative fuels;
Changes in vehicle km driven and driving style;
Modal shifts.

The question being addressed is what would happen going forward if GHG policy was
removed today, regardless of past policy – would new vehicle CO2 get worse, stay the same,
or get better, and by how much.
7.2 Passenger Cars
7.2.1 Context for ‘counterfactual’ assessment
To assess what the ‘no policy’ counterfactual would be for cars, it is important to understand
whether policies are having, or have in the past had, an effect on new car fuel efficiency and
capital costs of key vehicle technologies. It is also imperative to know whether other factors -
such as market conditions (fuel and resource prices), consumer preference and technology -
impact on new car fuel efficiency and capital costs of key vehicle technologies.
A systematic review of recent literature was undertaken to verify the ‘counterfactual’ case by
assessing whether there is any evidence on the:
Impact of climate change policies on the environmental and energy performance of cars;
Impact of climate change policies on vehicle costs and end-user prices; and
Impact of other exogenous factors – oil prices, income levels, technology and consumer
preference – on car fuel efficiency.
The key point for testing the above three propositions is that even though a number of
technologies, economic signals and consumer behaviours stem from regulation, these
exogenous factors can still impact on business and technical strategies of the automotive
sector (Figure 7.1). Hence, to separate out the impact of policies, it is also important to look
at exogenous factors and the extent to which have they been important in the past, as well
as what could happen to them going forward.
Figure 7.1: Number of factors impact on fuel efficiency improvements
Policy
• Air pollutant regulation
• CO2 regulation
• Noise regulation
• Safety standards
• Fuel quality & biofuel content
• Vehicle standardisation
• End of life material treatment
• Roadmaps & broader policy
• Intellectual property
Consumer
preferences
• Purchase price
• Brand differentiation
• Technical performance
• Comfort
• Design
• Transport demand
• Procurement
Economic
• Overcapacity
• Competition
• Cost
• Global platforms
• Changes in supply chains
• Employment
• Risk
• Resource scarcity
• R&D incentives & subsidies
• Wider economy
Technology
• New powertrains
• Electrification
• ICT integration
• Resource scarcity
• Cross-fertilization with other
industries
R&D

This study only considers policies designed with climate change in mind, so it excludes
policies such as fuel duty, which have an effect on emissions but are not primarily designed
to tackle climate change. The policies that currently affect, or have in the past affected, new
car CO2 emissions in UK are given in Box 7.1.
Box 7.1: UK new car CO2 legislation
The EU new car CO2 regulation – agreed late 2008 and sets a target, based on the
average mass of the vehicles sold, for manufacturers to reduce their average new car
CO2 emissions for all sales by 2015, such that overall new car CO2 emissions will fall to
130 g/km. It also suggested an EU wide target of 95 g/km by 2020.
Graduated VED – introduced in March 2001, with a reclassification in April 2009, with the
highest set at £455 per year and the lowest at £0. From 2010, a new first year rate was
introduced, ranging from £0 for vehicles in the lower bands, up to £950 for vehicles in the
highest band.
Voluntary Agreements – introduced from 1998/99 and required average new car CO2
from European, Japanese and Korean manufacturers to be reduced to 140 g/km by 2008
(Europe) and 2009 (Japan and Korea).
Company car tax - since April 2002 company car tax has been based on a car's list price
and official CO2 emissions figure. Various changes were also made in later budgets, e.g.
from 2011 tax rates for company cars that produce more than 129g/km will increase by
1% for every 5g per km increase in CO2 emissions, to a maximum of 35%. These rules
also exempt ultra-low emissions cars and allow businesses 100% first year write-down
when purchasing ultra-low emissions cars.
7.2.2 Impact of climate change policies on CO2 emissions and fuel efficiency
of cars
There is evidence to show that climate change legislation has led to improvement in vehicle
fuel efficiency or increased the pace of the improvement. Between 2000 and 2010, average
new car CO2 emissions of new cars in the UK fell to ~145 gCO2/km, 3.5% below 2009 levels
and 20% below 2000 levels (Figure 7.2).
Figure 7.2: Fall in new car CO2 in the UK since 2000
EU new car CO2
regulation
Graduated VED
Graduated VED
reclassification
Company car tax
Source: SMMT new car CO2 report 2011

Much of this improvement was driven by the increasing market share of diesel cars, which
took a record 46% market share in 2010. This is up significantly from 14% in 2000 and 42%
in 2009 (Figure 7.3).
Figure 7.3: Share of diesel in the UK, 2000-2010
Source: SMMT new car CO2 report 2011
A similar pattern in new car CO2 can be observed at the EU level, with the European
Commission confirming in December 2011 that average CO2 emissions of new cars across
Europe had fallen 3.7% from 2009 levels (Figure 7.4).
Figure 7.4: New car CO2 levels in the EU from 2000 to 2010.
Source: EEA 2011

However, in order to assess whether the introduction of the voluntary agreements had a
discernible effect on new car fuel efficiency it is important to review trends prior to 2000.
Publicly available figures for g/km CO2 emissions of new cars are not available for before
2000 as it was only at this time that the European Commission brought forward Decision No
1753/2000/EC, establishing a scheme to monitor the average specific CO2 emissions of new
cars. However, data on fuel efficiency going back further than 2000 does show continuing
trends in improving fuel efficiency, as shown in Figure 7.5 below, albeit with a period of
stagnation in the late 1980s and early 1990s. Note that this plot only shows figures for petrol
vehicles. The period 1987 to 2000, saw the introduction of mass market turbo-diesel engines
and direct injection turbo diesel technology. These new advances resulted in diesel car’s
market share growing to almost 15 % by 2000. Given the significantly lower fuel consumption
of these new diesel technologies, this would be likely to have resulted in a slight overall
downward trend in average new car CO2 emissions.
Figure 7.5: Average new car fuel consumption (petrol two wheel drive vehicles only) in
Litres/km, from 1978 to 2004
Source: DfT (2005)
The trends in Figure 7.2 and Figure 7.4 above shows a clear acceleration in fuel efficiency
improvements around the time of the introduction of the new car CO2 regulation (late 2008),
suggesting that the regulation had more of an impact on fuel efficiency of new cars than the
voluntary agreements that preceded it. Indeed the UK-level data in Figure 7.2 suggests that
the acceleration started in 2007. This could be an anticipatory effect of the EU new car CO2
regulation, with manufacturers ramping up efforts in the knowledge that a regulation was
being negotiated. However, it is also important to consider the exogenous factors that could
have played a part (see section 7.2.4). The sharp rise in fuel price around the same time
(see section 7.2.4.1) may have driven purchasing patterns, as well as the general squeeze
on incomes from the economic downturn (e.g. see “Recession has driven interest in greener
cars” - Sytner, 2011).
Before the introduction of the new car CO2 regulation, charts Figure 7.2, Figure 7.4 and
Figure 7.5 also show that fuel efficiency was improving, albeit at a slower rate. It is difficult to
isolate the specific drivers behind this trend, however two important factors have played a
part:
1. Company car tax - According to the Energy Savings Trust, of all the policies to reduce
new car CO2 emissions up to 2008, the revision of the company car tax system in 2002
had the strongest effect (EST, 2008). The HMRC evaluation in 2006 of the company car
tax system suggested that average CO2 emissions from company cars were around 15
g/km lower in 2004 than would otherwise have been the case if the reforms to the tax had
not taken place.

2. Voluntary Agreements - It is reasonable to assume that the voluntary agreements had
some impact, not least because Figure 7.5 shows that the rate of improvement in fuel
efficiency accelerated around the time of the introduction of the agreements (1998/9),
following around 10 years of no improvements. Figure 7.3 shows that much of the
improvements from 1998/9 will have come from increasing dieselisation of the fleet. It
would be reasonable to assume that between them, the voluntary agreements and the
reform of the company car tax played a significant role in the dieselisation of the car fleet
and consequent improvement in new car fuel efficiency.
Vehicle weight is a key factor that has impact on the level of CO2 emissions and fuel
efficiency. Figure 7.6 below shows how C-segment cars have got heavier since the 1980s.
Figure 7.6: Vehicle weight, 1970-2004
Source: Incerti et al (2005)
However in more recent years, the trend of increasing weight has slowed and started to
reverse, and in their 2011 report for the Low Carbon Vehicle Partnership (LowCVP), Element
Energy assume that weight will continue to fall. However they make this assumption on the
basis of the expected response by manufacturers to the existing EU new car CO2 regulation
(and on the assumption that a mass-based utility parameter will not encourage
manufacturers to increase the weight of their vehicles to receive easier targets). This
therefore does not give us compelling evidence for the likely trend of weight in cars in the
absence of any policies. But historically, the larger end of the car market has been
particularly profitable for manufacturers and it is possible that in the absence of policy,
manufacturers would explore whether even bigger vehicles are profitable.
7.2.3 Impact of climate change policies on vehicle costs and end-user prices
The last fifteen to twenty years has seen a significant increase in regulation to reduce the
environmental and health impacts of car emissions. One would assume that these more
stringent requirements will lead to higher production costs and consequently, higher vehicle
prices for consumers. However, in practice it is difficult to find real-world evidence that such
price increases have actually occurred (Figure 7.7), especially given that over the last two

decades there has been a significant amount of new EU-level legislation focused on road
vehicles.
Figure 7.7: EU-27 Harmonized indices of consumer prices indicate that vehicle prices have
remained constant
Source: Eurostat (2010)
Studies conducted ten and five years ago predicted that reducing CO2 emissions from new
cars to an average level of 140g CO2/km would make cars €2,400 and €1,200 more
expensive, from 1995 and 2002 baselines respectively (T&E, 2011). This implies that these
studies estimated the marginal costs of one percent of CO2 emissions reductions towards
140 g/km at around or likely above €100, which is about 0.5% of a car’s retail price.
Overall cars have become 12% to 22% cheaper – after inflation – in the eight years from late
2002 to late 2010 (AEA, 2011). For example, new cars have become 13% cheaper on
average in real terms over the past eight years, which means a €20,000 car in 2003 would
sell for €17,400 today. Before the CO2 regulation started to have an impact on the CO2
emissions from cars, the annual average reduction of car prices was slower compared to the
period after the CO2 regulation was announced in 2007. The average annual reduction in
CO2 emissions was 0.7% and 2.5% in 2002-2006 and 2002-2010 respectively.
Growth in environmental, safety and product regulation has led to a wide range of strategies
and practices by manufacturers to balance production costs and regulatory compliance.
Manufacturers have had to balance production costs while ensuring that they comply with
environmental regulation and meet the high standards of quality and performance that the
market demands. This has led to the growth of practices such as platform sharing, parts
‘commonisation’ and sharing of powertrains, all of which have been key to cost reductions in
the industry. Manufacturers have also shifted production of vehicles away from Western
Europe to Eastern Europe and Asia, in a bid not only to drive down costs through lower
labour rates, but also to satisfy rapidly growing new markets.
Essentially it has become extremely difficult to isolate the impact of vehicle attributes on
prices. This is mainly due to the complexity of vehicle production technology, pricing
strategies, numerous car segments and compliance with regulations. The findings of our

literature review show that it has now become more difficult to isolate the impact of various
car attributes - such as performance, environmental and safety features - on car prices
compared to ten to fifteen years ago. The temporal dimension of cost and profit is separate.
The cost of compliance, for example R&D investment or factor development, could be spread
over anything between 5 to 20 years. Pricing, servicing and finance plans are also designed
to spread the cost of ownership over a number of years.
Hence, climate change regulation is a sub-set of all the factors that affect costs and which,
potentially indirectly, influence car prices. The net impact of all these factors in balance will
determine how these costs translate into prices. While, additional features introduced as a
result of regulatory requirements increase costs, the inclusion of additional features that
improve performance levels and comfort can lead to improved margins (and higher prices),
where these bring added value to the consumer. For example, the introduction of catalysts,
the fitting of which was effectively required to meet EU air pollutant emissions legislation,
forced changes that have enabled improved performance of cars, (e.g. direct injection).
In summary, climate change legislation would always lead to increased costs, although the
requirements of some pieces of legislation did not necessarily increase costs. Where such
increased costs did not subsequently lead to increased prices, it was argued that this was
due to competition in the markets concerned. Reduced costs resulting from, for example,
economies of scale or improved productivity (for the reasons identified above), could offset
the increased costs of regulation. However, where net cost increases could not be passed on
to consumers, then the margins of manufacturers and/or their suppliers would be reduced.
More generally, if climate change legislation had not increased costs, car prices would be
lower than current levels. The extent to which increased costs can be passed on to
consumers depends on competition and market conditions. The ability to pass on costs
would vary by brand and the type of vehicle being sold (as well as the market) and exposure
to foreign brands.
7.2.4 Impact of other exogenous factors – oil prices, income levels,
technology and consumer preference – on car fuel efficiency
Car manufacturers function in an extremely complex and competitive market. Manufacturers
engage heavily in R&D to improve their service and product offering. In many cases
performance, safety and comfort related technologies and improvements have been
introduced while at the same time meeting more stringent environmental regulations.
Improvements in safety and comfort have tended to result in increased vehicle size and
weight. As a result, in order to maintain performance, engine power outputs have increased.
Despite this, test cycle fuel consumption figures were improving prior to the introduction of
tailpipe CO2 emissions regulations, although improvement has accelerated since regulations
were introduced. The impacts of rising fuel and commodity prices might also be expected to
provide a constant incentive to improve vehicle fuel consumption.
Car manufacturers are forced to maximise profits under whatever policy framework they all
operate in. They would thus only make efforts to improve fuel efficiency if it appeared this
would act to maximise profits. Much can also depend on consumer preferences – and these
can be influenced by a variety of external factors.
Hence, business strategy factors, direct cost factors (e.g. Resource Prices (raw materials,
energy), component and labour costs, exchange rates and shipping costs), indirect cost
factors (e.g. Research & Development, plant maintenance and depreciation, marketing),
market and consumer factors can all impact on fuel efficiency directly or indirectly. Some of
these factors are discussed in more detail below.
7.2.4.1 Fuel prices
In terms of exogenous factors affecting fuel efficiency of new cars, the principle factor is fuel
price, which in turn is driven by the oil price and taxation policy. Figure 7.8 below shows a

trend of increasing fuel price since 1991, with only some minor falls around 2001, 2006 and
2008.
Figure 7.8: Chart of Motor Spirit Prices in January from 1991 to 2011
0
20
40
60
80
100
120
140
Jan-78
Jan-79
Jan-80
Jan-81
Jan-82
Jan-83
Jan-84
Jan-85
Jan-86
Jan-87
Jan-88
Jan-89
Jan-90
Jan-91
Jan-92
Jan-93
Jan-94
Jan-95
Jan-96
Jan-97
Jan-98
Jan-99
Jan-00
Jan-01
Jan-02
Jan-03
Jan-04
Jan-05
Jan-06
Jan-07
Jan-08
Jan-09
Jan-10
Jan-11
penceperlitre
4 star/LRP
Premium Unleaded
Diesel
Source: DECC (2012)
Figure 7.5 showed that fuel efficiency improved from 9.8 l/100km in 1978 to about 8 l/100km
in 1987. As mentioned above, it remained fairly static at around 8 l/100km to 2000 and has
fallen again (i.e. improved fuel efficiency) since then to 7.5 l/100km in 2004. This pattern can
be compared against the trend in rising fuel price shown in Figure 7.8. Figure 7.9 below
combines these two trends.
This suggests that there may not always be a direct link between rising fuel price and new
car fuel efficiency, which can be a useful caveat when looking at future trends of fuel price.
Looking at the period from 1997 onwards, there would seem to be a reasonable correlation
between the progressive introduction of car CO2 policies, and new car fuel efficiency. This
would suggest going forward that the absence of policies could lead to little or no
improvements in new car fuel efficiency. However the above trends are not conclusive as
Figure 7.9 also shows that improvements in fuel efficiency took place from 1978 to 1987 in
the absence of any Government policy.

Figure 7.9: Average new car fuel consumption (registration weighted) Great Britain: 1978-2010
0
20
40
60
80
100
120
0
1
2
3
4
5
6
7
8
9
10
11
1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010
FuelPrice,p/litre
Litresper100kilometres
Average new car fuel consumption (registration weighted)
Great Britain: 1978-2010
Petrol cars
Diesel cars
Diesel Pricel
Unleaded Petrol Price
4 star/LRP Price
Source: DfT 2011, DECC (2012)
7.2.4.2 Elasticity of fuel consumption with respect to price
As noted by the World Energy Council in 2008, many studies have demonstrated a link
between the fuel consumption of cars and the price. Their results are consistent, and
converge towards a long-run elasticity of fuel efficiency to fuel price of +0.4 meaning that in
the long run, a 10% increase in the price of motor fuel leads to a total improvement in fuel
efficiency of 4% (WEC, 2008). This would suggest that a rising fuel price could lead to some
improvements in fuel efficiency (although this might include some changes to driving styles
rather than just from changes to new car purchasing habits). Figure 7.10 below suggests a
10% increase in fuel price by 2030, thus suggesting (that with all other factors equal) fuel
efficiency might improve by 4% over the same period. This would equate to a rate of around
0.3 g/km a year. This would represent a significantly lower rate of progress than historic
trends (for example, a 26% reduction in new car fuel efficiency over the 18 years from 1992
to 2010). Hanley et al (2002) suggest a lower elasticity of fuel price with respect to car
ownership, being 0.25 in the long term, suggesting that lower levels of fuel efficiency
improvements than those suggested above might result from the kind of fuel price increases
seen in Figure 7.10. Similarly, Graham and Glaister (2002) suggest a long run fuel price
elasticity with regard to car ownership of -0.1 to -0.24.

Figure 7.10:Forecast retail petrol price 2012-30, pence per litre
Source: DfT 2011
Finally, some studies (JTRC, 2007) suggest that fuel price elasticities are likely to fall in the
future as incomes increase and with increasing urbanisation. All of this suggests that the
relatively limited fuel price forecasts foreseen in Figure 7.10 could lead to yet lower levels of
fuel efficiency improvements in the absence of policies to drive such changes. Thus the
evidence in relation to fuel price forecasts and fuel price elasticities suggests that very low
levels of fuel efficiency improvements, if any, are to be expected from fuel price alone.
This view is supported by work done by Element Energy for the Low CVP which concluded
that low carbon cars are likely to require continuing financial support, in the form of
differential taxation (e.g. through company car tax or Vehicle Excise Duty) if they are to be
widely adopted in future (EE, 2011).
7.2.4.3 Business strategies and manufacturer choices
The car manufacturing business has seen huge changes over the past three decades. The
car markets these days feature a far greater range of models, variants and options. Most
studies (and interview respondents) indicate that manufacturers operate in several different
markets and determine optimal business and technological strategies on the basis and
nature of the car segment and competition in each market. If fuel prices remained constant
in real terms (and perhaps the distances that people felt they had to drive did too) then there
is little evidence that manufacturers would voluntarily improve fuel economy. Most likely there
would be an equilibrium in which manufacturers would provide a range of products some with
good fuel economy some with poor, to cater to the majority of customer tastes, but that
overall fleet average economy would remain constant over time.
Other factors can influence this equilibrium. For instance safety was never viewed by OEMs
as something which helped maximise profits – unless your brand was built on it like Volvo.
Euro NCAP changed all that as now OEMs pour money into getting the best possible rating.
Consumer preferences were shifted by some vivid press reporting of the Austin Metro’s
vulnerability in combination with nice clear metrics helping them to choose safer options.
Potentially the same thing could happen with fuel efficiency.

7.2.4.4 Technologies
Work done by AEA (2011) for the European Commission suggests that whilst car purchase
prices have fallen despite the introduction of car fuel efficiency policies, regulations and
standards have generally increased costs for manufacturers (although the extent of this is
dependent on the extent to which the manufacturer can offset the costs through economies
of scale or improved productivity, e.g. platform sharing). Flexible manufacturing techniques
allow manufacturers to more closely match supply to demand, while quality improvements
have reduced costs and contributed to profitability. Improved computing power coupled with
techniques such as simultaneous engineering has helped to reduce research and
development costs and product development times. This being the case, one would expect
manufacturers to avoid these costs in the absence of policy so as to maximise their profit
margins. Generally speaking, manufacturers are likely to continue to produce a range of
vehicles to suit different customers with a focus on optimising profits and if there were
improvements in overall fleet average fuel economy it would only be as a by-product of that
process.
7.2.4.5 Consumer demand and preferences
Consumer demands have evolved substantially over this period, with the emphasis shifting
from a desire simply to own a vehicle, to the desire to own a characterful and distinctive
vehicle of high quality and specification. Rising fuel prices, and to some extent environmental
concerns, are driving a shift in consumer preference towards relatively more economical
models; a sector which has traditionally generated thinner margins for the manufacturers. It
might also be reasonable to expect a continuing trend in consumer awareness of fuel
efficiency and climate change. However, there is no evidence that this consumer demand will
be a sufficiently strong signal in the absence of either a rapidly increasing fuel price or
policies to drive demand (e.g. graduated vehicle excise duty, fuel efficiency labelling etc). If
fuel prices remained constant in real terms then only a small percentage of the population
will make purchase decisions based on altruistic/environmental motives, the vast majority
primarily respond to price signals.
7.2.5 Historical trends in fuel efficiency in other countries
Some useful insight can be gained from the US, where the evolution of the Corporate
Average Fuel Economy (CAFE) standards gives an insight into potential ‘no policy’
counterfactuals. Figure 7.11 below shows that the introduction of CAFE coincided with sharp
improvements in fuel efficiency in the early to mid-1980s. When the CAFE standard was flat-
lined from 1990, improvements in fuel efficiency slowed and made little progress between
1990 and 2000.
Figure 7.11:Evolution of CAFE standards and change in fuel economy, from 1978 to 2019
Source: Shiau et. al (2009)

However, some improvements, albeit at a slower rate, are in evidence from 2000, which
could be taken as evidence to challenge the assumption that fuel efficiency would not
improve in the absence of policy. The improvements in this case could be due to rising fuel
prices, especially from 2006 onwards. However there could also have been an expectation
factor – having a flat-lined standard could be viewed quite differently by manufacturers to
having no standard. There may be an expectation that a flat-lined standard could be
tightened in future, so some improvements may come from a desire to manage this risk,
whereas manufacturers could in theory behave quite differently if they knew that there would
be no policy mechanism in place with which to regulate them. It is also important to bear in
mind that manufacturers became increasingly global organisations over this period, with
many products and technologies being developed to cater for markets where standards have
been introduced over this time. The US saw increasing levels of imports – e.g. rising imports
of Japanese cars – which may also have explained the rising fuel efficiency. This trend from
2000 onwards cannot therefore be seen as conclusive proof for a rising fuel efficiency
counterfactual, and indeed the sharp slowdown seen in fuel efficiency improvements around
1990 suggest a sizeable policy impact.
7.2.6 Likely future trajectories for fuel efficiency
Whilst historical trends can give us some insights into what the effect has been previously of
the introduction of policies, or the impact of exogenous factors such as oil price, it is also
important to consider the extent to which future improvements may mirror the historical
trends. For example, even if historical trends suggested that fuel efficiency improved in the
absence of policies in the past, this does not mean that such a trend will be likely in the
future, as there may be technological and physical constraints to further improvements.
The CCC’s work on fuel efficiency of new cars out to 2050 takes fuel price assumptions from
DfT, see Figure 7.10. This clearly shows a trend of increasing fuel price. However it is worth
noting that the rate of increase in the future is much lower than that seen historically in Figure
7.8 (0.4 ppl a year between 2012 and 2030, compared to 3 ppl a year from 1992 to 1999, 4.5
ppl a year from 2002 to 2006 and a huge 16.5 ppl a year from 2009 to 2011). Some
commentators have suggested that ‘peak oil’ will result in severe fluctuations and instability
in oil prices as demand exceeds supply. Sudden price spikes might provide an increased
focus on fuel economy amongst consumers (as happened as a result of the 1973 oil crisis).
What this means for the future evolution of new car fuel efficiency will depend on various
factors, including on the demand side (e.g. elasticity of fuel consumption in response to fuel
price) and on the supply side (e.g. available cost effective technologies for manufacturer to
install).
7.2.7 Conclusion
Overall, the following conclusions can be drawn for passenger cars:
I. An analysis of past trends does indicate a noticeable impact from climate
change policy.
Before the introduction of the voluntary agreements in 1998 new car fuel efficiency
had remained static for around ten years and started to improve after their
introduction.
The rate of fuel efficiency improvements increased further around 2007/8, coinciding
with the introduction of the more binding new car CO2 regulation.
II. However this evidence in itself is not conclusive and proving causality is always
difficult.
For example, there is some evidence that new car purchasing habits had been
influenced by the economic downturn from 2008, which could have helped
contribute to the acceleration of fuel efficiency improvements.

Furthermore, evidence on fuel efficiency from the late 70s and early 80s showed
rapidly improving fuel efficiency even in the absence of government policies.
III. Evidence from other countries gives a similar picture, namely that policies seem to
have had a noticeable impact on fuel efficiency, but the evidence is not clear cut.
Fuel efficiency improvements did stall noticeably when the CAFE standards in the
US were flat-lined from 1990, however improvements did start again in 1990 whilst
the standards still remained flat.
That said, a standard that is in place but not getting progressively harder, is not the
same as not having any policy in place, and some of the improvements from 1999
may have resulted from manufacturers’ expectation that the standards would be
tightened in the future, and therefore may not be expected in a no-policy
counterfactual.
IV. Whether the increased costs of complying with change policies lead to increases
in prices depends on inter alia the extent to which these costs are offset by cost
reductions resulting from economies of scale and improved productivity and
whether any cost increases can be passed on to consumers.
The extent to which increased costs can be passed on to consumers depends on
competition and market conditions. The ability to pass on costs can vary by brand
and the type of vehicle being sold (as well as the market) and exposure to foreign
brands.
Where net cost increases could not be passed on to consumers, then the margins of
manufacturers and/or their suppliers would be reduced. More generally, if
environmental and safety legislation had not increased costs, car prices would be
lower than current levels as manufacturers might relax fuel efficiency improvements,
so as to maximise their profit margins.
V. Looking ahead there is little evidence to suggest that fuel efficiency would
continue to improve in the absence of policies.
It is likely that fuel prices will continue to rise, and that this could lead to some fuel
efficiency improvements, but these improvements from fuel prices alone are likely to
be negligible and definitely much lower than historic rates.
We do not expect weight to continue to increase (weight can be a comparatively low
cost way of improving fuel consumption) and analysis of vehicle technologies
suggests there are few low cost options that can be implemented by manufacturers.
In summary, we believe that there is not sufficiently strong evidence to suggest that
the assumption of a flat counterfactual is incorrect and that the CCC should
therefore continue to use this assumption in its modelling work.
7.3 Vans/LCVs
Vans occupy an interesting position between passenger cars and the larger commercial
vehicles. In this section we are defining vans as commercial vehicles < 3.5 tonnes GVW, i.e.
those vehicles subject to EC Regulation 510/2011 Van CO2 regulations.
In preparation for the introduction of this regulation, the UK DfT was required to prepare a
Regulatory Impact Assessment. As part of this RIA AEA undertook a study of the
counterfactual CO2 emissions of new vans, working with TNO who undertook the generation

of revised cost curves (AEA, 2010a). This study considered the issues to be considered over
the 2008 – 2020 timeframe. These are the same issues that are relevant to this portion of
the study.
7.3.1 Diesel – petrol vehicle ratio
In 2001 DfT statistics indicated 95.3% of the around 275,000 LCV sold were diesel fuelled.
By 2010 this figure was 98.2% (DfT, 2011). However, closer examination of the detailed
database indicates that it contains a small number of vehicles registered as “light commercial
vehicles” which many would regard 4x4 private vehicles. These increase the non-diesel
proportion of the sales. Consequently, a 98.2% diesel proportion is likely to be an
underestimate, with very few non-diesel vans are sold. The corollary to this is that there is
negligible scope for improving the efficiency of the van fleet through increased dieselisation
of the fleet.
The principal driver for the existing diesel-petrol vehicle ratio is the economics of operating
vans. It is independent of GHG policy.
7.3.2 Trends in increasing vehicle efficiency
The study undertaken for the DfT noted that “vans” were not optimally considered as a single
homogenous group. For pollutant emission compliance they are grouped by their reference
mass (linked to kerb weight) with Class I vans having < 1265 kg, and Class III being >1,705
kg reference mass. Many class I vans can be viewed as being derived from cars, i.e. are
passenger cars from which the rear seats and windows have been replaced with a panel
body. These vehicles often use the chassis and powertrain platform of passenger cars, and
their CO2 emissions efficiency follows that of the passenger cars.
In contrast, the heavier light commercial vehicles have no car counterpart. Their CO2
emissions efficiency is independent from that of passenger cars, but is influenced by
commercial pressures, where the importance of fuel costs in the overall cost of ownership
ensures manufacturers promote fuel efficiency at the van design stage of the vehicles’
lifecycle.
Consequently, the increases in van efficiency differed for the fuel-weight range classes, as
illustrated below in Table 7.1.
Table 7.1: Increases in van efficiency and reported in the DfT new van counterfactual study
Diesel Petrol
Class I Class II Class III Class I Class II Class III
Change 2008 – 2020 -47.2% -29.5% -12% -34.7% -23.8% -12.0%
Change per year -3.9% -2.5% -1.0% -2.9% -2.0% -1.0%
From this analysis, which assumes the absence of GHG van regulations, if GHG car
regulations were removed today, the fuel efficiency of smaller vans would continue to
improve slightly more than heavier duty vans because of technology transferring from cars to
these small vans. The counterfactual study concluded that in the absence of any vehicle
CO2 regulations a natural improvement rate of 1% p.a. would occur between 2008 and 2010
(all other factors remaining unaltered). However, as will be seen, there are other factors
reducing this improvement.
7.3.3 Trends in increasing weight
A trend leading to poorer fuel efficiency, i.e. in the opposite direction to either technology
crossover from passenger cars, or intrinsic efficiency improvements, arises from the trend for
vehicles to get heavier. The analysis undertaken for the DfT quantified this trend as leading

to a +0.65% change in CO2 emissions per year, i.e. a 7.8% increase over the 12 year period
2008 – 2020.
7.3.4 The impact of environmental and safety regulations
The counterfactual van study also considered the impact of changes in the regulations
concerning vans. These included:
The introduction of diesel particulate filters (increasing CO2 emissions for diesel
vehicles);
The introduction of selective catalytic reduction for NOx abatement (increasing CO2
emissions for diesel vehicles);
The introduction of daytime running lights (increasing CO2 emissions for all vehicles).
Tyre pressure monitoring systems (TPMS) and gear shift indicators (GSI) were also
considered. In the DfT study it was anticipated these would not change CO2 emissions over
the regulatory test cycle (because at homologation vehicles are tested with correctly inflated
tyres and gear changes occur at specified points). However, for on-the-road driving, these
are anticipated to lead to modest CO2 emissions reductions.
7.3.5 Summary of data on vans
The DfT counterfactual new van CO2 study provides an evidence based baseline for vans.
Over the period 2008 to 2020 it concludes that in the absence of CO2 regulations the CO2
emissions (and by inference the fuel consumption) of Class III vans will decrease by 2.7% for
diesel vans( and by 5.2% for petrol vans though these only comprise around 2% of new van
sales). The difference is caused by other regulations on vehicle emissions and safety
leading to step increases in CO2 emissions from diesel vehicles at the date of their
introduction.
If the diesel trend were to apply consistently between 2020 and 2050 this would lead to the
following average van CO2 emission values:
Table 7.2: Increases in van efficiency as reported in the DfT new van counterfactual study,
projected from 2020 to 2050
Average van CO2 emissions Change relative to 2008
2008 202.9 g/km
2020 197.5 g/km -2.7%
2050 182.0 g/km -10.3%
Consequently this analysis predicts that in the absence of CO2 regulations by 2050 “natural
improvements” would not lead vans to meet the 175 g/km 2016 target.
It is also noted that this is a more modest rate of improvement than was reported for
passenger cars over the period 1978 – 2004 in DfT Statistics (a 23% improvement in this 26
year period). However, this figure includes contributions from:
the changing ratio of petrol to diesel fuelled vehicles, and
the replacement of carburettors for petrol vehicles to fuel injection systems, computer
engine management systems etc, and similar changes from mechanical systems to
electronically controlled fuel injection systems for diesel vehicles.
The former factor does not apply to vans, and the latter factor can be viewed as a one off
step change in technology that will not be repeated. When these factors are removed the
trends given in Table 7.2 appear well aligned with those seen in the past.

In summary, we believe that there is some evidence to suggest that the assumption
of a flat counterfactual is not valid for vans and it may be more appropriate for CCC
to revise this assumption in its modelling work to reflect a gradual rate of
improvement in van efficiency.
7.4 Heavy Trucks
Heavy trucks are defined as commercial vehicles whose gross vehicle weight (GVW) is
greater than 3.5 tonnes. They are used for the movement of goods, and have a range of
sizes and configurations from small (for example 5 tonne rigid trucks) used for local
deliveries, to large tractor units that are hitched to trailers and form a 44 tonne articulated
vehicle.
The EC has introduced regulations on the average CO2 emissions from cars and light
commercial vehicles (in 2010 and 2011) and is considering the options for heavy duty
vehicles. They commissioned two studies on the “reduction and testing of greenhouse gas
emissions from heavy duty vehicles;
Lot 1, Strategy, led by AEA, and
Lot 2, Test methodology, led by TU Graz
The Strategy study involved laying the foundations for all the work, and involved collecting
information on the European heavy duty vehicle market. Whilst this was from a European,
rather than only UK perspective, this study most probably provides the best contemporary,
authoritative information pertinent to this study for the CCC.
Some other noteworthy differences between heavy goods vehicles, passenger cars and vans
are:
Heavy goods vehicles are operated virtually exclusively for business, commercial,
purposes
The cost of fuel when operating a heavy goods vehicles is a markedly higher
proportion of the overall operational costs than for lighter vehicles.
These two factors mean that the operators of trucks are very aware of fuel consumption
(through its cost) and there is a much higher demand for improved fuel efficiency than for
light duty vehicles. However, there are some fundamental physical constraints, involved with
the energy requirements for moving a tonne of freight a set distance. These factors mean
that heavy duty trucks have been subject to reductions in fuel consumption in the absence of
GHG regulations, but there are higher barriers to further large reductions relative to light duty
vehicles.
Heavy duty vehicle fleet segmentation:
For this study heavy duty trucks are sub-divided into the following groups:
Rigid trucks up to 15 tonnes gross vehicle weight
Rigid trucks above 15 tonnes gross vehicle weight
All articulated trucks (those there are very few of these less than up to 15 tonnes
gross vehicle weight
Construction trucks (a mixture of the above 3 categories, with specialist body types
and duty cycles).

For heavy duty vehicles this is not a variable because the quantity of petrol used in HDVs,
relative to diesel fuel, is extremely small.
This is challenging to quantify because:
Unlike cars trucks are not homologated over a road drive cycle leading to mass of
emissions /km driven, but rather their engines are homologated using an engine
dynamometer, and emissions are characterised in units of mass /kWh output at the
driveshaft.
The fuel consumption achieved by trucks on the road is markedly affected by the load
it carries. This is rarely well characterised.
Commercial pressures on trucks mean that fleet managers focus their attention on
the fuel bill. Whilst vehicle km data are often gathered, attention is paid to aspects
like:
o Routing, i.e. avoiding unnecessary vehicle km;
o Load factors, particularly trying to carry a load for the return journey
o Driving style, e.g. promoting safe and fuel efficient driving.
All the above factors combine with the intrinsic fuel efficiency of the vehicle to affect the fuel
bill. Key questions for this study are:
How has fuel economy changed over time;
Is this different for the three categories of trucks; and
How might this change in the future.
A quantification as to how fuel efficiency has changed over time comes from the data within
road transport inventory compilation tools. This was quantified using the most recent DfT
sponsored review into the speed related emission functions for u-CO2 DfT (2009). The CO2
emissions (per km travelled for vehicles on flat roads with 50% payload) were taken for the
following heavy duty vehicle types:
Rigid truck 3.5 to 7.5 tonnes GVW Small (< 15 t) rigid truck
Rigid truck 12 to 14 tonnes GVW Small (<15 t) rigid truck
Rigid truck 20 to 26 tonnes GVW Large (>15 t) rigid truck
Rigid truck >32 tonnes GVW Large (>15 t) rigid truck
Articulated truck 20 to 28 tonnes GVW Articulated truck
Articulated truck 40 to 50 tonnes GVW Articulated truck
The data were referenced relative to Euro I (which applied to all vehicles sold from January
1993), and then pairs of truck weigh range data were averaged to give relative fuel efficiency
and CO2 emission factors for the three categories of trucks. This is plotted in Figure 7.12.

Figure 7.12:The CO2 emissions, and fuel efficiency, as a function of time for three different
sized trucks deduced from inventory emission factors.
88%
90%
92%
94%
96%
98%
100%
102%
Euro I Jan
1993
Euro II Oct
1996
Euro III Oct
2001
Euro IV Oct
2006
Euro V Oct
2009
CO2emissions(andfuelconsumption)
relativetoEuroIvehicles
Date of emission standards introduction
small rigid
large rigid
artic
Factors influencing the fuel efficiency for mainstream technology options include engine size
and their power rating. Changes that have occurred within the 1993 to 2011 timeframe
include:
Engine changes including from IDI to DI, the addition of turbochargers and the
addition of intercoolers;
Fundamental changes in fuelling systems, from mechanical pumps and metering to
very high pressure, computer controlled unit injectors, to very high pressure common
rail systems;
Improvements in vehicle aerodynamics;
The addition of speed limiters, mandated by Directive 2002/85/EC for all new vehicles
sold after 1st
January 2005;
Changes in emissions abatement technology;
Increases in mass caused by changes in comfort levels, cab sizes, fittings (MAC,
fridges etc) (Issue of demographic profile of truck drivers and need to make the
profession attractive).
An estimation as to how fuel efficiency might change in the future has been given in the EC
Lot 1 report, where a baseline for future fuel use and GHG emissions was developed (AEA-
Ricardo, 2011)9
. The tabulated conclusions from this are given in Table 7.3. Three different
components are considered:
Changes in fuel consumption caused by improvements in the powertrain for new
vehicles;
Changes in fuel consumption caused by changes in the rest of the vehicle (e.g. light
weighting, or aerodynamics and changes is weight and safety equipment) for new
vehicles;
Changes in fuel consumption caused by emissions legislation (negative numbers
indicate an increase in fuel consumption, or a fuel consumption penalty).
9
Section 4.4 (page 177) of final report

Table 7.3: BAU estimates on evolution of fuel consumption benefit (penalty) for base
conventional diesel vehicles - figures indicate benefit/penalty compared to
previous year
2010 2013 2015 2018 2020 2025 2030
New Vehicle % powertrain
natural improvement
(a)
Truck 0.0% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3%
Bus / Coach 0.0% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3%
New Vehicle % vehicle FC
improvement
Long Haul Truck
(b)
0.0% 0.5% 0.5% 0.5% 0.5% 0.5% 0.5%
Coach
(c)
0.0% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3%
Bus
(d)
0.0% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2%
% FC penalty from
emissions legislation
(e) All 0.0% -3.0% 0.0% -3.0% 0.0% 0.0% 0.0%
Source: Estimates by Ricardo (2010)
Notes: Business as usual scenario of fuel consumption of new vehicles - assuming no incentives or legislative
CO2 for HDV
(a) Natural p.a. improvement in powertrain efficiency includes transmission and engine auxiliaries
(b) Assume overall circa 10% reduction using vehicle aids by 2030
(c) Some aero improvements and weight reduction
(d) Forecast reduction in vehicle mass to increase fuel economy of vehicles - assume 1% reduction in
weight every 5 years - 0.8% fuel consumption improvement every 5 years
(e) Penalty from increasing emissions legislation in 2013 and then potential Euro VII around 2018
The categories used in the EC Lot 1 report (AEA-Ricardo, 2011) correlate with the categories
considered in this study as indicated in Table 7.4:
Table 7.4: Correlation between the truck categories used in this study and vehicle categories
in AEA-Ricardo (2011)
Vehicle category for this study Equivalent vehicle category in EC Lot 1 study
Rigid trucks up to 15 tonnes gross vehicle weight Truck
Rigid trucks above 15 tonnes gross vehicle weight Long haul truck
All articulated trucks Long haul truck
Bus Bus
Coach Coach
If the reductions in fuel consumption between 2010 and 2030 are extrapolated (i.e. set to be
the same as) between 2030 and 2050 (caused by both improvements in the powertrain and
in the rest of the vehicle) then the cumulative reduction in fuel consumption predicted is
shown in Figure 7.13.

Figure 7.13:The estimated reductions in fuel consumption (and CO2 emissions) as a function of
time for three different sized heavy duty vehicles (as reported to EC DG CLIMA)
-5%
0%
5%
10%
15%
20%
25%
2010 2015 2020 2025 2030 2035 2040 2045 2050
Estimatedreductionsinfuelconsimption
(litres/km)fornewheavydutyvehiclesupto2050
Year of manufacture
Long haul/articulated truck
Large rigid truck and coach
Bus
Small rigid truck and construction
It is emphasised that these predictions are made in the absence of there being any
regulations regarding CO2 emissions from large trucks in place. Also, these predictions are
for new vehicles. Consequently, the average for the fleet will lag behind this.
It is also interesting to note how there is a similarity between the predictions over the next 15
years, and those reported to have happened during the last 15 years from inventory
compilation emission factors.
7.4.3 Trends in increasing weight and the impact of environmental and safety
regulations
It is acknowledged that there is a trend for vehicles to get heavier. This is not uniform, with
higher levels of comfort, and indeed larger cabs, being more prevalent for the trucks
undertaking the long haul journeys, and occurring less for the small rigid trucks. There are
also changes to the regulations controlling vehicles led by safety. The estimates of changes
in fuel consumption caused by changes in the rest of the vehicle includes a component
covering these. Therefore, no further compensation is required.
7.4.4 Summary of data on trucks
Three categories of trucks are considered, rigid trucks <15 tonnes GVW, rigid trucks >15
tonnes GVW and all articulated trucks. The EC “Reduction and testing of GHG emissions
from heavy duty vehicles: Lot 1 Strategy” project provides an evidence based baseline for
trucks up to 2030. It concludes that in the absence of CO2 regulations the CO2 emissions
(and by inference the fuel consumption) of the larger rigid and the articulated trucks will
decrease by 10%, whereas for the smaller rigid trucks the decrease will be 6%.
The CO2 emissions (and by inference the fuel consumption) of trucks during the past 15
years has been calculated using inventory compilation emission factors. This enables a like
for like comparison to be undertaken. Data on average fuel consumed by trucks per vehicle
km driven also contain contributions from changes (increases) in average payload carried,
and increases in the average size of truck used. Hence these data are not able to provide a
like for like comparison to be made.

In summary, we believe that there is good evidence to suggest that the assumption
of a flat counterfactual is incorrect for specific sizes of heavy trucks. However, the
general trend of increasing vehicle sizing (presumably in a drive to increase
operational efficiency on a tonne-km basis) means that the fleet as a whole has a
trend to increasing MPG . CCC should therefore factor these elements into its
modelling work to reflect annual increases in heavy truck efficiency, but factoring in
changes in relative vehicle sizing affecting actual energy consumption per km.
7.5 Other Vehicles
7.5.1 Summary of fuel usage by different road vehicle types
It is useful to appreciate the importance of “other vehicles” (buses, coaches, motorcycles and
mopeds) to the UK road transport fuel usage overall. The data from the 2009 UK GHG
inventory assigns the consumption of petrol and diesel among road vehicles. This is
presented as a pie chart in Figure 7.14, with the fuel usage (in ktonnes) provided.
As a fraction of all fuel, buses and coaches consume 4.2% of all road transport fuel, whereas
two wheeled vehicles consume 0.5%.
Figure 7.14:The quantities of fuel used by different vehicle types in 2009
15,836
321196
7,077
4,659
3,548
3,915
1,542
Petrol Passenger cars
Petrol Light commercial vehicles
Petrol Mopeds and motor cycles
Diesel Passenger cars
Diesel Light commercial vehicles
Diesel Rigid trucks
Diesel Articulated trucks
Diesel Buses and coaches
As indicated in Figure 7.14, buses and coaches use, virtually exclusively diesel fuel, whereas
two wheeled vehicles use virtually exclusively petrol fuel. This ratio is unlikely to change in
the future.
There are modest improvements in the fuel efficiency of two wheeled vehicles in the absence
of CO2 regulations. However, there is little hard evidence available. Also, because their fuel
consumption is such a minor fraction of road transport fuel as a whole, large changes in the

fuel efficiency of these vehicles would make negligible overall difference. Therefore, two
wheeled vehicles are not considered further in this study.
Buses and coaches, whilst often grouped together because both are public service vehicles,
have very different drive cycles and are best considered separately.
The powertrain of coaches are quite similar to those of rigid trucks of up to around 18 tonnes
GVW, whereas buses, with their stop-start urban usage have powertrains somewhat different
to those of trucks. For both groups of vehicles there are improvements in fuel efficiency that
are occurring for new vehicles, both from changes in the powertrain and changes in the rest
of the vehicle (e.g. light weighting, or aerodynamics and changes is weight and safety
equipment). This was discussed in Section 7.4, using data from the EC Lot 1 report, where a
baseline for future fuel use and GHG emissions was developed (see Table 7.3 and Figure
7.13). This concluded that for coaches (and small rigid trucks) over the period 2010 – 2030
an overall 6% improvement in fuel efficiency was predicted, whereas for buses it was 4%.
Projecting forward to 2050 the cumulative reductions, relative to 2010, are predicted to be
18% for coaches and 14% for buses.
In summary, we believe that there some evidence to suggest that the assumption of
a flat counterfactual for bus and coach efficiency is incorrect and that the CCC
should therefore consider revising this assumption in its modelling work. For
motorcycles there is insufficient evidence to make a qualified judgement either way,
so we would recommend CCC continue to use its existing assumption.
7.6 Capital Costs of Vans, Trucks and Other Vehicles
The purpose of this section was to review the evidence for changes in the capital costs of the
dominant vehicle technologies within each vehicle category as another dimension in the
development of a baseline scenario.
The cost of an item of technology over a period of time is often difficult to establish because
of advances in technology making it extremely difficult to establish a like for like comparison.
The capital costs of such items are also affected not only by their intrinsic price but also by:
The impact of inflation,
Changes in exchange rates for imported items.
Research into the capital costs of vans, trucks and other vehicles (excluding passenger cars)
has provided virtually no hard evidence for trends. In addition to general internet searches, a
number of specific sites were visited and searched including:
SMMT ACEA
FTA RHA
Searches aimed to find the evolution of the cost of buying, or operating, vehicles. Whilst it
was relatively easy to find trends in the average cost of a commodity, e.g. fuel prices, there
was a lack of information on average new prices paid. Data for the number of vehicles sold,
or registered, was found, but not for the average price paid.
Further, whilst we have data that would enable the average price of, for examples, new vans
to be weighted by sales volume, for 2010 and also for 2008 examination of these shows that
the pattern of buying has changed, with the current poorer economic climate leading to a
reduction in sales of class III (the heaviest light duty vans) relative to their lighter
counterparts, the Class I vans. Consequently, such a comparison, when the effects of

inflation are stripped out, are not comparing like for like because of the changing purchasing
patterns.
The challenge of comparing like for like data becomes even more challenging for heavy duty
vehicles, trucks, buses and coaches. The twin challenges here are the relatively low
numbers of units sold (for example the UK fleet comprises around 27 million passenger cars
but under 0.4 million trucks) and the wide diversity of different models available. The
diversity of trucks often means that each order is close to bespoke, with the basic powertrain
vehicle GVW specification being augmented with the type of cab, and all the accessory
options being offered. The ordering of a truck (or fleet of trucks) is therefore more complex
than for passenger cars. Furthermore, with the flexibilities in vehicle accessory specifications
there are also flexibilities in price. Hence, whilst there are generic guide prices, the price
paid by a customer for heavy duty vehicle(s) is also bespoke. All these factors go some way
towards explaining why what is conceptually simple, trends in average price, is both difficult
to find, and when data are available, difficult to interpret.
At a qualitative level it can be reasoned that there are a number of different manufacturers
for trucks, buses, coaches and other vehicles. The competition of the free market leads to
there being a “current going price” for like for like new vehicles. As time passes there are
two competing driving forces on price:
The desire of manufacturers to maintain, indeed to increase, their market share
means that innovation and efficiencies in production generally cause like for like
vehicles to become less expensive over time, despite there being advances in
technology.
The regulations that vehicles have to comply with, in terms of emissions, and safety,
force vehicle manufacturers to adopt certain technologies otherwise their vehicle will
not be certified for sale. This drive leads to vehicles generally becoming more
expensive over time.
These two factors generate pressures in opposite directions. To a first approximation it is
assumed: that the price of new vehicles has remained relatively flat (in real terms for like
for like vehicle utility) over the last 20 years but that the sophistication of vehicles has
markedly increased.
In summary, we believe that there is not sufficiently strong evidence to suggest that
the assumption of a flat counterfactual is incorrect and that the CCC should
therefore continue to use this assumption in its modelling work for the capital costs
of other vehicles.
7.7 Summary of Recommendations
The following Table 7.5 provides a summary of this study’s recommendations in terms of
future changes in the vehicle fuel consumption for the dominant vehicle technologies within
each vehicle category in the absence of any government policy to reduce GHG emissions.
It should be noted that the estimates provided in Table 7.5 assume no changes in average
vehicle sizes in each category, nor changes in average loading/utilisation of capacity by
weight. For heavy duty trucks in particular there has been a historic trend in both increasing
vehicle size within the broad weight categories, and improvements in utilisation are likely to
have increased the average weight of the loads carried. These effects have appear to have
counter-acted the overall improvements in vehicle efficiency seen at a vehicle level

historically. If it were possible to suitably quantify further changes in these aspects in the
future, the resulting impacts on fuel consumption should be taken into account. It was not
possible to account for such effects for this study.
The study has found that there is either no evidence or no sufficiently strong evidence to
suggest that the current assumption on costs is incorrect – i.e. that costs will remain
approximately constant in the absence of any government policy to reduce GHG emissions.
Table 7.5: Recommended projection of changes in road vehicle fuel consumption in the
absence of any government policy to reduce GHG emissions*
Mode Category Projected % change in fuel consumption
2010 2020 2030 2040 2050
Cars All 0.0% 0.0% 0.0% 0.0% 0.0%
Vans All 0.0% -2.2% -4.8% -7.3% -9.9%
Motorcycles All 0.0% 0.0% 0.0% 0.0% 0.0%
Heavy Duty Trucks Small rigid 0.0% 3.0% -0.1% -3.0% -5.9%
Large rigid 0.0% -0.1% -5.9% -11.4% -16.6%
Articulated 0.0% -2.1% -9.6% -16.6% -23.0%
Construction 0.0% 3.0% -0.1% -3.0% -5.9%
Buses and Coaches Bus 0.0% 0.9% -4.0% -8.7% -13.2%
Coach 0.0% -0.1% -5.9% -11.4% -16.6%
Notes: * Estimates assume no changes in average vehicle sizes in each category, nor changes in average
loading/utilisation of capacity by weight.

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136 Ref: AEA/ED57444/Issue Number 2
Appendices
Appendix 1: Technology Specific Characteristics

Appendix 1 – Technology Specific
Characteristics
This appendix provides information on the assumptions for specific vehicle and powertrain
characteristics for all vehicle types used in the study calculations.
Table A1: Technology Specific Characteristics for all vehicle types
Category Item Powertrain 2010 2020 2030 2040 2050
Car New vehicle lifetime, years All 14 14 14 14 14
Car ICE range (km) NG ICE 500 500 500 500 500
Car Other 500 500 500 500 500
Car Hydrogen range (km) H2FC 500 500 500 500 500
Car Electric range (km) Petrol HEV 2 2 2 2 2
Car Diesel HEV 2 2 2 2 2
Car Petrol PHEV 30 30 30 30 30
Car Diesel PHEV 30 30 30 30 30
Car Petrol REEV 60 60 60 60 60
Car Diesel REEV 60 60 60 60 60
Car H2FC 2 2 2 2 2
Car H2FC PHEV 30 30 30 30 30
Car H2FC REEV 60 60 60 60 60
Car BEV 160 200 240 280 320
Car Distance in fuel mode 1
(ICE/H2/NG) (%)
Petrol ICE 100% 100% 100% 100% 100%
Car Diesel ICE 100% 100% 100% 100% 100%
Car Petrol HEV 100% 100% 100% 100% 100%
Car Diesel HEV 100% 100% 100% 100% 100%
Car Petrol PHEV 69% 57% 57% 57% 57%
Car Diesel PHEV 69% 57% 57% 57% 57%
Car Petrol REEV 38% 38% 38% 38% 38%
Car Diesel REEV 38% 38% 38% 38% 38%
Car BEV 100% 100% 100% 100% 100%
Car H2FC 100% 100% 100% 100% 100%
Car H2FC PHEV 69% 57% 57% 57% 57%
Car H2FC REEV 38% 38% 38% 38% 38%
Car NG ICE 100% 100% 100% 100% 100%
Car Diesel FHV
Car Diesel HHV
Car DNG ICE
Car # New vehicles/year (UK
approx.)
All
1,996,325 2,934,598 3,081,328 3,235,394 3,397,164
Car # Fleet vehicles (UK
approx.)
All
27,948,550 29,345,978 30,813,276 32,353,940 33,971,637
Car ICE sizing as % Max
Power (inverse DOH)
ICE 100% 100% 100% 100% 100%
Car HEV 75% 75% 75% 75% 75%
Car PHEV 75% 75% 75% 75% 75%
Car REEV 38% 38% 38% 38% 38%
Car BEV 0% 0% 0% 0% 0%
Car H2FC 0% 0% 0% 0% 0%
Car Basic real-world %
increase
ICE 19.5% 19.5% 19.5% 19.5% 19.5%
Car HEV 21.7% 21.7% 21.7% 21.7% 21.7%
Car PHEV 22.6% 23.0% 23.0% 23.0% 23.0%
Car REEV 23.5% 23.5% 23.5% 23.5% 23.5%
Car BEV 24.6% 24.6% 24.6% 24.6% 24.6%
Car H2FC 24.6% 24.6% 24.6% 24.6% 24.6%
Car Battery usable SOC for
electric range
All
70% 70% 80% 85% 90%

Car Ratio fuel cell size to max
power
H2FC REEV
50.0% 50.0% 50.0% 50.0% 50.0%
Car % Max power ICE for
electrified drivetrains
BEV 85.1% 85.1% 85.1% 85.1% 85.1%
Car H2FC 85.1% 85.1% 85.1% 85.1% 85.1%
Car REEV 138.1% 138.1% 138.1% 138.1% 138.1%
Car Other 124.8% 124.8% 124.8% 124.8% 124.8%
Van New vehicle lifetime, years All 14 14 14 14 14
Van ICE range (km) NG ICE 500 500 500 500 500
Van Other 500 500 500 500 500
Van Hydrogen range (km) H2FC 500 500 500 500 500
Van Electric range (km) Petrol HEV 2 2 2 2 2
Van Diesel HEV 2 2 2 2 2
Van Petrol PHEV 30 30 30 30 30
Van Diesel PHEV 30 30 30 30 30
Van Petrol REEV 60 60 60 60 60
Van Diesel REEV 60 60 60 60 60
Van H2FC 2 2 2 2 2
Van H2FC PHEV 30 30 30 30 30
Van H2FC REEV 60 60 60 60 60
Van BEV 160 200 240 280 320
Van Distance in fuel mode 1
(ICE/H2/NG) (%)
Petrol ICE 100% 100% 100% 100% 100%
Van Diesel ICE 100% 100% 100% 100% 100%
Van Petrol HEV 100% 100% 100% 100% 100%
Van Diesel HEV 100% 100% 100% 100% 100%
Van Petrol PHEV 69% 57% 57% 57% 57%
Van Diesel PHEV 69% 57% 57% 57% 57%
Van Petrol REEV 38% 38% 38% 38% 38%
Van Diesel REEV 38% 38% 38% 38% 38%
Van BEV 100% 100% 100% 100% 100%
Van H2FC 100% 100% 100% 100% 100%
Van H2FC PHEV 69% 57% 57% 57% 57%
Van H2FC REEV 38% 38% 38% 38% 38%
Van NG ICE 100% 100% 100% 100% 100%
Van Diesel FHV
Van Diesel HHV
Van DNG ICE
Van # New vehicles/year (UK
approx.)
All
226,135 332,418 349,039 366,491 384,816
Van # Fleet vehicles (UK
approx.)
All
3,165,890 3,324,185 3,490,394 3,664,913 3,848,159
Van ICE sizing as % Max
Power (inverse DOH)
ICE 100% 100% 100% 100% 100%
Van HEV 75% 75% 75% 75% 75%
Van PHEV 75% 75% 75% 75% 75%
Van REEV 38% 38% 38% 38% 38%
Van BEV 0% 0% 0% 0% 0%
Van H2FC 0% 0% 0% 0% 0%
Van Basic real-world %
increase
ICE 19.5% 19.5% 19.5% 19.5% 19.5%
Van HEV 21.7% 21.7% 21.7% 21.7% 21.7%
Van PHEV 22.6% 23.0% 23.0% 23.0% 23.0%
Van REEV 23.5% 23.5% 23.5% 23.5% 23.5%
Van BEV 24.6% 24.6% 24.6% 24.6% 24.6%
Van H2FC 24.6% 24.6% 24.6% 24.6% 24.6%
Van Battery usable SOC for
electric range
All
70% 70% 80% 85% 90%
Van Ratio fuel cell size to max
power
H2FC REEV
50.0% 50.0% 50.0% 50.0% 50.0%
Van % Max power ICE for BEV 85.1% 85.1% 85.1% 85.1% 85.1%

Van electrified drivetrains H2FC 85.1% 85.1% 85.1% 85.1% 85.1%
Van REEV 138.1% 138.1% 138.1% 138.1% 138.1%
Van Other 124.8% 124.8% 124.8% 124.8% 124.8%
Small rigid New vehicle lifetime, years All 12 12 12 12 12
Small rigid ICE range (km) NG ICE 500 500 500 500 500
Small rigid Other 500 500 500 500 500
Small rigid Hydrogen range (km) H2FC 500 500 500 500 500
Small rigid Electric range (km) Petrol HEV 2 2 2 2 2
Small rigid Electric range (km) Diesel HEV 2 2 2 2 2
Small rigid Petrol PHEV
Small rigid Diesel PHEV
Small rigid Petrol REEV
Small rigid Diesel REEV
Small rigid H2FC 2 2 2 2 2
Small rigid H2FC PHEV
Small rigid H2FC REEV
Small rigid BEV 160 200 240 280 320
Small rigid Distance in fuel mode 1
(ICE/H2/NG) (%)
Petrol ICE
Small rigid Diesel ICE 100% 100% 100% 100% 100%
Small rigid Petrol HEV
Small rigid Diesel HEV 100% 100% 100% 100% 100%
Small rigid Petrol PHEV
Small rigid Diesel PHEV
Small rigid Petrol REEV
Small rigid Diesel REEV
Small rigid BEV 100% 100% 100% 100% 100%
Small rigid H2FC 100% 100% 100% 100% 100%
Small rigid H2FC PHEV
Small rigid H2FC REEV
Small rigid NG ICE 100% 100% 100% 100% 100%
Small rigid Diesel FHV 100% 100% 100% 100% 100%
Small rigid Diesel HHV 100% 100% 100% 100% 100%
Small rigid DNG ICE 60% 60% 60% 60% 60%
Small rigid # New vehicles/year (UK
approx.)
All
11,206 14,120 14,826 15,567 16,346
Small rigid # Fleet vehicles (UK
approx.)
All
134,476 141,200 148,260 155,673 163,456
Small rigid ICE sizing as % Max
Power (inverse DOH)
ICE 100% 100% 100% 100% 100%
Small rigid HEV 75% 75% 75% 75% 75%
Small rigid FHV 80% 80% 80% 80% 80%
Small rigid HHV 80% 80% 80% 80% 80%
Small rigid BEV 0% 0% 0% 0% 0%
Small rigid H2FC 0% 0% 0% 0% 0%
Small rigid Basic real-world %
increase
ICE 41.3% 41.3% 41.3% 41.3% 41.3%
Small rigid HEV 42.5% 42.5% 42.5% 42.5% 42.5%
Small rigid FHV 42.5% 42.5% 42.5% 42.5% 42.5%
Small rigid HHV 42.5% 42.5% 42.5% 42.5% 42.5%
Small rigid BEV 43.9% 43.9% 43.9% 43.9% 43.9%
Small rigid H2FC 43.9% 43.9% 43.9% 43.9% 43.9%
Small rigid Battery usable SOC for
electric range
All
70% 70% 80% 85% 90%
Small rigid Ratio fuel cell size to max
power
H2FC REEV
Small rigid % Max power ICE for
BEV 85% 85% 85% 85% 85%
Small rigid H2FC 85% 85% 85% 85% 85%
Small rigid Other 125% 125% 125% 125% 125%
Large rigid New vehicle lifetime, years All 10 10 10 10 10

Large rigid ICE range (km) NG ICE 500 500 500 500 500
Large rigid Other 500 500 500 500 500
Large rigid Hydrogen range (km) H2FC 500 500 500 500 500
Large rigid Electric range (km) Petrol HEV 2 2 2 2 2
Large rigid Electric range (km) Diesel HEV 2 2 2 2 2
Large rigid Petrol PHEV
Large rigid Diesel PHEV
Large rigid Petrol REEV
Large rigid Diesel REEV
Large rigid H2FC 2 2 2 2 2
Large rigid H2FC PHEV
Large rigid H2FC REEV
Large rigid BEV
Large rigid Distance in fuel mode 1
(ICE/H2/NG) (%)
Petrol ICE
Large rigid Diesel ICE 100% 100% 100% 100% 100%
Large rigid Petrol HEV
Large rigid Diesel HEV 100% 100% 100% 100% 100%
Large rigid Petrol PHEV
Large rigid Diesel PHEV
Large rigid Petrol REEV
Large rigid Diesel REEV
Large rigid BEV
Large rigid H2FC 100% 100% 100% 100% 100%
Large rigid H2FC PHEV
Large rigid H2FC REEV
Large rigid NG ICE 100% 100% 100% 100% 100%
Large rigid Diesel FHV 100% 100% 100% 100% 100%
Large rigid Diesel HHV 100% 100% 100% 100% 100%
Large rigid DNG ICE 70% 70% 70% 70% 70%
Large rigid # New vehicles/year (UK
approx.)
All
9,167 9,625 10,106 10,611 11,142
Large rigid # Fleet vehicles (UK
approx.)
All
91,666 96,249 101,062 106,115 111,421
Large rigid ICE sizing as % Max
Power (inverse DOH)
ICE 100% 100% 100% 100% 100%
Large rigid HEV 75% 75% 75% 75% 75%
Large rigid FHV 80% 80% 80% 80% 80%
Large rigid HHV 80% 80% 80% 80% 80%
Large rigid BEV 0% 0% 0% 0% 0%
Large rigid H2FC 0% 0% 0% 0% 0%
Large rigid Basic real-world %
increase
ICE 9.0% 9.0% 9.0% 9.0% 9.0%
Large rigid HEV 10.1% 10.1% 10.1% 10.1% 10.1%
Large rigid FHV 10.1% 10.1% 10.1% 10.1% 10.1%
Large rigid HHV 10.1% 10.1% 10.1% 10.1% 10.1%
Large rigid BEV
Large rigid H2FC 11.5% 11.5% 11.5% 11.5% 11.5%
Large rigid Battery usable SOC for
electric range
All
70% 70% 80% 85% 90%
Large rigid Ratio fuel cell size to max
power
H2FC REEV
Large rigid % Max power ICE for
BEV
Large rigid H2FC 85% 85% 85% 85% 85%
Large rigid Other 125% 125% 125% 125% 125%
Articulated New vehicle lifetime, years All 10 10 10 10 10
Articulated ICE range (km) NG ICE 1000 1000 1000 1000 1000
Articulated Other 1000 1000 1000 1000 1000
Articulated Hydrogen range (km) H2FC 1000 1000 1000 1000 1000
Articulated Electric range (km) Petrol HEV 2 2 2 2 2

Articulated Electric range (km) Diesel HEV 2 2 2 2 2
Articulated Petrol PHEV
Articulated Diesel PHEV
Articulated Petrol REEV
Articulated Diesel REEV
Articulated H2FC 2 2 2 2 2
Articulated H2FC PHEV
Articulated H2FC REEV
Articulated BEV
Articulated Distance in fuel mode 1
(ICE/H2/NG) (%)
Petrol ICE
Articulated Diesel ICE 100% 100% 100% 100% 100%
Articulated Petrol HEV
Articulated Diesel HEV 100% 100% 100% 100% 100%
Articulated Petrol PHEV
Articulated Diesel PHEV
Articulated Petrol REEV
Articulated Diesel REEV
Articulated BEV
Articulated H2FC 100% 100% 100% 100% 100%
Articulated H2FC PHEV
Articulated H2FC REEV
Articulated NG ICE 100% 100% 100% 100% 100%
Articulated Diesel FHV 100% 100% 100% 100% 100%
Articulated Diesel HHV 100% 100% 100% 100% 100%
Articulated DNG ICE 75% 75% 75% 75% 75%
Articulated # New vehicles/year (UK
approx.)
All
17,037 17,889 18,784 19,723 20,709
Articulated # Fleet vehicles (UK
approx.)
All
170,374 178,893 187,837 197,229 207,091
Articulated ICE sizing as % Max
Power (inverse DOH)
ICE 100% 100% 100% 100% 100%
Articulated HEV 75% 75% 75% 75% 75%
Articulated FHV 80% 80% 80% 80% 80%
Articulated HHV 80% 80% 80% 80% 80%
Articulated BEV
Articulated H2FC 0% 0% 0% 0% 0%
Articulated Basic real-world %
increase
ICE 0.0% 0.0% 0.0% 0.0% 0.0%
Articulated HEV 1.1% 1.1% 1.1% 1.1% 1.1%
Articulated FHV 1.1% 1.1% 1.1% 1.1% 1.1%
Articulated HHV 1.1% 1.1% 1.1% 1.1% 1.1%
Articulated BEV
Articulated H2FC 2.6% 2.6% 2.6% 2.6% 2.6%
Articulated Battery usable SOC for
electric range
All
70% 70% 80% 85% 90%
Articulated Ratio fuel cell size to max
power
H2FC REEV
Articulated % Max power ICE for
BEV
Articulated H2FC 85% 85% 85% 85% 85%
Articulated Other 125% 125% 125% 125% 125%
Construction New vehicle lifetime, years All 10 10 10 10 10
Construction ICE range (km) NG ICE 500 500 500 500 500
Construction Other 500 500 500 500 500
Construction Hydrogen range (km) H2FC 500 500 500 500 500
Construction Electric range (km) Petrol HEV 2 2 2 2 2
Construction Electric range (km) Diesel HEV 2 2 2 2 2
Construction Petrol PHEV
Construction Diesel PHEV
Construction Petrol REEV

Construction Diesel REEV
Construction H2FC 2 2 2 2 2
Construction H2FC PHEV
Construction H2FC REEV
Construction BEV
Construction Distance in fuel mode 1
(ICE/H2/NG) (%)
Petrol ICE
Construction Diesel ICE 100% 100% 100% 100% 100%
Construction Petrol HEV
Construction Diesel HEV 100% 100% 100% 100% 100%
Construction Petrol PHEV
Construction Diesel PHEV
Construction Petrol REEV
Construction Diesel REEV
Construction BEV
Construction H2FC 100% 100% 100% 100% 100%
Construction H2FC PHEV
Construction H2FC REEV
Construction NG ICE 100% 100% 100% 100% 100%
Construction Diesel FHV 100% 100% 100% 100% 100%
Construction Diesel HHV 100% 100% 100% 100% 100%
Construction DNG ICE 70% 70% 70% 70% 70%
Construction # New vehicles/year (UK
approx.)
All
7,361 7,729 8,116 8,522 8,948
Construction # Fleet vehicles (UK
approx.)
All
73,612 77,293 81,157 85,215 89,476
Construction ICE sizing as % Max
Power (inverse DOH)
ICE 100% 100% 100% 100% 100%
Construction HEV 75% 75% 75% 75% 75%
Construction FHV 80% 80% 80% 80% 80%
Construction HHV 80% 80% 80% 80% 80%
Construction BEV
Construction Basic real-world %
increase
ICE 9.0% 9.0% 9.0% 9.0% 9.0%
Construction HEV 10.1% 10.1% 10.1% 10.1% 10.1%
Construction FHV 10.1% 10.1% 10.1% 10.1% 10.1%
Construction HHV 10.1% 10.1% 10.1% 10.1% 10.1%
Construction BEV
Construction H2FC 11.5% 11.5% 11.5% 11.5% 11.5%
Construction Battery usable SOC for
electric range
All
70% 70% 80% 85% 90%
Construction Ratio fuel cell size to max
power
H2FC REEV
Construction % Max power ICE for
BEV
Construction Other 125% 125% 125% 125% 125%
Bus New vehicle lifetime, years All 15 15 15 15 15
Bus ICE range (km) NG ICE 500 500 500 500 500
Bus Other 500 500 500 500 500
Bus Hydrogen range (km) H2FC 500 500 500 500 500
Bus Electric range (km) Petrol HEV 2 2 2 2 2
Bus Electric range (km) Diesel HEV 2 2 2 2 2
Bus Petrol PHEV
Bus Diesel PHEV
Bus Petrol REEV
Bus Diesel REEV
Bus H2FC 2 2 2 2 2
Bus H2FC PHEV
Bus H2FC REEV

Bus BEV 160 200 240 280 320
Bus Distance in fuel mode 1
(ICE/H2/NG) (%)
Petrol ICE
Bus Diesel ICE 100% 100% 100% 100% 100%
Bus Petrol HEV
Bus Diesel HEV 100% 100% 100% 100% 100%
Bus Petrol PHEV
Bus Diesel PHEV
Bus Petrol REEV
Bus Diesel REEV
Bus BEV 100% 100% 100% 100% 100%
Bus H2FC 100% 100% 100% 100% 100%
Bus H2FC PHEV
Bus H2FC REEV
Bus NG ICE 100% 100% 100% 100% 100%
Bus Diesel FHV 100% 100% 100% 100% 100%
Bus Diesel HHV 100% 100% 100% 100% 100%
Bus DNG ICE 60% 60% 60% 60% 60%
Bus # New vehicles/year (UK
approx.)
All
5,352 8,429 8,851 9,293 9,758
Bus # Fleet vehicles (UK
approx.)
All
80,280 84,294 88,509 92,934 97,581
Bus ICE sizing as % Max
Power (inverse DOH)
ICE 100% 100% 100% 100% 100%
Bus HEV 75% 75% 75% 75% 75%
Bus FHV 80% 80% 80% 80% 80%
Bus HHV 80% 80% 80% 80% 80%
Bus BEV 0% 0% 0% 0% 0%
Bus H2FC 0% 0% 0% 0% 0%
Bus Basic real-world %
increase
ICE 8.8% 8.8% 8.8% 8.8% 8.8%
Bus HEV 9.9% 9.9% 9.9% 9.9% 9.9%
Bus FHV 9.9% 9.9% 9.9% 9.9% 9.9%
Bus HHV 9.9% 9.9% 9.9% 9.9% 9.9%
Bus BEV 11.4% 11.4% 11.4% 11.4% 11.4%
Bus H2FC 11.4% 11.4% 11.4% 11.4% 11.4%
Bus Battery usable SOC for
electric range
All
70% 70% 80% 85% 90%
Bus Ratio fuel cell size to max
power
H2FC REEV
Bus % Max power ICE for
BEV
85% 85% 85% 85% 85%
Bus H2FC 85% 85% 85% 85% 85%
Bus Other 125% 125% 125% 125% 125%
Coach New vehicle lifetime, years All 15 15 15 15 15
Coach ICE range (km) NG ICE 500 500 500 500 500
Coach Other 500 500 500 500 500
Coach Hydrogen range (km) H2FC 500 500 500 500 500
Coach Electric range (km) Petrol HEV 2 2 2 2 2
Coach Electric range (km) Diesel HEV 2 2 2 2 2
Coach Petrol PHEV
Coach Diesel PHEV
Coach Petrol REEV
Coach Diesel REEV
Coach H2FC 2 2 2 2 2
Coach H2FC PHEV
Coach H2FC REEV
Coach BEV
Coach Distance in fuel mode 1
(ICE/H2/NG) (%)
Petrol ICE
Coach Diesel ICE 100% 100% 100% 100% 100%

Coach Petrol HEV
Coach Diesel HEV 100% 100% 100% 100% 100%
Coach Petrol PHEV
Coach Diesel PHEV
Coach Petrol REEV
Coach Diesel REEV
Coach BEV
Coach H2FC 100% 100% 100% 100% 100%
Coach H2FC PHEV
Coach H2FC REEV
Coach NG ICE 100% 100% 100% 100% 100%
Coach Diesel FHV 100% 100% 100% 100% 100%
Coach Diesel HHV 100% 100% 100% 100% 100%
Coach DNG ICE 70% 70% 70% 70% 70%
Coach # New vehicles/year (UK
approx.)
All
3,568 5,620 5,901 6,196 6,505
Coach # Fleet vehicles (UK
approx.)
All
53,520 56,196 59,006 61,956 65,054
Coach ICE sizing as % Max
Power (inverse DOH)
ICE 100% 100% 100% 100% 100%
Coach HEV 75% 75% 75% 75% 75%
Coach FHV 80% 80% 80% 80% 80%
Coach HHV 80% 80% 80% 80% 80%
Coach BEV
Coach H2FC 0% 0% 0% 0% 0%
Coach Basic real-world %
increase
ICE 9.0% 9.0% 9.0% 9.0% 9.0%
Coach HEV 10.1% 10.1% 10.1% 10.1% 10.1%
Coach FHV 10.1% 10.1% 10.1% 10.1% 10.1%
Coach HHV 10.1% 10.1% 10.1% 10.1% 10.1%
Coach BEV
Coach H2FC 11.5% 11.5% 11.5% 11.5% 11.5%
Coach Battery usable SOC for
electric range
All
70% 70% 80% 85% 90%
Coach Ratio fuel cell size to max
power
H2FC REEV
Coach % Max power ICE for
BEV
Coach H2FC 85% 85% 85% 85% 85%
Coach Other 125% 125% 125% 125% 125%
Motorcycle New vehicle lifetime, years All 12 12 12 12 12
Motorcycle ICE range (km) H2ICE 150 175 200 225 250
Motorcycle Other 300 300 300 300 300
Motorcycle Hydrogen range (km) H2FC 200 225 250 275 300
Motorcycle Electric range (km) Petrol HEV 2 2 2 2 2
Motorcycle H2FC 2 2 2 2 2
Motorcycle BEV 50 75 100 125 150
Motorcycle Distance in fuel mode 1
(ICE/H2/NG) (%)
Petrol ICE 100% 100% 100% 100% 100%
Motorcycle Petrol HEV 100% 100% 100% 100% 100%
Motorcycle BEV 100% 100% 100% 100% 100%
Motorcycle H2FC 100% 100% 100% 100% 100%
Motorcycle # New vehicles/year (UK
approx.)
All
100,090 100,090 100,090 100,090 100,090
Motorcycle # Fleet vehicles (UK
approx.)
All
1,234,369 1,234,369 1,234,369 1,234,369 1,234,369
Motorcycle ICE sizing as % Max
Power (inverse DOH)
ICE 100% 100% 100% 100% 100%
Motorcycle HEV 75% 75% 75% 75% 75%
Motorcycle BEV 0% 0% 0% 0% 0%
Motorcycle H2FC 0% 0% 0% 0% 0%
Motorcycle Basic real-world % ICE 30.2% 30.2% 30.2% 30.2% 30.2%

Motorcycle increase HEV 30.2% 30.2% 30.2% 30.2% 30.2%
Motorcycle BEV 24.6% 24.6% 24.6% 24.6% 24.6%
Motorcycle H2FC 24.6% 24.6% 24.6% 24.6% 24.6%
Motorcycle Battery usable SOC for
electric range
All
70% 70% 80% 85% 90%
Motorcycle Ratio fuel cell size to max
power
H2FC REEV
Motorcycle % Max power ICE for
BEV 85.1% 85.1% 85.1% 85.1% 85.1%
Motorcycle H2FC 85.1% 85.1% 85.1% 85.1% 85.1%
Motorcycle Other 124.8% 124.8% 124.8% 124.8% 124.8%

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