Simulation of adaptive power management circuit for hybrid energy harvester and real-time sensing application

International Journal of Power Electronics and Drive System (IJPEDS)
Vol. 11, No. 2, June 2020, pp. 658~666
ISSN: 2088-8694, DOI: 10.11591/ijpeds.v11.i2.pp658-666  658
Journal homepage: http://ijpeds.iaescore.com
Simulation of adaptive power management circuit for hybrid
energy harvester and real-time sensing application
M. S. Tamrin, M. R. Ahmad
Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Malaysia
Article Info ABSTRACT
Article history:
Received Nov 18, 2019
Revised Dec 22, 2019
Accepted Jan 28, 2020
Many wireless sensor network (WSN) applications, nowadays, require real-
time communication, which demands cautious design consideration to
resolve inherent conflicts between energy efficiency and the need to meet
Quality of Services (QoS), such as end-to-end delay communications.
Numerous innovative solutions are proposed such as Real-time Power-Aware
Routing (RPAR) protocol, which dynamically adapts transmission power to
meet specified communication delays at low energy cost. Hence, to enable
real-time communication with RPAR protocol, an adaptive Power
Management Circuit (PMC) using hybrid energy harvester to support WSN
real-time communication is proposed. In this paper, a high-level architecture
of the proposed PMC is discussed, which consists of Thermal Energy
Generator (TEG), and Piezoelectric Energy Harvester (PEG) as energy
providers, with low-power Maximum Power Point Tracking (MPPT) feature
enabled. Preliminary simulations which analyze and characterize TEG and
PEG system are conducted separately to determine the optimal design
parameters to support the conventional WSN QoS requirement. Next, both
systems will be integrated into a single PMC implementation prior to
fabrication and lab characterization.
Keywords:
Fractional open-circuit voltage
Hybrid energy harvester
Piezoelectric energy
Power management circuit
Thermal energy
This is an open access article under the CC BY-SA license.
Corresponding Author:
M. S. Tamrin
Department of Electrical and Electronic Engineering,
Universiti Teknologi PETRONAS,
Seri Iskandar, 32610 Perak, Malaysia.
Email: shahritamrin@gmail.com
1. INTRODUCTION
With the recent advancement in wireless sensor applications, such as Internet of Things (IoT),
ambience intelligence, and wireless sensor networks (WSN), it is expected to have 50 billion of sensor nodes
by the year 2025 [1, 2]. These large number of sensor nodes are normally required to operate for a long
period of time, ranging from months to years. In addition to that, many WSN applications require real-time
communication. For example, a surveillance monitoring system needs to alert authorities instantaneously
(or within few seconds) of suspicious intruders. However, supporting real-time communications in WSNs is
very challenging due to inherent conflict between stringent requirement of end-to-end delay and energy
efficiency [3, 4]. All these challenges require a very careful design consideration to manage energy resources.
Batteries are commonly used to power up WSN sensors. Although they are designed to be low-
power, eventually their batteries will be drained out, and are required to be replaced, hence incurring high
maintenance cost. Therefore, energy harvesting devices have now become a preferred solution to provide
cheap, clean, and unlimited alternative energy. Conventionally, energy harvesting system is mainly built
upon three major components. First component is energy generator, which is used to harvest energy from

Int J Pow Elec & Dri Syst ISSN: 2088-8694 
Simulation of adaptive power management circuit for hybrid energy harvester … (M. S. Tamrin)
659
ambiance, and convert it into electrical energy (e.g. piezoelectric, photovoltaic, and thermoelectric). Second
component is called energy capture & storage, which consists of power management circuit and storage. The
last component is energy consumption, which varies based on usage condition.
However, due to relatively low output power generated by standalone energy harvester, the focus of
research area has now shifted from standalone to Hybrid Energy Harvester (HEH). HEH is basically a
technique to integrate multiple energy sources into a single system, which is capable to generate higher
output power as compared to standalone harvester. The input energy can come from different sources, such
as vibration, thermal, light, acoustic, solar, wind, radio frequency etc. Cao et al. [5] has listed down clearly
conventional energy sources, and its corresponding power density and harvesting method. However, one of
the key challenges for any HEH system is to design a Power Management Circuit, which can provide
efficient and seamless output power to any load applications.
Over the years, many techniques have been developed to design an efficient PMC for standalone
energy harvesters. In this section, we will discuss on PMC techniques used for thermal and vibration-based
energy harvesters, followed by brief introduction of Maximum Power Point Tracking (MPPT) techniques,
and last but not least, discussion on common architectures used in HEH systems. The main objectives of this
paper are; to propose a new adaptive PMC architecture for HEH system to support real-time WSN, with
overall efficiency of >80%, and to characterize TEG, and PEG sub-system separately through simulation.
Thermal-based PMC, A common example of thermal-based PMC design approach is through the
integration of inductor-based boost converter and switched-capacitor buck converter into an integrated chip
[6]. This Energy Harvesting IC (EHIC) is fabricated with 65 nm CMOS, with area less than 0.5 mm2
. The
proposed PMC is basically divided into two stages: Power Conversion, for which inductor-based converter is
used to boost low input voltage to a suitable output voltage, with Zero Current Switching (ZCS) capability to
improve efficiency during low current mode, and Power Management Unit, in which switched capacitor
converter is used to regulate output voltage into multiple levels. In addition to that, in low energy condition,
PMC is able to bypass switched capacitor, and the load is getting direct supply from inductor-based
converter. The peak efficiency, however, varies with output voltage.
An improved version of thermal-based PMC is proposed in [7]. The Boost LC filter is equipped
with a feedback loops to regulate the output voltage. Any difference between output and desired voltage is
amplified and then modulated to Pulse Density Modulation (PDM) signal, which is used to control on/off of
MOS power switch. The charge pump circuitry is added to reduce losses across MOS switch, by increasing
the voltage of PDM signal. This PMC technique comes with feedback control loop. To achieve theoretically
infinite lifetime, a system is expected to run in Energy Neutral Operation (ENO). Hence, a low-complexity
Power Manager (PM) is proposed, [8] which is used to adapt with its computational load to ensure the
consumed energy is equal to the harvested energy. Only if this condition meets, the system is considered to
achieve ENO. The proposed PM is basically deciding and changing duty cycle of the load, according to the
estimation of harvested and consumed energy, provided by a low-power super-capacitor-based
energy monitor.
Typically, a desired voltage of super-capacitor when the load has converged to ENO is pre-defined.
This method is very suitable for long-term monitoring applications, but not for real-time sensing application,
which requires stringent end-to-end delay requirement. While majority of thermal-based PMC techniques
employ DC-to-DC boost converters, a simple and novel implementation of thermal PMC is proposed in [9],
which requires no complex start-up circuitry (e.g. charge pump) or typical DC-to-DC boost inductor, because
TEG used in this system is capable to generate high enough voltage to drive load application directly. This
PMC basically consists of two main Positive-channel MOS (PMOS) switches, MPPT control block, and one
Storage Capacitor. The “brain” of this PMC design lies within MPPT control block, which is capable to
control active/sleep state of the load, hence minimizing any power mismatch between TEG devices and load
application. Therefore, this PMC can successfully achieve very high peak power efficiency, which is ~95%.
Vibration-based PMC Kong et al. [10] presented a simple and self-powered PMC, which does not
require additional power to operate. The PMC is basically divided into four major components: 1) AC-to-DC
converter, using full-bridge rectifier. 2) Buck-Boost Convertor which is running at Discontinuous Current
Mode (DCM) is implemented to support wide range of input voltage as well as to match source impedance.
3) ultra-lower micro-controller, which is configured for MPPT and output voltage regulation. 4)
Supercapacitor as storage device. The MPPT technique used for this PMC is called impedance matching.
Besides, Elie et al. [11] developed a single-switch inductorless PMC for electrostatic vibration
harvester, which consists of a single switch, one biasing capacitor (Cbias), and three diodes. Charging or
discharging state of the Cbias is controlled by this single switch. This PMC design operates in two modes: 1)
Charging of Cbias, where single switch is in “open” state 2) Charging of Battery, for which single switch is in
“closed” state. One great advantage of this PMC design is that it can be embedded in miniaturized system
since it does not require inductor component.

 ISSN: 2088-8694
Int J Pow Elec & Dri Syst, Vol. 11, No. 2, June 2020 : 658 – 666
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Maximum Power Point Tracking, when discussing efficiency for energy harvesting devices, one
should always consider the followings: 1) conversion efficiency; 2) transfer efficiency; 3) buffering
efficiency; and 4) consumption efficiency. In generic term, transfer efficiency considers amount of loss due
to power transfer activity from energy source to load. Hence, there is a technique, called Maximum Power
Point Tracking (MPPT), which is typically used to minimize power mismatch between energy source and
load [13]. Currently, there are several MPPT techniques developed. One of them is leveraging digital signal
processing [12]. Another MPPT technique is based on impedance matching [14, 15]. This approach is not
only simple to implement, but also consume very low power. The basic idea is to match between input
impedance, which varies from one energy generator device to another, and inductor value of boost converter.
However, due to huge tolerance of inductor itself, this technique is not be very accurate. Another MPPT
technique used is called fractional open-circuit voltage (FOC) [16-18]. The open-circuit voltage (Voc) of a
specific energy generator is sampled periodically to determine its maximum power point voltage (VMPP),
leaving this energy generator electrically open. In this paper, we will focus on FOC technique as it is proven
to achieve ~99.5% of efficiency.
Common PMC Architectures for HEH System, There are few prior works done to design PMC for
HEH system. The simplest method is called “complementary,” as defined by [19], which mainly collects
energy from a primary source, while secondary transducers is merely used to power up auxiliary circuitry,
such as biasing and boot-up circuitry. As shown in Fig. 5, the Piezoelectric Generator is used during cold
start-up, in which at that time, the power generated by the Thermal Generator is not sufficient to sustain the
whole operation. However, this simple method does not implement maximum power point tracking (MPPT),
which is crucial to ensure maximum harvesting efficiency. Besides, another common technique to harvest
multiple source of energy is called “Power ORing” architecture [20-22], which offers modular approach,
connecting multiple sources in parallel through diodes. Each of energy sources is independent from each
other, hence could perform its own MPPT technique. The diodes is used to ensure self-synchronized
operation. However, there are several disadvantages linked to this method. First, there is additional power
loss due to forward voltage drop on the diodes, as well as having independent MPPT could increase cost and
size of the system. The “Power ORing,” architecture.
Last but not least, another more sophisticated technique commonly used in managing multisource
energy harvesting is called voltage level detection method. For example, in [23] the charging of a micro
battery is taken either from the voltage generated by a thermal or RF harvesting subsystem, depending on
which exhibits higher voltage value. However, this cannot be applied for loads that require constant voltage
supply [24]. In [25], an improved technique is used: each input is connected to the output at pre-defined
period of time, provided its respective voltage surpasses the determined voltage threshold.
2. RESEARCH METHODOLOGY
2.1. Specification definition
Since each and every WSN applications has its own requirement, determining specific Quality of
Services (QoS) is a crucial step. Table 1 shows a QoS for specific Structural Montoring Application.
Table 1. QoS for structural monitoring application [8]
Efficiency
(%)
Nominal Voltage
(v)
Average Current Requirements (A)
Sleep
Active
(Normal)
Active (Performance)
Quality of Service
(QoS)
>80 3.5 175e-6 3.5e-3 Min. 20% higher than normal
operation
3.2. Architecture Definition
In this paper, we propose an adaptive PMC, which is capable to integrate inputs from two energy
harvesters, which are TEG & PEG. This proposed PMC integrates some of the elementary techniques from
the previous works into one single architecture. For example, the proposed PMC applies basic
“complementary” technique to meet specified workload requirement. For structural monitoring application,
which stays in sleep mode for most of the time, TEG is used to provide energy during sleep mode, while PEG
is during active mode. Besides, the proposed PMC also implements MPPT technique for TEG sub-system.
Unlike “Power ORing,” the TEG and PEG is not independent from each other - they are controlled by Pulse
Generator, which already have pre-determined timing information between sleep and active mode. This Pulse
Generator is used to control the “ON” and “OFF” state for each sub-system.

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The high-level architecture of proposed PMC design is shown in Figure 1. The proposed PMC
architecture consists of several main blocks, and components. For example, Pulse Generator is used to
control switching between TEG and PEG supply. The pulse is generated based on the loading conditions, i.e.
sleep mode = TEG is activated; active mode = PEG is activated. Enable Generator block, which consists of
2-stage comparator and latch circuitry is critical to ensure TEG system operates within tolerated supply,
hence maximizing power transfer from TEG harvester to the load. PWM Generator, and the Error Amplifier
are used as a control loop to ensure PEG system is regulated into desired voltage. Full-wave rectifier,
together with ripple-cancellation capacitor, C1, are the major component of the AC-to-DC voltage converter.
Comparator and Phase Delay blocks are added to ensure seamless transition between TEG and PEG
subsystem
Figure 1. High-level diagram of the Proposed PMC Architecture
Details implementation for each power states (i.e. sleep/active) is described by flowcharts in Figure
2 and Figure 3.
Figure 2. Proposed architectural flow
during active mode
Figure 3. Proposed architectural flow
during sleep mode
The main challenges to design PMC for HEH system is to integrate sub-systems seamlessly.
Therefore, to ensure smooth transition, the PMC required definite overlapped time between two-subsystems,
as shown in the Figure 4.

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Figure 4. Power-state transition policy
3. RESULTS AND ANALYSIS
A feasibility study of the proposed PMC architecture is carried out using HSPICE software 1) To
characterize design parameters for PEG system in order to meet QoS during active mode; 2) To analyze
feasibility of TEG to meet efficiency target.
3.1. Characterization of PEG
PEG is basically a generator that converts vibration generated from steps into electrical energy. In
this [26]. PEG system typically consists of two parts: AC-to-DC converter, and DC-to-DC boost converter.
For AC-to-DC converter, we are evaluating for optimal capacitor to minimize voltage ripple. Meanwhile, for
boost converter, we are tuning duty cycle, switching frequency, and inductor to find the optimal solutions.
For this simulation, we assumed 5-layer piezoelectric thick films as our PEG device, which has 5.3x higher
output current vs. single layer PEG device, 34x smaller matching impedance [27].
First, we characterized the rectifying circuit. Figure 5 shows the converted DC voltage, together
with its corresponding voltage ripple for varying capacitance value. Based on this results, minimum
capacitance of 10uF is required to meet sub-mV voltage ripple. We, then characterized DC-DC boost
converter by sweeping crucial design parameters. Figure 6 to Figure 7 show sensitivitities of different design
parameters such duty cycle, switching frequency, and inductor values.
Figure 5. Voltage ripple due to AC-to-DC conversion

663
Figure 6. Duty cycle sensitivity study for DC/DC boost converter
Figure 7. Switching freq sensitivity study for DC/DC boost converter
Based on the preliminary simulation results, a recommendation for optimal design of the PEG
system to meet specified QoS is shown in Table 2.
Table 2. Recommended design parameters for peg system
Design Paramaters Proposed Value
(for normal active operation)
Qos Simulated Results
Duty Clycle 0.10 Output Voltage 3.55 V
Switching Frequency 150 KHz Output Current 3.55 mA
Inductor 22 uF Output Power 12.3 mA
Output Capacitor 47 uF Inductor Current 96 mA
4.1. Characterization of TEG
Unlike the PEG System, TEG efficiency is very crucial to ensure we can meet >80% efficiency
requirement. Based on Yoon, et al. [7], due to the linear I-V characteristics of TEG, Maximum Power Point
(MPP), for each TEG device can easily be determined by (1) and (2).
Vmpp = 1/2Voc (1)
Pmax= Voc^2/4RT (2)
Hence, by knowing the RT & Voc of any TEG device, we can determine its corresponding Vmpp.
Figure 8 shows MPP Voltage (Vmpp) for TEG device with Voc = 6 V and RT = 300 KOhm.

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Figure 8. Maximum power point versus Voc.
Figure 9 shows that maximum power can be harvested from this speficic TEG is ~3.5 V, meaning
that if we can regulate our TEG system to ~3.5 V, our power efficiency will be almost 100%. However,
achieveing 100% efficiency is nevertheless unrealistic. Hence, for this study, we are only targeting efficiency
of >80%, which is typical for buck-boost platform regulator. Therefore, we can allow Vsto1 to be varied
slightly from its optimal Vmpp.
Figure 9. Projected power efficiency versus Voc.
Other than that, for this architecture to work decently, we need to guarantee PEG can sustain sleep
current for long period of time. This is actually related to capacitor selection of Csto1. The bigger the
capacitor, the longer it can sustain. However, this could impact the charging time of capacitor as well. Figure
10 shows linear correlation between sustain time vs. capacitance value, assuming Vsto1 varies between 4.25
V and 2 V (i.e. >80% efficiency) for 175 uA loading current.

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Figure 10. Capacitance vs. sustain time for 175ua load current.
4. CONCLUSION
In summary, based on preliminary simulation results, we can see line of sight to meet conventional
WSN QoS requirement for structural monitoring application. Optimal design parameters for PEG has also
been identified to support output current of 3.5 mA during active state. Besides, TEG is projected to sustain
output current of 175 uA, which is required during sleep state, for >600 ms, with efficiency of >80% using
47 uF capacitor. In addition to that, relationship of output voltage of TEG to power efficiency has also been
characterized in this paper. Next step is to integrate both systems and to implement our power-state transition
policy, which is key component of our proposed PMC architecture.
ACKNOWLEDGEMENTS
Authors would like to acknowledge the supports given by the Electrical and Electronic Department
of Universiti Teknologi PETRONAS in providing the facilities to perform the simulations and not to forget
the Yayasan UTP for providing the funding.
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BIOGRAPHIES OF AUTHORS
Mohamad Shahrir bin Tamrin obtained his Bachelor of Electrical Engineering (Hons) in 2011
from Stevens Institute of Technology USA. He is currently holding a position of Analog
Engineer in Intel Malaysia for the past 8 years. On top of that, he is also currently pursuing a
MSc in Electrical Engineering from Universiti Teknologi PETRONAS Malaysia. His research
interest is mainly on Power Management Circuit for Hybrid Energy Harvesting System.
Dr. Mohamad Radzi Ahmad received his BSc. in Electrical Engineering from Purdue University
School of Engineering and Technology Indianapolis USA in 1991, MSc. in Electronics System
Design Engineering from Universiti Sains Malaysia in 2010, and PhD in Electrical and
Electronics Engineering from Universiti Teknologi PETRONAS Malaysia in 2014. He joined
Universiti Teknologi PETRONAS in 2016, where he is currently a Senior Lecturer in the
Electrical and Electronic Engineering Department. Prior to joining UTP, Dr Radzi had 20 years
of professional experience with electronic industries such as Intel, Avago, Western Digital and
Fairchild Semiconductor, and held various positions ranging from Senior Manager to Product
R&D Engineer.

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