Modelling and simulation of driving cycle using simulink

International Journal of Power Electronics and Drive Systems (IJPEDS)
Vol. 12, No. 3, September 2021, pp. 1450~1458
ISSN: 2088-8694, DOI: 10.11591/ijpeds.v12.i3.pp1450-1458  1450
Journal homepage: http://ijpeds.iaescore.com
Modelling and simulation of driving cycle using simulink
S. K. Arun1
, I. N. Anida2
, J. S. Norbakyah3
, A. R. Salisa4
1,2,3,4
Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, Terengganu, Malaysia
3,4
Renewable Energy and Power Research Interest Group (REPRIG), Universiti Malaysia Terengganu, Terengganu,
Malaysia
3,4
Energy Storage Research Group (ESRG) Universiti Malaysia Terengganu, Terengganu, Malaysia
Article Info ABSTRACT
Article history:
Received Apr 19, 2021
Revised Jun 25, 2021
Accepted Jul 19, 2021
Driving cycle is commonly known as the relationship and a series of speed-
time profile. The study on this discipline aids vehicle manufacturers in
vehicle construction, environmentalists in studying environment quality in
proportion with vehicle emissions and traffic engineers to further investigate
the behaviour of drivers and the road conditions which assist automotive
industry in a better and energy efficient vehicle productions. In order to
develop a proper driving cycle for selected routes, information and data
based on real-time driving behaviour is important. This research focusses on
the modelling of each component and latter designing a conceptual model in
Simulink which takes up the data of speed of vehicles in SI unit which is m/s
and draws out distance travelled and acceleration of the vehicle together with
driving cycle of the route for given timestamp. This relation will be verified
with existing Kuala Terengganu BasKITe driving cycle, highway fuel
economy test (HWFET), new europian driving cycle (NEDC) and worldwide
harmonised light vehicle test procedure (WLTP) driving cycles for the use of
future projects and improvements of technology in studies and analysis of
powertrain and electric vehicle performances.
Keywords:
Driving cycle
Hybrid electric vehicles
Iot
MATLAB/Simulink
Road profile
Simulation
This is an open access article under the CC BY-SA license.
Corresponding Author:
A. R. Salisa
Faculty of Ocean Engineering Technology and Informatics
Universiti Malaysia Terengganu
21030 Kuala Nerus, Terengganu, Malaysia
Email: salisa@umt.edu.my
1. INTRODUCTION
Automotive industries face some challenges and pressure to reduce their development times when
they are about to innovate and produce unique products. According to James [1], the need tor educe
emissions in automotive sectors provides a more related and connected experiences and demand for
autonomous and simulators. Driving cycle plays an important role in automotive sector where engineers
requires driving cycle to analyze road condition, road profile, driving behavior, fuel consumptions, fuel
emissions and road conditions and latter aids vehicle manufacturing sectors in more efficient vehicle
productions [2]-[5]. Driving cycle is a profile which represents a speed-time relationship in which can be
used to draw out fuel economy of a vehicle [6]-[10]. According to Polizanno and Saletti [11], working
efficiently is indispensable to success in a globalized market, especially for high-tech industries such as
automotive, aerospace and communications, where electronic controls are a vital part of each new product.
The increasing demand of simulation and software use in application domains has resulted in a staggering
complexity that has proven difficult to manage with conventional design approaches. Due to the ability of
embedded software and simulations to determine complexity and challenges, model-based designs and

Int J Pow Elec & Dri Syst ISSN: 2088-8694 
Modelling and simulation of driving cycle using Simulink (S. K. Arun)
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modelling is becoming vital among engineers. As stated by Hayfield [12], automotive industries are moving
towards new decades where vehicle autonomy had been one of the important improvements needed the most
in which the use of technologies are vital to automate and simulate some or all driving functions. As time and
technology is proceeding, the amount of work modern software has to perform is always growing more, thus
resulting in the amount of complexity of the programs and software becoming bigger. Mainly for automotive
simulation application, a product of MathWorks also well-known as Simulink is widely used as it has the
ability and functions of powertrain block set, vehicle dynamics block set, automated driving toolbox and
model-based calibration toolbox. Some of the models which are constructed using Simulink are fault-tolerant
fuel control system, data management for a fuel control system using data dictionary, engine timing using
triggered subsystems, engine timing model with closed loop control, building a clutch lock-up model, anti-
lock braking system, automotive suspension. This research and study on Driving Cycle discipline uses
common block set in Simulink such as signal builders and subsystems to do interpolation of signals and
produce driving cycle relation by mean of integration and derivation from speed.
2. SIMULINK
Simulink is a product of MATLAB that provides interactive modelling, simulating and analysing
system in the form of graphical and block interface. It enables quick and effective production and innovation
of virtual prototypes to explore design to any extent with minimal effort. According to Baloi et al. [13],
Simulink provides a graphical user interface (GUI) for building models as block diagrams and it includes a
library or defined blocks which can be used to design simulation as desired. Besides, user can also define
blocks separately with custom formulas according to required applications in subsystems which ease the
production of simulation designs
2.1. Simulation applications in the automotive industry
According to Zeigler et al. [14], simulation analyses are divided into two major subcategories;
discrete-event simulation and continuous simulation Continuous simulation studies processes amenable to
analysis using differential equations in which can be applied in the field of ecological systems, chemical
synthesis and manufacturing, refinaries and aerodynamic system designing whereas discrete-event simulation
studies processes in which most of the used variables are integers and not amenable to be used in continuous
equations. According to Miller and Pegden [15], individuals tend to learn simulation quicker and understood
that simulation is a delightfully quick, inexpensive, and non-disruptive alternative to the potential purchase,
installation, and integration of expensive machines or material-handling equipment. In automotive industry,
simulation allows manufacturers to imagine new systems and enables them to estimate the final outcome if
for all models and designs made and also to observe the behaviour. Whether the system is a production line,
operating system, control system or an emergency response system, simulation and modelling can be used
conduct pre assessment on the suystem they are about to built and collect data and information to move
further or before constructing a prorotype of a device, machine or a system in vehicles.
Several automotive related simulation projects were conducted all over. Firstly, Law and McComas
[16] discussed steps of a sound simulation study and secrets of a successful industrial simulation. A case
study to select the best system from a few proposed systems related to production using simulation were
described and discussed by Scriber [17]. Besides, Azadeh and Farid [18] comes up with an integrated
simulation model which generates optimization alternatives for heavy rolling mills for a steel-making factory.
Patel et al. [19], studied on the methodology of modelling and research on final process systems of
automobile processes to develop efficient process to ensure quality output of the system. Choi et al. [20],
discussed the initial efforts to implement simulation modelling as a visual management and analysis tool at
automotive foundry plant manufacturing engine blocks. To add on to that, Potoradi et al. [21], described
simulation engine can be run in parallel with a large number of products on wire-bond machines. Altiparmak
et al. [22], improvised analysis and decision-making processes by using simulation metamodels of an
asynchronous assembly system in buffer size optimization. Last but not least, Norbakyah and Salisa [23],
conducted a study on plug-in hybrid recreational boat (PHERB) powertrain with a special energy
management strategy modelling and analysis using Simulink. To add to that, Williams and Ülgen [24],
divides automotive simulation into two categories which is manufacturing simulatyion and automotive
industru simulation. According to Edward, manufacturing simulation is one of the first major application
areas of discrete-event process simulation whereas automotive industry involves them in a very large supply
chain apex.

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2.2. Conceptual design in Simulink
According to Daltrophe et al. [25], function blocks can always be inter-transformed and
interchanged to suit the application of automation and autonomous system which is in line with the standard
of IEC61499 in order to validate the complexity of the simulating environment. In this study, a few blocks
from Simulink library were used such as digital clock, signal builder, hit crossing, saturation, rate transition,
integration and derivative blocks, subsystems, basic operation blocks, gain, mux, bus creator and selector,
scope and on-screen scope. A database in form of Excel file with speed data will be uploaded into signal
builder and the stop time of the simulation will be amended according to applications. Signal Builder >
Import from files > Browse > choose file > OK. Example of file import is as per Figure 1.
Figure 1. Data file import
3. RESULT AND DISCUSSION.
3.1. Conceptual design in Simulink
The development of Simulink design begun with the analyis on the availability of the data collected
such as speed, acceleration, displacement of distance travelled. In this conceptual design, only speed data is
required where distance travelled will be calculated by integrating speed and the data of acceleration will be
obtained through the first derivative of speed. The formulas of calculation varies depending on availability of
types of data. After obtaining three required data which is distance, speed and acceleration, interpolation of
distance and speed data is done in a subsystem. According to Lepot et al. [26], interpolation of data is carried
out to fill gaps in time-series efficiency criteria and incertainty quantifications. In addition, Daltrophe et al.
[27], ecxplained an efficient sampling alternative for a sensor aggregated data points using big data
interpolation. In this research, data interpolation is important to filter out unnecessary data points and to fill
in gaps between points with relevant points as a method to mitigate noises and irrelevant errors due to high
sensitivity of the sensor. The main use of data interpolation is to aid the study and analysis of data which
might exist outside their collected data. Interpolation is also frequently used to convert the sampling rate of
digital signals to estimate an unknown potential yield. Figure 2 shows the conceptual design of the
simulation. Distance is interpolated in the “Distance Interpolator” subsystem according to Formula 1 whereas
velocity is interpolated in the “Velocity Interpolator” subsystem according to Formula 2. Since magnitude
and vector is negligible in this case, data of speed will be used as velocity and distance will be used as
displacement.
𝑠(𝑛) +
𝑎(𝑛)
2
𝑑𝑇2
+ 𝑣(𝑛)𝑑𝑇 = 𝑠 (1)
𝑎(𝑛)𝑑𝑇 + 𝑣(𝑛) = 𝑣(𝑡) (2)

1453
Where, s is distance/displacement, m, v(t) is speed/velocity, m/s and (t) is acceleration, m/s2
.
Figure 2. Driving cycle conceptual design and model in Simulink
A “gain” block is used with multiplication of 1e-3 or equivalent to division of 1000 to convert the
unit of distance from m to KM. The unit for velocity is remained as m/s and the unit of acceleration is
converted from m/s2
to acceleration with gravitational force which is g-unit by multiplication of 1/9.80665.
The final 3 signals are connected to scope to be monitored and analyzed. Several established driving cycles
have been chosen to verify the conceptual design; Kuala Terengganu BasKITe driving cycle, HWFET,
NEDC and WLTP. Figures 3 (a)-(d), Figures 4 (a)-(d), Figures 5 (a)-(d) and Figures 6 (a)-(d) shows the
results obtained.
The first axis of simulation results shows distance travelled, second axis shows driving cycle
relation with time and third axis shows acceleration. This procedure is carried out by connecting collected
data into velocity interpolator and also to a separate scope in which interpolated signal and direct signal is
applied to compare the required and acquired data. In all the above figures, the acquired data overlaps
required data in which can be concluded as zero signals lost throughout the simulation. To prove this, as per
Figure 3 (c), Figure 3 (d), Figure 4 (c), Figure 4 (d), Figure 5 (c), Figure 5 (d), Figure 6 (c) and Figure 6 (d),
comparison was done at 25% and 75% of the total elapsed time for each driving cycle. For Kuala Terengganu
BusKITE driving cycle, comparison was done at the time of 655 and 1963 which gives ΔY=0. HWFET
driving cycle was compared at the time of 192 and 575. NEDC driving cycle was compared at the time of
295 and 885 whereas WLTP driving cycle was compared at the time of 450 and 1350. The end result for each
driving cycle gives ΔY=0 in which the constructed conceptual design does not lose any signal in accordance
throughout the simulation process. Signal lose identification is important in any simulation processes and
procedures as it may give out inaccurate and inconsistent final result. To ensure minimal signal losses in
simulation, it is crucial to identify types of blocks and functions being used, the rate of signal losses and also
appropriate insertion and return signal paths are chosen. In most cases of simulation, signals are often lost
during return process as a due part of the simulation consumes certain decibels of it during initialization. On
the other hand, the default input data into signal builder is in m/s, which is the SI unit of speed. Figure 7 are
some of the direct conversions using “gain” block. It is also advisable to convert the values in the Excel
datasheet before uploading into signal builder to ensure consistency of data points being generated and
simulated.

 ISSN: 2088-8694
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(a) (b)
(c) (d)
Figure 3. (a) Established kuala terengganu BasKITE driving cycle; (b)simulated kuala terengganu
BusKITE driving cycle; (c)measurement at time 655; and (d)measurement at time 1963
(a) (b)
(c) (d)
Figure 4. (a) Established HWFET driving cycle; (b) simulated HWFET driving cycle; (c) measurement at
time 192; and (d) measurement at time 57

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(a) (b)
(c) (d)
Figure 5. These figures are; (a) established NEDC driving cycle; (b) simulated NEDC driving cycle;
(c) measurement at time 295; and (d) measurement at time 885
(a) (b)
(c) (d)
Figure 6. These figures are; (a) established WLTP driving cycle; (b) simulated WLTP driving cycle;
(c) measurement at time 450; and (d) measurement at time 1350

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Figure 7. Direct conversion using “gain” block
4. CONCLUSION
In the nutshell, driving cycle conceptual designing and modelling in Simulink is successful and can
be wrapped up and verified since the result of the simulation gives zero error compared with four established
driving cycles which is Kuala Terengganu BusKITE, HWFET, NEDC and WLTP driving cycles. The
conceptual design therefore can be used in future to simulate driving cycles by uploading the data of speed of
vehicles into signal builder in the format of Excel to further analyse the emissions and driving behaviours of
individuals. The novelty of this research is proven as the use of Excel in daily life has increase drastically to
perform calculation, visualization and analysis of data and proven to be one of the platforms used by various
business and research sectors. Since there are no any researches conducted previously to link the use of Excel
and Simulink to construct driving cycle, this opportunity was taken to design a conceptual modelling of
driving cycle in which the relationship of speed and time can be used to analyze various information required
by traffic engineers and environmentalists. In accordance, Excel had been chosen to be used in this research
as the data collected to construct driving cycle can be tabulated and simulated at the same time and directly
uploaded into signal builder of conceptual design to undergo required processes.
ACKNOWLEDGEMENTS
The authors would like to be obliged to Ministry of Education Malaysia for providing financial
assistance under FRGS 2020 (59623) grant and Faculty of Ocean Engineering Technology and Informatics,
UMT for all their technical and research support for this work to be successfully completed.
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BIOGRAPHIES OF AUTHORS
S. K. Arun received the Bachelor of Applied Sciences (Electronic and Instrumentation
Physics) from Universiti Malaysia Terengganu in 2019 and currently pursuing his Master
Degree in Engineering and Engineering Studies (Electronics) in Universiti Malaysia
Terengganu. He is also currently working as an Instrumentation Engineer at Malay-Sino
Chemical Industries Sdn. Bhd. (Lahat). His main research interest is in instrumentation &
automation systems, control systems, energy management, driving cycle and
implementation of IoT in big data management.

 ISSN: 2088-8694
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I. N. Anida received the Bachelor of Engineering (Mechatronics) from International
Islamic University Malaysia, Gombak in 2015 her and Master Degree in Physics
(Renewable Energy) from Universiti Malaysia Terengganu in 2019. She currently pursuing
her Ph.D in Engineering (Energy and Electrical) Her main research interest is Plug in
Hybrid Vehicle, driving cycle, energy rate, emissions and implementation of IoT in big
data management.
J. S. Norbakyah received the Bachelor of Applied Science (Physics Electronic and
Instrumentation) and MSc and PhD from Universiti Malaysia Terengganu, Terengganu, in
2009, 2014 and 2019 respectively. She is currently working as a lecturer at Universiti
Malaysia Terengganu, Malaysia. Her main research interest is Electric Vehicle, Hybrid
Electric Vehicle, Plug in Hybrid Vehicle, Modelling and simulation and renewable energy.
A. R. Salisa received the B.E. and M.E. in Electrical & Electronics Engineering from
University of Technology Petronas, Perak, Malaysia in 2004 and 2006, respectively while
Ph.D. in Optimal Energy Management Strategy for the University of Technology Sydney
Plug-In Hybrid Electric Vehicles from University of Technology Sydney (UTS), Australia.
She is currently working as a senior lecturer at University Malaysia Terengganu, Malaysia.
Her research interests are in Hybrid Electric Vehicles, innovation powertrain, simulation
and modeling, energy management strategy, driving cycles, fuel economy, emissions and
optimization.

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