Acta Polytechnica


https://doi.org/10.14311/AP.2025.65.0371
Acta Polytechnica 65(4):371–394, 2025 © 2025 The Author(s). Licensed under a CC-BY 4.0 licence

Published by the Czech Technical University in Prague

ENERGY MANAGEMENT FOR ELECTRIC VEHICLES WITH
BATTERY AND SUPERCAPACITOR

Abhishek Shankar Bhagata, Vaiju Kalkhambkara,∗,
Pranda Prasanta Guptab, Vivek Prakashc

a Shivaji University, Rajarambapu Institute of Technology, Department of Electrical Engineering,
415409 Maharashtra, India

b GLA University, Department of Electrical Engineering, Mathura, 281406 Uttar Pradesh, India
c Bansthali Vidyapith, School of Automation, Tonk, 304022 Rajasthan, India
∗ corresponding author: kvaijnath@gmail.com

Abstract. This paper proposes an energy management strategy for battery and supercapacitor hybrid
energy storage systems for electric vehicles. The main objective of the hybrid energy storage systems is
to extend the durability of the battery pack by minimising peak currents of the battery during the
charging and discharging of the battery in high power demand operations. During regenerative braking,
energy is captured in the supercapacitor and is later used in high-power demand operations. In the
proposed approach, energy consumption is reduced, the size of the battery pack is reduced, and the
vehicle range is extended. The approach is based on a simple rule of power splitting in average, peak,
and regenerative modes. The proposed hybrid energy management system is implemented and tested
in MATLAB Simulink environment for different standard drive cycles.

Keywords: Hybrid energy storage system, energy management system, supercapacitor, regenerative
braking, electric vehicle.

1. Introduction
Internal combustion engine vehicles are a significant
source of environmental pollution and present a chal-
lenge for the automotive industry. Electric tech-
nologies like HEVs have been developed with var-
ious energy storage options to address this issue,
though some still depend on internal combustion en-
gines [1]. Initial studies show that EVs can over-
come these obstacles and meet the fuel economy de-
mands if battery designers collaborate with the devel-
opment team to improve power and energy density
and battery cycle life [2]. Electric vehicles are con-
sidered a promising solution for sustainable urban
transportation due to their high efficiency and local
emissions. The primary challenges regarding elec-
tric vehicles (EVs) are their short range compared
to internal combustion engine vehicles (ICEV) that
meet the practical needs of consumers and the long
battery recharge time. One effective approach is to
use regenerative braking energy to extend the range
of electric vehicles and prevent the need for extra
energy storage [3]. When an electric vehicle acceler-
ates at high speeds, the transient current resulting
from regenerative braking feedback in the motor cur-
rent can surge up to 200 A or more [4]. The high
current generated during regenerative braking can
cause damage to lithium-ion batteries, whereas super-
capacitors with higher power density enable efficient
and quick charging from significant braking energy
through the conversion of kinetic energy into electrical
energy [5, 6].

However, according to previous research, the drastic
change in battery charging/discharging power require-
ments, especially in urban areas, can reduce battery
life [7]. One possible solution could be high-specific
energy storage devices that can support high-peak
power applications without a significant loss of durabil-
ity [8]. Regenerative braking systems depend mostly
on batteries alone as their energy storage component.
However, this system has several drawbacks, including
poor temperature characteristics, low specific power,
and short life cycle. Additionally, the high power de-
mands of vehicles, particularly in cities where there
are loads is a lot of starting, accelerating, and decel-
erating, might damage the battery [9]. To address
this problem, high-power density energy sources such
as supercapacitors (SC) can help reduce the surge
of high current charging and discharging on the bat-
tery [5]. The proposed Energy Management System
(EMS) higher-level strategy includes using a superca-
pacitor to supply high power during peak demand and
recover the braking energy. This is achieved through
the implementation of the adaptive low-pass filter
technique [10]. For optimal utilisation of regenerative
braking (RB), an energy management system (EMS)
is necessary. The EMS enables efficient distribution of
power consumption among different sources within the
vehicle’s energy storage system. This ensures effective
utilisation of kinetic energy, optimal vehicle perfor-
mance, and improvement of battery life cycle [11].

Supercapacitors are an attractive option for electric
vehicles, because of their many benefits, including high

371

https://doi.org/10.14311/AP.2025.65.0371
https://creativecommons.org/licenses/by/4.0/
https://www.cvut.cz/en


A. S. Bhagat, V. Kalkhambkar, P. P. Gupta, V. Prakash Acta Polytechnica

power density, extended life cycle, and strong transient
charge and discharge performance [6, 12, 13]. The
switched structure of battery/supercapacitor (SC) hy-
brid energy storage systems (HESS) allows for switch-
ing between the battery and the SC energy storage
during different vehicle operations [12]. A control
strategy for a fully-active hybrid energy storage sys-
tem that uses two bidirectional DC/DC converters
to regulate battery and supercapacitor currents as
well as DC bus voltage can be seen here [11]. A con-
trol algorithm for the fully active HESS, is shown
in reference [13]. Various aspects of lithium-ion bat-
teries, including EV systems, energy management
systems, challenges, and recommendations for future
work, including battery components, energy storage,
management systems, monitoring, and protection, can
be found in reference [5]. Integrating the battery and
SC units in to the DC-bus of the inverter is a ma-
jor challenge when designing a hybrid energy storage
system (HESS). There are many challenges faced in
HESS development, such as the size of energy storage
devices, controlling the supercapacitor charge, and
maintaining the DC bus voltage constant [9, 14]. The
size and type of the Energy Storage System (ESS)
used mainly depend on driving patterns, which can
vary based on factors, such as driver behavior, lo-
cation, and traffic conditions [15]. Therefore, it’s
important to understand the impact of these driving
patterns on the ESS size to prevent under or over-
sizing [16]. There are several commonly used methods
for designing an Energy Management System (EMS)
with a hybrid Energy Storage System (HESS), Fuzzy
logic [17, 18], rule base [12, 19], nonlinear program-
ming [7], sliding mode [13], classical controller and
hysteresis controller [20].

This paper presents an innovative control strategy
for regenerative braking systems, an allocation con-
trol mechanism that effectively splits the required
optimal brake torque into two different components.
These components are then precisely assigned to the
friction brakes and regenerative brakes, respectively,
optimising their functions [21]. The rule-based en-
ergy management strategy incorporates primary and
secondary hierarchical energy management strate-
gies. These strategies are integrated into the charge-
depleting/charge-sustaining (CD/CS) control strat-
egy [19]. The approach involves a setup of the drive
system and an execution of the frequency separation
technique [22]. The DC bus voltage regulation ensures
energy balance in the hybrid system. A supercapacitor
pack, known for its high power density and dynamic
characteristics, supplies energy and maintains a stable
DC bus voltage [23]. The sequential logic controller
plays an important role in activating various regula-
tion controllers and facilitating the switching between
the storage devices based on the system’s different
driving modes [24]. The main objective of the con-
troller is to ensure correct tracking of the reference
values for the battery current, supercapacitor (SC)

current, and DC bus voltage. The control inputs used
are the duty cycles of two DC/DC converters. The
SC current is also controlled as it helps maintaining
the DC bus voltage at a constant level. Specifically,
the reference values for battery and SC currents are
calculated by the Energy Management System (EMS),
which uses a rule-based controller to regulate the DC
bus voltage within the desired value range. Addition-
ally, the proposed rule-based controller regulates both
the battery and the SC currents.

This paper proposes a simplified rule-based control
strategy for a hybrid energy storage system (HESS) us-
ing supercapacitors and a battery, connected by a bidi-
rectional DC/DC converter. The control strategy regu-
lates the supercapacitors output and charging current,
which are calculated based on the energy manage-
ment strategy, which controls the DC/DC converter’s
switching between the battery and supercapacitors
in different driving modes. Additionally, the study
examines the contribution of battery-supercapacitor
energy for city and highway driving cycles in order to
determine the appropriate sizing for both components
with regenerative braking systems. Energy recovery
is analysed for various driving cycles to evaluate the
effectiveness of the proposed strategy and analysing
the energy recovery for different driving cycles.

The contributions of the paper are as follows:
(1.) Implementing a rule-based power management

for the battery and supercapacitors.
(2.) Proposing a model for utilisation of regenerative

braking energy using supercapacitors and battery
as the energy storage system.

(3.) Analysing the battery-supercapacitor energy man-
agement at different driving conditions.
The paper is organised as follows, Section 2 con-

sists of mathematical modelling of system components:
battery, supercapacitor, vehicle body, and DC/DC
converter. Section 3 discuses the power-train com-
ponents, necessities and challenges faced in HESS
vehicles. Section 4 discusses a detailed introduction
of EMS and the implementation of rule-based Energy
Management strategy in a HESS. Section 5 discusses
the Simulation and Analysis of the proposed Hybrid
Energy Storage System. Section 6 discusses the results
obtained from the proposed rule-based energy man-
agement strategy. At the end all simulation results
data are presented in a comparative form in a table to
evaluate the performance of each case and for different
driving cycles.

2. Modelling of system
components

2.1. Electric vehicle model
It is crucial to comprehend the dynamics of electric
vehicles (EVs), in order to optimize their performance
and improve their efficiency. This study introduces
a fundamental model that captures the primary forces

372



vol. 65 no. 4/2025 Energy management for electric vehicles with battery and supercapacitor

affecting an electric vehicle and establishes their con-
nection with the power demand required for propul-
sion. The model incorporates various factors, includ-
ing rolling resistance, aerodynamic drag, vehicle mass,
incline angle, and powertrain efficiencies. The dynam-
ics of the vehicle are shown in Figure 1.

The fundamental model that describes the dynamics
of an electric vehicle is presented as:

rV cos β + 0.5CdAρV 3 + mVx
dv

dt
+ mgV sin β

= Pdemand ηtηm.
(1)

The variables used in the model include the mass
of the vehicle (m), gravitational acceleration (g), the
rolling resistance coefficient (r), the velocity of the
vehicle (V ), the incline angle of the road (β), the
aerodynamic drag coefficient Cd, the front area of
the vehicle (A), the density of the medium (ρ), the
transmission efficiency (ηt), and the efficiency of the
electric motor (ηm).

mV = Fx − Fd − mg · sin β, (2)
Fx = n (Fxf + Fxr) , (3)

Fd = 1
2CdρA (V + Vw)2 · sgn (V + Vw) , (4)

Fzf = −h (Fd + mg sin β + mV ) + b · mg cos β

n(a + b) . (5)

The normal force acting on the front and the rear
tyre are calculated by zero normal acceleration and
zero normal pitch torque:

Fzr = +h (Fd + mg sin β + mV ) + a · mg cos β

n(a + b) , (6)

Fzf + Fzr = mg
cos β

n
, (7)

where a and b are the distance between the front and
rear axles, h is the height of the vehicle, n is the
number of wheels on the n axle, Vw is the wind speed,
Fd is the aerodynamic drag force, Fxf and Fxr are
longitudinal forces on the front and rear tire, Fzf and
Fzr are normal load forces on the front and rear tire.

2.2. Battery model
In an energy storage system, battery modelling is very
important to study the behaviour of the system by
considering all parameters, which affect the overall
battery performance. For an electric vehicle, a lithium-
ion battery is modelled in a MATLAB Simulink en-
vironment. For modelling the lithium-ion battery in
MATLAB battery specific ratings are required and
they are given in the simulation section. The battery
model is designed using various control system blocks
to simulate an actual battery performance, and it is
based on fundamental mathematical equations which
are discussed below. Figure 2 shows the circuit of the
battery block model in MATLAB [25].

Figure 1. Vehicle body dynamics.

The output voltage of the battery is given by the
equation:

Vbat = Ebat − Riibat, (8)

where Vbat denotes the battery’s output voltage, Ebat
denotes its open-circuit voltage, Ri denotes its internal
resistance of the battery, and ibat denotes the battery
current. The charging and discharging voltage of
the battery is represented by dynamic mathematical
Equations (9) and (10):

Ebatdis = E0 − K
Q

Q − q
id − K

Q

Q − q
q

+ M exp(−N ∗ q),
(9)

Ebatch = E0 − K
Q

q + 0.1Q
id − K

Q

Q − q
q

+ M exp(−N ∗ q),
(10)

where Q represents the maximum battery capacity,
E0 represents the constant voltage, id represents the
altered current from the low pass filter to the battery
current, K serves as the polarisation constant, M exp
represents the exponential voltage, q represents the
extracted capacity, and N represents the exponen-
tial capacity. The SOC of the battery is given by
Equation (11):

SOC = 100
(

1 −
∫ t

0 ibat dt

Q

)
. (11)

2.3. Supercapacitor model
Supercapacitors are widely used in various applica-
tions where a fast and dynamic response is required
in a short time period. When modelling a super-
capacitor, all characteristics must be accounted for.
In hybrid energy storage, a supercapacitors are used
mainly for providing peak power for a short duration
and storing regenerative braking energy effectively
without degrading the life of the supercapacitor and
helping the battery to maintain its SOC. Compared
to batteries, the relations between the terminal volt-
age and the remaining capacity of supercapacitors is
more linear. Therefore, the SOC of a supercapacitor
is used to indicate its remaining capacity, which can
be calculated using the expression provided in [18].
The supercapacitor model circuit is shown in Figure 3.

373



A. S. Bhagat, V. Kalkhambkar, P. P. Gupta, V. Prakash Acta Polytechnica

Figure 2. The equivalent circuit of the battery block model in MATLAB [25].

Figure 3. Supercapacitor model circuit in MATLAB
platform.

The supercapacitor output voltage is expressed in
mathematical form by a Stern equation:

Vsc = NsQT d

NpNeεε0Ai
+ 2NeNsRT

F

· sinh−1
(

QT

NpN2
e Ai

√
8RTεε0c

)
− Rsc · isc,

(12)

where Vsc is the output voltage of the SC, Np repre-
sents the number of parallel capacitors, Ns represents
the number of series capacitors, Ne represents the
number of layers of electrodes in the SC, QT repre-
sents the electric charge (C), d is the molecular radius,
R is the ideal gas constant, T is the operating tem-
perature, q represents the extracted capacity, Ai is
the interface area between the electrodes and elec-
trolyte in (m2), F is faraday constant, and ε, ε0 are
the permittivity of material and free space.

With:
QT =

∫
iscdt, (13)

the self-discharge of a supercapacitor is represented

by the electric charge of a supercapacitor is modified
as follows (isc = 0):

QT =
∫

iselfdisdt, (14)

iselfdis =



CT α1

1 + sRSCCT
, if t − tOC ≤ t3

CT α2

1 + sRSCCT
, if t3 < t − tOC ≤ t4

CT α3

1 + sRSCCT
, if t − tOC > t4

(15)

where isc is the current of the supercapacitor, CT is
the total capacitance, RSC is the internal resistance
of the supercapacitor, and α1, α2 and α3 show the
constraints and the rate of change of supercapacitor
voltage during the time intervals (tOC ; t3), (t3; t4) and
(t4; t5), respectively. The SOC of a supercapacitor is
used to indicate its remaining capacity, which can be
calculated using the expression provided in [16]:

SOCSC = Vter − Vmin

Vmax − Vmin
, (16)

where the variable Vter refers to the terminal voltage
of the SC, while Vmin and Vmax denote the minimum
and maximum cutoff voltages of the supercapacitor,
respectively.

2.4. Converter model
Figure 4 shows the circuit diagram for the fully active
HESS that is being used. It is composed of a battery,
supercapacitor, and a motor load, along with two
standard bi-directional DC/DC converters. The load,
which includes the motor has a predictable power de-
mand, represented as a varying current im. The model
only takes into account the inner series resistance of
the battery and supercapacitor models, Rbat and Rsc,
respectively, to simplify it. During the charge and

374



vol. 65 no. 4/2025 Energy management for electric vehicles with battery and supercapacitor

Figure 4. Circuit diagram of fully active HESS.

discharge operations, there are two DC/DC convert-
ers used as current controllers for both the battery
and the supercapacitor. The bi-directional DC/DC
converter comprises two IGBTs, a capacitor, and an
inductor with its series resistance considered. The
two IGBTs operate synchronously, with S1 and S2 op-
erating in opposite phases, and the same applies to S3
and S4. The duty cycle of the IGBT is represented as
D1, with the on-resistance of switch S1 represented as
Ron1. Additionally, Vbat and Vsc represent the open-
circuit voltages of the battery and the supercapacitor,
respectively.

The HESS circuit diagram, shown in Figure 4, in-
cludes a battery pack, an SC pack, and two bidi-
rectional DC/DC converters consisting of resistors,
inductors, and capacitors. To connect the battery
pack to the DC bus, an IGBT is used. Another bidi-
rectional DC/DC converter, consisting of two IGBTs
(S1 and S4), a capacitor (C0), and an inductor (L2)
is used to link the SC pack to the DC bus. The model
of the DC/DC converter incorporates the inductor
series resistance, the equivalent IGBT resistance, and
the IGBT freewheel diode. The motor load current is
represented as Im.

The HESS’s average model over a single switching
period can be defined as follows:

V1 = − V1

RbatC1
− i1

C1
+ Vbat

RbatC1
, (17)

V2 = − V2

RSCC2
− i2

C2
+ ESC

RSCC2
, (18)

i1 = V1

L1
− i1

RL1 + Ron2

L1
− V0

L1

+ D1i1
Ron2 − Ron1

L1
+ V0

D1

L1
,

(19)

i2 = V2

L2
− i2

RL2 + Ron4

L2
− V0

L2

+ D3i2
Ron4 − Ron3

L2
+ V0

D3

L2
,

(20)

V0 = i1 + i2

C0
− im

C0
− D1

i1

C0
− D3

i2

C0
. (21)

Capacitor voltages V0, V1, and V2, along with in-
ductor currents i1, and i2, are used to represent the
typical HESS model through a single switching cy-
cle. Since the SC has a large capacitance, its voltage
change within a switching period is disregarded. Us-
ing the aforementioned average model, a 5th order
state-space model for the HESS can be derived, which
has been previously confirmed in [5].

3. Hybrid energy storage system
3.1. HESS powertrain
The HESS powertrain consists of various components
that involve different energy sources with different
characteristics, such as electronic converters and me-
chanical coupling from the motor to the vehicle drive
shaft. The energy storage systems have different but
complementary characteristics to each other. Two
power electronic converters, which convert DC/DC
operation and link the motor and the energy storage
system, control part of an electric vehicle, which con-
trol and transfer energy based on the requirement of
the vehicle. The battery and supercapacitor are con-
nected to a motor via a DC/DC converter as shown
in Figure 5, which uses a fully active hybrid topology
that uses two independent converters. This type of
topology was chosen because it actively controls the
output current of the battery and the supercapacitor,
which helps to maintain a stable DC link voltage at
the input motor terminals. The motor is connected to
tyres through a mechanical coupling to transfer the
rotational mechanical power.

The whole fully active topology is controlled by
a main controller, which collects data from powertrain
components and instructs them to perform specific
control actions based on an energy management algo-
rithm. This design was chosen for the HESS because

375



A. S. Bhagat, V. Kalkhambkar, P. P. Gupta, V. Prakash Acta Polytechnica

Figure 5. Architecture of electric vehicle powertrain.

it allows for more flexible and efficient regulation of
the battery and supercapacitor independently [26].
This configuration of power electronic converters en-
sures a high system performance and efficiency due
to the stable voltage across the DC link, with low
cell balancing issues with the battery and SC packs.
The topology is widely adopted in smart grid sys-
tems, where two or more energy storage devices are
integrated for more efficient operation due to its inde-
pendent control of each source.

3.2. Necessity of HESS
The Li-Ion battery cannot handle rapid changes in
power demands during acceleration or regenerative
braking due to its dependency on reduction and oxi-
dation reactions for charge transfer [5]. These sudden
power demands put excessive stress on the battery
and as a result, reduce the lifespan of the battery.
These actions result in degradation at the cell level, as
a result of increased internal resistance and capacity
loss over time [9]. However, flywheel (FW) or super-
capacitor (SC) technologies have the power density
needed to sustain high power outputs for short time
period [27]. The Li-Ion battery is the primary en-
ergy storage component in electric vehicles due to its
high energy density. When it comes to automotive
applications, particularly during extreme braking and
traction driving situations, the battery shows several
weaknesses related to its chemical reactions during
charging and discharging operations [28]. These re-
actions result in capacity degradation and reduced
lifespan, and in extreme cases, fire can also happens
and damage the whole vehicle and endanger human
life [22]. Due to the fast degradation of the battery, the
whole battery pack needs to be replaced, which is very
costly. Additionally, the battery is struggling to meet
the power requirements specified by the traction and
braking control systems during these various intense
driving operations [29]. To tackle these challenges and
minimise the stress on the battery, it is necessary to

Figure 6. Energy vs power density.

add another energy storage device, which supports the
battery [30]. This secondary storage system should
have the ability to deliver the required power for in-
tense driving scenarios, replacing the battery during
periods of high power demand. The secondary energy
system has a high dynamic response without affecting
its life cycle [16]. The supercapacitor is a promising
solution for secondary energy storage elements, be-
cause it shows high power density as compared to
battery and fuel cell energy storage devices, Figure 6
shows the energy and power characteristics of different
energy storage devices [31].

High-power energy storage devices have a high re-
sponse rate and high-energy devices have a slow re-
sponse rate. This integration proves highly suitable
for electric vehicle applications, mostly due to its
exponential power density with energy density capa-
bilities. Combining a battery with a supercapacitor
offers many advantages, which include high dynamic
response, providing peak power during high accel-
eration and starting operation, storing regenerative
braking energy effectively without shortening the life
of the supercapacitor, fast charging, and improving
the storage system life of the vehicle [32].

376



vol. 65 no. 4/2025 Energy management for electric vehicles with battery and supercapacitor

There are different topologies by which the bat-
tery and supercapacitor are connected to the DC bus,
which include the parallel, semi-active, and fully ac-
tive combinations. The topology is selected based
on operational modes and the size of primary and
secondary energy storage devices. The supercapacitor
allows for smooth energy transfer within the system,
which also helps to preserve the vehicle’s dynamic
load profile and extend the possible driving range.
Therefore, in electric vehicles, the use of a battery and
supercapacitor together provides an effective method
of long-term energy management and dynamic power
regulation.

3.3. Challenges in HESS
3.3.1. System design and sizing
When a battery and a supercapacitor are combined,
it makes the system complex and challenging to con-
trol due to the differences in unpredictable driving
patterns. Combining high power and high energy
density devices together requires maintaining the DC
bus voltage without putting stress on the power con-
verter while shifting between different modes. Another
challenge while designing the system is the proper siz-
ing and optimisation of both energy sources with the
power and energy density requirement of an EV, for
which no standard practices are available. If the size
of the battery and supercapacitor pack is not optimal.
Without taking specific requirements into account,
choosing a large capacitor increases costs and small
SC packs fail to provide peak power, putting a strain
on the battery and adding weight and space to the EV.
On the other hand, choosing a small battery causes
faster degradation and reduction of battery life, and
the vehicle dynamic response will be significantly af-
fected.

3.3.2. Topology and converter design
challenges

Designing a power electronic converter for these HESS
applications is very challenging due to the rapidly
changing driving patterns, peak-power and regenera-
tive braking demands, and high current flows through
converter switches. In addition, they need to be very
robust with fast response for safe operation [33]. While
designing these hybrid complex systems, the main
challenge is to design the system as complex as pos-
sible without compromising the vehicle performance.
There are different converter topologies available to
connect the battery, SC, converter, and the motor
together. If we select a single converter topology, it
comes with more complex control. On the other hand,
if we choose a converter topology for easy control,
then it increases system cost, space, and weight. So
choosing the right topology is very critical. The power
converter faces issues such as switching losses, PWM
control, device durability, and its overall reliability to
ensure safe operation [34].

The batteries need converters designed for stable,
high-energy transfer, supercapacitors demand fast-
response, high-power converters capable of handling
rapid charge and discharge cycles. Integrating sepa-
rate converters for the battery and the supercapacitor
increases cost and operational complexity, but differ-
ent vehicle modes of operation need to run the system
for the rated power, while maintaining efficiency, sta-
ble operation, and proper cooling of these components.
While adding regenerative braking, the bidirectional
DC/DC converter rating is very important, because
during this mode, the converter handles high peak
currents to charge the supercapacitor while maintain-
ing a safe DC voltage to ensure stable operation [33].
The converter switch must be selected appropriately
for sustaining high peak current and fast switching
without affecting the converter’s performance and sys-
tem stability. These challenges can be overcome by
optimal design and rating selection synchronised with
the energy management system.

3.3.3. Control strategy and energy
management challenge

The main challenge of EMS is real-time, precise deci-
sion making to calculate how much power should be
drawn from the battery and how much from the super-
capacitor, the key challenge is to control the EMS for
various based objectives effectively and without fail-
ure. This requires developing and tuning a controller
algorithm based on logic that handles real time driv-
ing situations and SOC limitations of storage devices.
Real-time, accurate estimation is challenging due to
temperature, current transients, and aging effects of
energy storage and another power components [35].
To make optimal energy allocation decisions, future
power demand must be accurately predicted, so it’s
challenging to develop and integrate into actual hard-
ware for implementing predictive algorithms that learn
or adapt to driving behavior and unexpected changes.
In some cases, a primary power source fails to supply
power for all components of the system, mainly for
the control unit of the vehicle. In this case, a backup
power source must be present to supply power in differ-
ent load demand conditions. Continuous monitoring
of all parameters and processing of data is very impor-
tant to make decisions on power shifting in different
driving scenarios while maintaining a safe operating
state. The control and coordination of all compo-
nents in real time is essential for a hybrid system
control [36].

3.3.4. Thermal management
The continuous discharging and charging of the HESS
based on vehicle acceleration and deceleration and
regenerative braking modes, causes high bidirectional
peak currents to flow through the system, which causes
losses in the form of heat from the battery, SC, con-
verters and motor. As the HESS is complex due to
the integration of various components, which results

377



A. S. Bhagat, V. Kalkhambkar, P. P. Gupta, V. Prakash Acta Polytechnica

in more heat generated from different units, it is es-
sential to dissipate heat at the fastest rate possible
to maintain system temperature in a safe range. For
heat dissipation, energy management systems requires
auxiliary electrical power, which is drawn from HESS,
and therefore impacts the vehicle range. To address
this, energy storage devices and converters need to
be designed and located in EVs in such a way that
they naturally dissipate heat at a faster rate without
affecting the performance. Therefore, the thermal
management is challenging to implement, so is to
maintain a safe range with the available space, low
cost, and higher efficiency. The challenge is to design
and integrate a thermal management system for all
equipment to maintain safe thermal regulation within
a limited space, without affecting the performance.

3.3.5. Protection and fault tolerance
The battery and SC both have their safe operational
range that depends on their rated design and cell
integration. In order for a protection system to be
capable of protecting in uncertain conditions, safe
operating parameters are needed. When a fault hap-
pens in one of the batteries, supercapacitor, or its
independent converter, it should not compromise the
vehicle’s operation. So it is a challenge for designers
to design a fault-tolerant system that is very robust
for fault detection and isolation mechanisms to bypass
the faulty system and not hinder vehicle operation.

3.3.6. Cost and space requirement
A hybrid energy storage system combines two en-
ergy storage devices, DC/DC converters, controllers,
sensors, and an auxiliary power supply unit, which
increases the cost of the system. For a hybrid system,
which has multiple components, it is very important
to consider the limited space available when designing
the placement of the components. Manufacturing this
hybrid storage system for an EV at a low price is very
challenging.

Regardless of the placement of the components, such
as additional SC packs, DC/DC converters, superca-
pacitors and their energy management system (EMS),
control units increase the initial cost and space require-
ments, however, these are countered by a number of
significant benefits. The supercapacitor supplies high
current transients and absorbs regenerative braking
energy efficiently, by reducing peak power spikes on
the battery. The rule-based power splitting energy
management results in an extended battery life cycle
and extend battery replacement time, which leads
to reduced maintenance and operational costs. In
future work, we plan to conduct a full economic cost-
benefit analysis to determine the cost-benefit of the
system with the battery alone and the HESS electric
vehicle [37].

As the system is more complex due to the multiple
components, the complementary characteristics of the
battery and supercapacitator, they share workload,

which in turn reduces the stress on individual com-
ponents and increases their lifespan. The modular
design of supercapacitor and battery modules with
converters and other components makes repairs and
replacements easier. Although the initial cost is high
due to the extra energy storage elements and its sys-
tems and control hardware, the long-term benefits are
the longer battery replacement period, which carries
a huge percentage of the vehicle maintenance costs,
and also due to the energy recovery from regenerative
braking, its operational cost are also reduced. There-
fore, the initial high cost is compensated by long-term
user benefits.

3.3.7. Packaging
We plan a modular system architecture for the battery
and SC modules, in which energy storage units and
power converters are fit into a single compact unit.
This modification provides easier integration into the
current EV architecture makes it easier to integrate
the system into an EV and it also reduces the need
for long wiring harness, significantly reducing space
requirements and costs [38]. The prototype unit, with
all components combined, was placed directly on the
chassis without modifying the vehicle. For minimis-
ing space and cost, we suggest a DC/DC semi-active
hybrid converter configuration that uses only one con-
verter with advanced control. Implementing a hybrid
topology to integrate a battery and supercapacitor in
to the motor reduces overall space requirement, con-
stant weight, and additional connections and wiring.
We can optimise space and cost at the same time by
using a less complex system. However, the exact sys-
tem and configuration depend on the specific vehicle
and its applications.

Based on the vehicle space availability, components
such as SC modules can be placed areas that are less
sensitive to space, such as near the wheels or on top
of the vehicle in electric buses. This balances weight
distribution and makes efficient use of available space.
However, there are some trade-offs when modifying
a standard EV in this way. The integration of SC and
its supporting electronic systems adds some weight to
the vehicle. However, the added weight is offset by
increased regenerative braking, reduced strain on the
battery bank, and increased range. This allows the
size of the battery pack to be reduced. Introducing
these components may lead to a smaller vehicle interior
space and reducing the vehicle’s usable boot space.
However, these effects are minimised in most cases
and can be mitigated through proper optimal design
planning with the help of an EV design expert.

4. Energy management in HESS
The EMS in a hybrid energy storage system is re-
sponsible for controlling and optimising the energy
flow between two or more energy storage devices and
the electric motor, maximising the efficiency and per-
formance of the EV. Energy management systems

378



vol. 65 no. 4/2025 Energy management for electric vehicles with battery and supercapacitor

Figure 7. Block diagram of standard EMS.

extend the range of electric vehicles by using different
control strategies, which ensure an optimal operation
of vehicles.

The need for EMS stems from the integration of
different types of energy storages used in electric ve-
hicles for different operating modes, such as average
power mode, peak power mode, regenerative braking
mode, and charging of one source from another. To ef-
fectively transfer and manage the flow of energy from
two sources, EMSs require real-time data from the
elements in the hybrid energy storage system. The
EMS consists of different elements, a fundamental
block diagram is shown in Figure 7, which consists of
the electronic control unit (ECU), Driver commands
that act as input signals, and its output controls the
DC/DC converters. System parameters consist of bat-
tery and supercapacitor voltage, SOC, current that
is supplied to the main electronic control unit where
all the data are processed and the appropriate control
strategies are calculated. Effective energy capture
during regenerative braking of different intensities is
ensured by EMS, which controls the converter power
using buck and boost modes.

The effectiveness of an energy management strat-
egy depends on the various strategies and algorithms
designed using control techniques and vehicle driving
scenarios. EMS actively analyses the data from sen-
sor inputs and works to extend the driving range by
developing effective control algorithms, in addition
to collecting and recording the data during vehicle
operation. The choice of EMS depends on several
factors, including the driver’s inputs, the distance of
the travel, the speed of the electric motor/generator,
and the battery state of charge (SOC) [39]. It is dif-
ficult to control the power distribution without the
access to these data and information. The EMS in
HESS tries to satisfy power demands, maintain battery
voltage and charge, improve overall system efficiency,
and extend the battery life [40]. The strategies used
for EMS are classified as rule-based strategies and
optimization-based strategies, which depend on var-
ious battery, supercapacitor, fuel cell, and flywheel
combinations. Rule-based strategies are structured
methods designed to operate vehicles in different driv-
ing modes, based on various input data generated by

the system. The control strategies used for EMS are
divided into two types, which are rule-based strategies,
and optimisation-based strategies. Rule-based strate-
gies are control techniques designed to control electric
vehicles using mathematical and logical approaches.
These rule-based strategies use various approaches
as per the need of control requirements, which in-
clude deterministic rule-based, frequency-based, fuzzy
logic-based, and neural network based strategies [41].

The optimisation strategy predicts the optimal val-
ues based on comparing different modules and pro-
cesses. Optimization strategies are used to determine
set points and correct and minimise feedback errors.
Prediction accuracy and control optimisation are es-
sential to effective energy management [42]. To per-
form a complex task and calculations, a large data
storage and a processing system is required for an
effective optimisation-based approach in EMS. These
optimisation strategies are further divided into vari-
ous types based on different standard equations and
principles, which include dynamic programming, in-
stantaneous optimisation, and Pontryagin’s princi-
ple [43]. These different strategies are based on differ-
ent problem-solving approaches and behaviour of con-
trol algorithms, including numerical analytical models.
Machine learning techniques are implemented for EMS
to effectively utilise the energy split in different sce-
narios and extend the range of EVs, by capturing
maximum energy by controlling all necessary opera-
tions mainly controlling of DC/DC power converters
efficiently and without any control malfunctions in real
time [41]. An effective EMS should be stable against
noise, uncertainty, inaccuracies, and disruptions. Low
computing complexity, real-time controllability, accu-
racy, and global optimization. When an EMS satisfies
these requirements, it can manage energy resources
efficiently, guarantee system stability, and maximise
energy use, resulting in more effective and sustainable
energy management strategies [44].

4.1. Implementation of energy
management strategy

An energy management strategy for the hybrid storage
system comprises several steps. Initially, the algorithm
checks whether the vehicle’s power demand is above

379



A. S. Bhagat, V. Kalkhambkar, P. P. Gupta, V. Prakash Acta Polytechnica

Figure 8. Energy management system flowchart.

zero, and if yes, the system initiate driving. Next, the
algorithm verifies whether the power demand exceeds
the maximum power level, and in such a case, the
system draws power from the supercapacitor pack.
On the other hand, if the power demand is below the
maximum power level, the algorithm draws the power
from the battery pack. During braking, the power
demand becomes negative, and the system employs
regenerative braking to convert the vehicle’s kinetic
energy into electrical energy. This energy is then
stored in the supercapacitor pack for future use to
power the vehicle or supplement the battery pack.
Overall, the energy management algorithm provides
constant access to the required power and improves
the system’s efficiency. The flowchart is shown in
Figure 8 [45].

Where Pd is the vehicle’s power demand and Pmax
is the maximum power limit of the battery. The
vehicle operates in the following 5 different modes:
• Mode 0: In this mode, if the battery SOC is less

than 10 % then the vehicle goes to shutdown mode.
• Mode 1: When the power demand exceeds the max-

imum demand power (Pmax) limit, the superca-
pacitor pack is employed in the peak power mode
when its SOC is more than 10 %. The supercapaci-
tor pack provides bursts of high power required for
rapid acceleration or climbing steep inclines. Its
high power density and fast charging/discharging
capabilities allow it to supplement the battery pack
during high-power demands.

• Mode 2: When SC SOC is less than 10 %, the
battery provides the vehicle with power in the event
of sudden-high power demands.

• Mode 3: In the average power mode, the EV draws
power from the battery pack during normal driv-
ing conditions when the power demand is below
the maximum power demand (Pmax) limit. This
mode ensures continuous operation while keeping
the power consumption within the rated limits of
the battery pack.

• Mode 4: During deceleration or braking, the EV
operates in the regenerative braking mode. In this
mode, the electric motor acts as a generator, con-
verting the kinetic energy of the vehicle into electri-
cal energy. In this mode, the first priority is given
to the SC. This is achieved by checking whether the
SOC of the SC is less than 100 % and if so, the SC is
then charged. The regenerative braking mode is ac-
tivated when the power demand becomes negative,
indicating the potential for energy recovery.

• Mode 5: If the SC SOC is 100 %, then the system
diverts the power to the battery and checks the
SOC of the battery. If it is less than 90 %, then
regenerative braking energy is charges the battery.
If the battery SOC percentage is more than 90 %,
then battery charging is disabled, and the algorithm
activates the mechanical brake. Mechanical braking
is necessary when the power demand exceeds the
capabilities of the regenerative braking system or
in cases where immediate maximum braking force
is needed for safety reasons.
These different operational modes of electric vehi-

cles contribute to efficient power management, im-
proved energy utilisation, and enhance the overall
performance. The utilisation of battery and super-
capacitor packs in various modes optimises power

380



vol. 65 no. 4/2025 Energy management for electric vehicles with battery and supercapacitor

Figure 9. Block diagram of proposed controller.

distribution, ensuring smooth operation, and max-
imising the potential of regenerative energy recovery.
In the initial phase of the research, the power demand
of the vehicle under various driving conditions was
extracted from the vehicle’s system. This includes
parameters such as power demand (Pd), current de-
mand (Id), and voltage demand (Vd), as illustrated
in Figure 9. The real time data consists of power
demand, bus voltage, motor current, and SOC and
they are fed to the EMS where the based on rule rule-
based algorithm allocates power to the battery and
supercapacitor. The EMS divides the power demand
into average power and peak power demand, as shown
in the flowchart in Figure 8 [20]. Next, the individ-
ual power demands are processed together with the
battery voltage to determine the current flow in the
battery and supercapacitor during different vehicle
operations. This process generates two current signals,
namely Ibat and Isc. These Ibat and Isc are compared
with the actual battery and supercapacitor currents,
which generates current error signals. These signals
are then fed into a PI controller, where signals are con-
verted into appropriate duty cycles. After that, the
PI controller output duty cycle is given to the PWM
block to generate appropriate PWM switching signals
to control the battery and supercapacitor converter
depending upon the various operating modes. Specifi-
cally, S1 and S2 represent the IGBT switches of the
battery-side DC/DC converter. These switches are
controlled by PWM signals to discharge the battery
during the average power demand. On the other hand,
S3 and S4 correspond to the IGBT switches of the
supercapacitor side DC/DC converter, which operates
during the discharging mode during peak acceleration
conditions. The controller design is described in de-
tail in [22]. When regenerative braking is applied, the
DC/DC converter switches to the charging mode to
charge the supercapacitor depending on the regenera-
tive power. The values of PI of the battery and SC
controllers are P = 0.13 and I = 65 respectively, the
control loop bandwidth is 10 kHz, and the sampling
time is 5 × 10−6 sec.

4.2. Control and maintenance
To manage the increased system complexity of HESS
for real-time maintenance and control, we developed
a comprehensive strategy focused on real-time control
efficiency and fast maintenance. The rule-based en-
ergy management gives a two-layer hierarchical control
system approach designed to deliver fast and optimal
energy management without depending on computa-
tionally intensive optimisation algorithms. The first
layer sends hardware-triggered logic signals, which
are based on the reference current, SOC of battery,
and supercapacitor threshold in less than 10 millisec-
onds intervals. As a response, it makes immediate
controlled switching decisions, which are very impor-
tant to maintain HESS responsiveness during sudden
and dynamic load changes, and regenerative braking.
Following the primary layer, a second control layer
runs at less than 15 Hz control thresholds based on
the feedback signal from the BMS of the battery and
supercapacitor modules health indicators, as well as
driving scenarios [36]. This layered hierarchy structure
ensures a fast response to any sudden acceleration, de-
celeration, and braking during real-time driving events
while keeping the system functional with computation
simplicity [46].

Maintenance is very important for such complex
systems to ensure all subsystem components work
properly without failing and affecting their perfor-
mance. If one of the system failures happens, it may
cause the whole system to stop working. For that,
modular control and hardware architecture handle
system complexity and enhance maintainability. In
modular architecture energy management, speed con-
trol, and current control modules are designed as
independent but synchronised and coordinated mod-
ules. This approach makes for easy implementation,
troubleshooting and future scalability. For hardware
ease of maintenance and to ensure minimal downtime
modular design of battery, supercapacitor should be
adopted, which is easy in case of faulty equipment
replacement without opening the complete system.
Real-time parameter data monitoring plays a very im-

381



A. S. Bhagat, V. Kalkhambkar, P. P. Gupta, V. Prakash Acta Polytechnica

portant role in the maintenance of such complex sys-
tems. HESS diagnostic algorithm continuously takes
battery and SC current, voltage, SOC and equivalent
series resistance (ESR). This real-time data moni-
toring and its computation through fault detection
algorithms give preventive fault indications and locate
faults in complex systems.

4.3. Real time optimization
The proposed EMS has been designed with real-time
application in mind depending on various external
factors. To validate the working performance of this
EMS under realistic operating driving scenarios, we
conducted tests using a real-time standard driving
cycle simulation that mimics actual vehicle dynam-
ics under different load conditions. This simulation
environment allows for emulating a designed vehicle
model in MATLAB with acceleration, deceleration,
and other driving conditions. The energy manage-
ment strategy periodically collects input data that
is generated by various components, such as SOC,
power demand from the motors, the DC bus voltage,
and regenerative braking energy. Based on these real
time data, the EMS rule-based algorithm allocate the
high and low frequency power demand between the
supercapacitor and the battery from which the PWM
switching signal is generated through the PI controller
with different loops are responsible for controlling
the voltage and individual currents for the battery
and supercapacitor modules [47]. During regenerative
braking, this command signal is sent to the controller,
which gives priority to the SC to recharge through
the bidirectional converter. During this continuous
operation, the EMS includes safety and fault-handling
logic to limit overcharging and over-discharging of the
energy storage sources. The decision-making capabil-
ity that maintains the energy flow within the a safe
operating range of the battery, SC, and converters,
while ensuring optimal energy utilisation. The real-
time performance of the proposed energy management
strategy was tested in real-time simulation for stan-
dard drive cycles. This demonstrates the optimisation
and effectiveness of the proposed real-time control
approach under dynamic driving patterns [48].

4.4. Risk mitigation in dynamic and
unpredictable driving environments

When proposed EMS is integrated with a hybrid en-
ergy storage system in a real world application, it
can face some difficulties during unpredictable driving.
The main risk is the delayed response of the EMS
to sudden changes from the controller to the hard-
ware components. Frequent fast changes of driving
modes, such as high peak power demand to sudden
emergency braking can limit the system’s ability to
respond and control the energy distribution properly
without affecting its performance. Wrong predictions
from the EMS or sudden dynamic changes can cause
poor energy utilisation, which affects and reduces the

performance and operational efficiency of the hybrid
energy storage system. Without proper system design
and advanced control, continuous peak power and dy-
namic demand on unpredictable road conditions cause
stress to all components, resulting in a reduction in
their operational lifespan. To avoid these risks preven-
tive steps need to be taken for the safety of the whole
integrated system. These include a continuous real-
time computation of all parameters with defined safe
limits that need to be implemented, which allows the
system to respond to any sudden abnormal behaviour
and take necessary action based on an adaptive fault
mitigating algorithm [49].

To prevent possible incidents, the control algorithm
and its hardware communication protocol time re-
sponse must be optimised for any risks that can be
introduced into the system. Predictive maintenance
involves collecting operational data. The system is
expected to detect equipment stress and issue main-
tenance alerts before a major issue occurs [37]. It is
crucial to test these potential risks at a hardware
test bench level, with all integrated components un-
dergoing various load cycles. Extensive robustness
operation tests must also be performed under addi-
tional environmental physical conditions to mitigate
these risks and validate the functional response of the
designed HESS with control strategy in the real world.

5. Simulation and analysis of the
proposed hybrid energy storage
system

When designing a model of an electric vehicle using
MATLAB Simulink, it is crucial to consider various pa-
rameters that impact the vehicle’s performance. Both
electrical and mechanical parameters must be taken
into account to accurately simulate the system. Cre-
ating a validated working prototype model requires
a comprehensive understanding of the necessary ele-
ments and block parameters involved in the simulation
process. Modeling of an electric vehicle poses addi-
tional challenges as it necessitates the consideration of
physical parameters specific to the vehicle. Moreover,
predefined system specifications must be incorporated
into the model to meet the desired requirements and
objectives. This ensures that the resulting model ac-
curately represents the behaviour and performance of
the electric vehicle in the simulation environment.

The Electric Vehicle was simulated using MATLAB
Simulink, using specifications of the TATA Nexon
Electric Vehicle used to represent the vehicle body
and tires. The schematic block diagram of the MAT-
LAB Simulink model is shown in Figure 10. The bat-
tery current demand for different driving scenarios
are recorded by running the model. The model also
investigates how the use of a Hybrid Electric Vehicle
(HEV) or a pure EV affects the battery current de-
mand. To supply the necessary power from the energy
storage system, a DC/DC buck-boost chopper is used.

382



vol. 65 no. 4/2025 Energy management for electric vehicles with battery and supercapacitor

Figure 10. Block diagram of the HESS used for MATLAB simulations.

The output voltage of the chopper is controlled using
a PWM control technique, and a PI controller is used
to regulate the vehicle speed [24].

To validate the efficiency and durability of the sug-
gested controller, a simulation is conducted using
MATLAB Simulink models, as can be seen in Fig-
ure 10. The key parameters of the HESS model,
specified in Tables 1–4 are determined through mea-
surements of the HESS components. Consequently,
the simulation accurately represents the actual sys-
tem.

The load is simulated using a controlled current
source with different current values depending the
load demand Pd of electric vehicles in different driving
conditions for acceleration and braking modes [12].
The aim of the rule-based controller is to create con-
tinuous control strategies that ensure robust tracking
of the Ib(battery) and Isc(supercapacitor) currents.
On the other hand, the objective of the rule-based
controller is to maintain the bus voltage at the desired
level under varying load conditions. First, the DC bus
voltage reference was initially established at 125 V,
taking into account the configuration of the electric
vehicle model. The battery and supercapacitor SOC
was set to 100 %, while the supercapacitor voltage was
set to at 130 V. To test how well the system can han-

Parameters Value Units
Total mass 1 500 kg
Tire radius 0.3 m
Front area 2.91 m2

Drag coefficient (Cd) 0.19 –
Rolling resistance coefficient (r) 0.022 –

Table 1. Vehicle parameters.

Parameters Value Units
Li-ion battery capacity 20 Ah
Battery pack voltage 120 V
Battery voltage range 90 to 140 V
Internal resistance min 0.06 Ω
Battery pack 2.4 kW

Table 2. Li-ion battery parameters.

Parameters Value Units
Pack capacitance 200 F
Pack voltage 130 V
Internal resistance min 0.20 Ω

Table 3. Supercapacitor parameters.

383



A. S. Bhagat, V. Kalkhambkar, P. P. Gupta, V. Prakash Acta Polytechnica

Components Value Components Value
C0 940 µF Csc 200 F
Rbat 0.06 Ω Rsc 0.20 Ω
C1 470 µF C2 470 µF
L1 1.7 mH L2 1.2 mH
RL1 700 mΩ RL2 300 mΩ

Table 4. DC/DC converters parameters.

Figure 11. SC03 city drive cycle vehicle speed.

dle different situations, the load profile used for the
validation includes frequent changes and noticeable
ups and downs, as shown in Figure 11. When Pdemand
is negative, it indicates the regenerative braking con-
dition, whereas in traction mode, Pd varies between
500 W and 8 kW for the SC03 driving cycle.

A number of simulations are performed based on
the proposed rule-based energy management system
with a battery-supercapacitor hybrid system, In these
simulations, the initial SOC values of battery and the
supercapacitor at the start of the driving cycle are
set 100 % and 101 %. The proposed hybrid model is
tested for city, urban, and highway driving conditions
and include chassis dynamometer test cycle (SC03),
high acceleration aggressive driving cycle (US06), and
Highway Fuel Economy Test (HWFET) cycle. Vehicle
body parameters required for modeling are given in
Table 1. The simulation parameters used in MATLAB
modeling of battery pack are voltage, capacity, and
internal resistance and are given in Table 2.

The parameters used for modeling supercapacitors
in MATLAB are given in Table 3, DC/DC bidirec-
tional converter, which are the resistance, inductance,
and capacitance values are given in Table 4.

The performance of the energy management strat-
egy is analysed for different drive cycles, which include
city and highway driving cycles. Braking and accelera-
tion depend on road conditions, so the power demand
and regenerative braking power differ. The proposed
energy management strategy is tested for SC03, US06,
HWFET, and BCD drive cycles.

6. Simulation results and
discussions

The proposed energy management strategy is anal-
ysed for different driving cycles using the MATLAB
simulink platform to evaluate the effectiveness of the
proposed strategy and the response of the battery
and the supercapacitor in different operating modes
of the vehicles. The simulation test is carried out for
two different cases: the first one is a pure EV with
no regenerative braking implemented, and the second
case is an EV with HESS, which consists of a battery
and supercapacitor, and implemented regenerative
braking.

6.1. SC03 drive cycle results
Figure 11 shows the SC03 driving cycle, a 5.8-
kilometer test route, with a maximum speed of
88.2 km h−1. The first test case data of the SC03
drive cycle is shown in Figure 12, where only a bat-
tery is used to power the vehicle and regenerative
braking is not implemented. From the waveform of
the motor load current, we can see that the maxi-
mum current was 80 A and the average continuous
current was around 40 A when the speed is lower than
60 km h−1. As the vehicle suddenly accelerated, the
peak power demand was very high, around 8 kW, for
a short period of time, when the vehicle accelerated
from 0 to 90 km h−1 speed. The SOC of the battery
was continuously depleting from 100 % to 86.44 % at
the end of the drive cycle.

The second test case results of the SC03 drive cy-
cle with a hybrid system operating in average power

384



vol. 65 no. 4/2025 Energy management for electric vehicles with battery and supercapacitor

Figure 12. Battery SOC, current, and voltage of normal system without regenerative braking for SCO3 drive cycle.

Figure 13. Bus voltage, and power of hybrid system for SCO3 drive cycle.

mode M1. The battery plays a crucial role in deliv-
ering the required average power for a vehicle speed
below 60 km h−1. Figure 13 demonstrates how the
presence of a supercapacitor reduces the power de-
mand on the battery and its charging-discharging cur-
rent. Figure 12 shows that without a supercapacitor,
the battery experiences numerous significant peaks in
its charge and discharge current cycles.

Figure 14 shows the battery and supercapacitor
currents for different modes and speed of the vehicle.
The blue curve should is the battery current Ib in
average power mode M3 and the current was below

40 A, the positive red curve shows the SC current
in peak power mode M1 and the negative red curve
shows the SC charging current in regenerative braking
mode M4. During the peak power mode, the current
can reach as high as 80 A, while during regenerative
braking, the charging currents can go up to negative
45 A. The voltage of the supercapacitor pack starts
at 125 V and momentarily decreases to around 100 V.
From the waveform of the current, it can be seen that
when the speed reaches 25 km h−1, the regeneration
stops and the regenerative part shows that the energy
is generated during a sudden braking situation, during

385



A. S. Bhagat, V. Kalkhambkar, P. P. Gupta, V. Prakash Acta Polytechnica

Figure 14. Battery and supercapacitor currents and vehicle speed of hybrid system for SCO3 drive cycle.

Figure 15. Battery, and supercapacitor power of hybrid system for SCO3 drive cycle.

which the speed drastically decreases for a short period
of time.

Figure 13 shows the variations of the bus voltage
and bus power for both the battery and supercapaci-
tor of the proposed hybrid system. Id represents the
current demand and Vbus, exhibits a dynamic oscil-
lating range between 140 and 80 V. The bus voltage
decreases in specified intervals while the bus current
incrementally increases in response to a rapid accel-
eration of the vehicle. As a result, as the vehicle
slows down during the regenerative braking mode, the
bus voltage increases, indicating the activation of the
supercapacitor’s charging mode.

Figure 15 shows the power demand and regeneration
of battery and supercapacitor of the hybrid system
during different vehicle scenarios for the SC03 drive
cycle. In mode M1 and M4, the supercapacitor plays
a crucial role by providing peak power to the vehicle
and absorbing regenerative power during regenera-
tive braking, this behaviour is a consequence of the
supercapacitor’s frequent charging and discharging
cycles, which aim to maintain its SOC and prevent
continuous depletion. The voltage of the battery at
the start is 140 V, and then ranges between 125 and
to 135 V.

From Figure 16, it is seen that the SOC of the
hybrid system battery is decreasing slower than that
of the normal system battery because the battery in
HESS only supplies power in average power demand
mode M3 not in peak power demand. The SOC of the
supercapacitor is decreasing in peak power demand
mode M1 and increasing in regenerative braking mode
M4, and it also increases due to regenerative braking
energy recovery. At the end of the drive cycle, SOC of
the battery in HESS is 90.59 % and the normal system
battery SOC is 86.44 % which shows that less energy
is used to drive the vehicle.

Figure 17 shows the Energy consumption for test
cases, red curve shows the energy consumption from
the battery in the case where no regeneration is im-
plemented and only the battery supplies the power to
the vehicle and the consumed energy is 347.8 W h−1

while the second case, the blue curve, shows the total
energy consumption for HESS with regenerative brak-
ing, which is 285.15 W h−1. Therefore, 62.65 W h−1,
18.01 % of the total energy, is regenerated during re-
generative braking and the SOC of both the battery
and the supercapacitor is decreasing at a slower rates
as compared to the single-battery EV.

386



vol. 65 no. 4/2025 Energy management for electric vehicles with battery and supercapacitor

Figure 16. SOC of battery and supercapacitor of normal and hybrid systems for SCO3 (city) drive cycle.

Figure 17. Energy consumption for normal and hybrid systems for SCO3 (city) drive cycle.

6.2. US06 drive cycle results
The US06 is a high acceleration aggressive driv-
ing cycle. The average speed during the cycle
was 77.9 km h−1, and the maximum speed was
129.2 km h−1. In this cycle, the average current drawn
during a constant speed from the battery pack is 35 A.
In transient situations such as sudden acceleration
from a stationary position, the supercapacitor pro-
vides a peak current of Isc = 100 A, the charge and
discharge currents of the supercapacitor have high
peaks for short time instants. During regenerative
braking, the current can reach up to −50 A.

Figure 18a shows the vehicle speed for the US06
drive cycle with the current demand for batteries and
supercapacitors. It can be seen that the proposed
EMS supplies peak and average currents according to

the current demand at various speeds of the vehicle.
From the Figure 18b, it can be seen, that the peak

power of 9 kW is required for a short time when the
vehicle is accelerating from zero speed in M1 mode. At
that instant, the supercapacitor effectively provides
the starting peak power and then the battery provides
the average power of 3 kW during mode M3. During
mode M4 operation, regenerative energy is stored in
the supercapacitor, as shown by the negative power
displayed in the graph during sudden braking. Most
of the regenerative braking energy is stored during
sudden braking.

Figure 18c shows the SOC of a 20-Ah normal system
battery. The starting value is 100 %, and it slowly
decreases during to cycle to 82.38 %. While for the
HESS system, the SOC of the battery at the end is
86.36 %.

387



A. S. Bhagat, V. Kalkhambkar, P. P. Gupta, V. Prakash Acta Polytechnica

(a). Battery and supercapacitor currents and vehicle speed.

(b). Battery and supercapacitor power.

(c). State of charge (SOC) of battery and supercapacitor.

(d). Energy consumption for normal and hybrid systems.

Figure 18. Simulation results for US06 (urban) drive cycle.

388



vol. 65 no. 4/2025 Energy management for electric vehicles with battery and supercapacitor

Figure 18d shows the energy consumption from the
battery and supercapacitor in two test cases, red curve
shows the energy consumption from the battery for
the first case, in which no regeneration is implemented
and only the battery supplies and power to the vehicle
and the consumed energy is 447.6 W h−1, while the
second case, the blue curve, shows the total energy
consumption of HESS with regenerative braking which
is 335.21 W h−1. Therefore, 112.39 W h−1, 25.1 % en-
ergy is regenerated during regenerative braking and
the SOC of both the battery and the supercapacitor is
decreasing at a slower rate as compared to the single-
battery EV. The battery contributes 80.48 % and the
supercapacitor 19.51 %.

6.3. HWFET drive cycle results
Figure 19a shows the vehicle speed for the HWFET
drive cycle with the current demand on batteries and
supercapacitors. In this cycle, the vehicle is drawing
an average current of Ib = 25 A. The supercapacitor
supplies a peak current when sudden acceleration is
required to reach a speed of 95 km h−1 from 40 km h−1,
which in this case was a total of 300 sec. In this
highway cycle, speed decreases for short instances but
there is no sudden braking. Regenerative energy is
captured effectively at the end of the drive cycle when
braking is applied at a speed of 80 km h−1 and the
vehicle comes to a stop.

Figure 19b shows the power supplied by the bat-
tery and supercapacitor during the HWFET drive
cycle and shows the expected outcome of the pro-
posed energy management strategy, which is designed
based on the rule-based controller by splitting the
high and low-frequency components of the battery
and the supercapacitor. The highest power supplied
by the supercapacitor during M1 mode is 4.5 kW at
t = 300 sec. During the highway drive cycle, vehi-
cles there are only small power fluctuations and the
average power in M3 mode is 4 kW.

From Figure 19c it can be seen that during the
HWFET drive cycle, the regenerative energy does not
have a significant impact, as the SOC of the normal
system battery at the end of the cycle is 80.64 %
and 81.53 % for the hybrid system battery, which
means an improvement of only 1 %. As there are no
high power demand fluctuations, the single battery
system is sufficient to provide power to the vehicle on
a highway.

Figure 19d shows the energy consumption from the
battery and the supercapacitor, only 2 % of the energy
required to run a vehicle is recovered during regenera-
tive braking. The total energy consumed by the single
battery system is 498.2 W h−1, and the total energy
consumed by the hybrid system is 488.45 W h−1. The
battery contributes 97.43 % of the energy, while the
supercapacitor contributes 2.5 %.

These results, obtained from different drive cycles,
clearly show that different driving patterns signifi-
cantly affect the regenerative braking energy recov-

ery performance of electric vehicles, as well as the
efficiency, cost, and performance of energy storage
devices, and most importantly, the driving range of
the vehicle on a single charge of the on-board energy
storage system.

As shown in Table 5, MATLAB simulation data for
the comparison of energy consumption of vehicles for
different drive cycles are recorded at the end of each
cycle. It can be seen that in the case of the US06
drive cycle with a single battery system, the energy
consumption rate was 447.9 W h−1, and in the case
of an a supercapacitor, it was 335.21 W h−1, which
is a 25.1 % lesser energy consumption. During re-
generative braking, 112.39 W h−1 of energy is stored
in the supercapacitor and then supplied to the drive
unit in peak power demand mode. During the SC03
drive cycle, 62.65 W h−1 of energy is stored in the
supercapacitor through regeneration, and a total en-
ergy consumption reduction of 18 % is reached for the
hybrid storage system proposed in this paper.

Table 6 shows the observed SOC data for different
MATLAB simulations tested for each drive cycle for
the normal and hybrid systems. The SOC of the bat-
tery and supercapacitor is recorded at the start and
end of each drive cycle for the same energy manage-
ment strategy. At the start of each drive cycle, the
SOC for both the battery and the supercapacitor is
100 %. As can be seen in Table 6, the SOC of the
battery changes differently in normal and hybrid sys-
tems. The SOC for the hybrid system is higher than
that of the normal system for every drive cycle. This
means that the battery of the hybrid system depletes
at a slower rate, which in turn means a longer range.
In the SC03 drive cycle, the battery supplied 85.56 %
of the energy and the supercapacitor supplied 14.43 %.
For the US06 drive cycle, the battery supplied 80.48 %
and the supercapacitor supplied 19.51 %. For the
HWFET drive cycle, the battery supplied 97.43 %
and the supercapacitor supplied only 2.5 %. During
the highway cycle, the supercapacitor supplied just
2.5 %, showing a very small contribution. The energy
shared by the battery and the supercapacitor is shown
in the second column in Table 6 which only shows the
SOC of the battery. The hybrid system SOC is shown
in columns 3 and 4. The energy contributed by the
battery and the supercapacitor, i.e. HESS, is shown
in columns 5 and 6. For example, in the SC03 drive
cycle, the battery contributed 85.56 % of the energy
and the supercapacitor contributed 14.43 %. From
this individual energy contribution percentage, we
can determine the optimal battery and supercapacitor
ratings for short drive cycles.

6.4. Result summary
The effectiveness of the hybrid system varies depend-
ing on the driving cycles. The main objectives of the
proposed system are to reduce the peak current of
the battery pack using a supercapacitor pack, thereby
reducing stress on the battery pack and increasing

389



A. S. Bhagat, V. Kalkhambkar, P. P. Gupta, V. Prakash Acta Polytechnica

(a). Battery and supercapacitor currents and vehicle speed.

(b). Battery and supercapacitor power.

(c). State of charge (SOC) of battery and supercapacitor.

(d). Energy consumption for normal and hybrid systems.

Figure 19. Simulation results for HWFET (highway) drive cycle.

390



vol. 65 no. 4/2025 Energy management for electric vehicles with battery and supercapacitor

Normal HESS Bat HESS SC HESS total Energy Energy
Drive cycles system energy energy energy energy saving saving

[Watt/Hr] [Watt/Hr] [Watt/Hr] [Watt/Hr] [Watt/Hr] [%]
SCO3 (city) 347.8 244 41.15 285.15 62.65 18.0118
US06 (urban) 447.6 269.8 65.41 335.21 112.39 25.1
HWFET(highway) 498.2 475.9 12.55 488.45 9.75 1.95
BCD (city) 980.3 642.4 123.1 765.5 214.8 21

Table 5. Energy consumption table for different driving cycle.

Drive
cycle

Normal HESS
SOC [%]

HESS
SOC [%]

HESS energy
share [%]system

SOC [%]
Battery Battery Supercapacitor Battery SC

SCO3 86.44 90.59 93.81 85.56 14.43
US06 82.38 86.36 89.58 80.48 19.51
HWFET 80.64 81.53 99.13 97.43 2.5
BCD 61.44 74.98 79.47 83.91 16.08

Table 6. State of charge SOC and energy contribution in hybrid system.

its life. Another main objective is to effectively store
regenerative braking energy using the fast-charging
characteristics of the SC, which overcome the limi-
tations of the battery. To implement and validate
our energy management strategy, we created a vehicle
model which we tested in the MATLAB Simulink envi-
ronment using standard system parameters to mimic
vehicle dynamics and all integrated HESS components.
The model is tested using standard city, urban, and
highway drive cycles. The benefits of regenerative
braking can be seen in the city drive cycle due to
the frequent braking. In real-world scenarios, vehicle
driving patterns and road conditions are continuously
change, combining city, urban, and highway driving,
including uphill and downhill driving in hilly areas.
While we generate more energy during the city drive
cycle, we also utilise the benefits of the hybrid energy
storage system during sudden acceleration and uphill
driving on the highway, using the stored regenerative
braking energy.

Due to the integration of HESS and the energy man-
agement strategy, we can achieve an increased driving
range, reduced operational costs, an improved battery
lifespan, and high dynamic performance. These bene-
fits justify the implementation of this system, despite
them not being consistent across all driving conditions.
In a highway driving cycle, there are fewer braking in-
stances, so energy recovery is reduced. To improve the
energy recovery on the highway, we can implement
alternative strategies and system configurations to
enhance energy recovery. Firstly, maximum priority
should be given to the SC absorbing regenerative en-
ergy instead of using a mechanical friction brake. The
highway road conditions are more predictable than
urban conditions, therefore, predictive control can
be used for highway braking, optimising the energy

recovery. Using adaptive cruise control with smooth
deceleration for HESS improves regenerative brak-
ing, especially in combination with specially designed
PMSM machines, which give high output regenerative
energy, and a motor that uses double stator winding
or a separate generator set. Other ways to improve
the efficiency include using mild regenerative brak-
ing to maintain speed and avoid sudden braking and
using economic mode, which is designed to improve
regeneration and reduce power consumption. In order
to tailor the system to different user profiles or vehicle
applications, we suggest a flexible and adaptive system
design that is utilised for different user profiles and
vehicle applications. To differentiate between users’
driving cycles, a machine learning based approach for
classifying city, highway, uphill, and downhill driving
cycle patterns can be used, which automatically learns
and optimally adapts the energy flow. This classifica-
tion provides an optimal sizing ratio for the battery
and SC system. Based on the identified driving pro-
files, the EMS can be adjusted to recognise driving
patterns and achieve high performance by utilising
a hybrid energy storage system.

7. Conclusion
This paper proposes an energy management system
designed for a hybrid storage system combining bat-
teries and supercapacitors, implemented in MATLAB
Simulink modelling for various driving cycles. The
modelling results show that the implemented rule-
based energy management strategy effectively splits
the peak and average power, and captures regenerative
braking energy in the supercapacitors. Energy Man-
agement strategy plays a crucial role in hybrid energy
storage systems used in different driving conditions.
Regenerative braking energy recovery changes depend-

391



A. S. Bhagat, V. Kalkhambkar, P. P. Gupta, V. Prakash Acta Polytechnica

ing on the vehicle driver’s behavior. Integration of
supercapacitors reduces the charging-discharging cy-
cles of the battery, so the life cycle and degradation
rate of the lithium-ion battery reduces and the overall
energy storage system works efficiently in long-term
operation. SC improve the performance of EVs by
overcoming problems faced by single-battery vehicles.

As per results obtained from the MATLAB Simulink
model, the energy used in the hybrid system is reduced
by up to 25 % during a US06 city drive cycle com-
pared to normal single-battery system. This is due
to continuous acceleration and deceleration driving
patterns. The benefits from the battery/SC hybrid
energy storage system include improving the lifespan
of the energy storage system, recovering kinetic energy,
and a better overall vehicle performance. With the
proposed effective energy management control strat-
egy, the performance of an EV is improved with the
low energy consumption and extended driving range
on a single charge. This hybridisation further reduces
the necessity of a large battery pack.

In future work, we will implement an energy man-
agement strategy with a power blending feature based
on the state of charge of the battery and superca-
pacitor to enhance the range and performance of the
vehicle.

References
[1] J. Armenta, C. Núñez, N. Visairo, I. Lázaro. An

advanced energy management system for controlling the
ultracapacitor discharge and improving the electric
vehicle range. Journal of Power Sources 284:452–458,
2015.
https://doi.org/10.1016/j.jpowsour.2015.03.056

[2] L. Kouchachvili, W. Yaïci, E. Entchev. Hybrid
battery/supercapacitor energy storage system for the
electric vehicles. Journal of Power Sources 374:237–248,
2018.
https://doi.org/10.1016/j.jpowsour.2017.11.040

[3] N. M. Jamadar, A. Bhagat, S. Gaikwad, et al.
Reliability assessment of supercapacitor for electric
vehicle with hybrid energy storage. Life Cycle
Reliability and Safety Engineering 11(1):49–59, 2022.
https://doi.org/10.1007/s41872-022-00184-5

[4] Z. Zou, J. Cao, B. Cao, W. Chen. Evaluation strategy
of regenerative braking energy for supercapacitor
vehicle. ISA transactions 55:234–240, 2015.
https://doi.org/10.1016/j.isatra.2014.09.011

[5] M. K. Hasan, M. Mahmud, A. K. M. A. Habib, et al.
Review of electric vehicle energy storage and
management system: Standards, issues, and challenges.
Journal of energy storage 41:102940, 2021.
https://doi.org/10.1016/j.est.2021.102940

[6] W. Zhao, G. Wu, C. Wang, et al. Energy transfer and
utilization efficiency of regenerative braking with hybrid
energy storage system. Journal of Power Sources
427:174–183, 2019.
https://doi.org/10.1016/j.jpowsour.2019.04.083

[7] C. Capasso, D. Lauria, O. Veneri. Experimental
evaluation of model-based control strategies of
sodium-nickel chloride battery plus supercapacitor
hybrid storage systems for urban electric vehicles.
Applied energy 228:2478–2489, 2018.
https://doi.org/10.1016/j.apenergy.2018.05.049

[8] A. T. Hamada, M. F. Orhan. An overview of
regenerative braking systems. Journal of Energy
Storage 52:105033, 2022.
https://doi.org/10.1016/j.est.2022.105033

[9] K. Itani, A. De Bernardinis, Z. Khatir, A. Jammal.
Comparative analysis of two hybrid energy storage
systems used in a two front wheel driven electric vehicle
during extreme start-up and regenerative braking
operations. Energy Conversion and Management
144:69–87, 2017.
https://doi.org/10.1016/j.enconman.2017.04.036

[10] Z. Fu, Z. Li, P. Si, F. Tao. A hierarchical energy
management strategy for fuel
cell/battery/supercapacitor hybrid electric vehicles.
International Journal of Hydrogen Energy
44(39):22146–22159, 2019.
https://doi.org/10.1016/j.ijhydene.2019.06.158

[11] N.-D. Nguyen, C. Yoon, Y. I. Lee. A standalone
energy management system of battery/supercapacitor
hybrid energy storage system for electric vehicles using
model predictive control. IEEE Transactions on
Industrial Electronics 70(5):5104–5114, 2023.
https://doi.org/10.1109/TIE.2022.3186369

[12] I. Shchur, I. Bilyakovskyy, V. Turkovskyi.
Improvement of switched structure semi-active
battery/supercapacitor hybrid energy storage system
for electric vehicles. IET Electrical Systems in
Transportation 11(3):241–255, 2021.
https://doi.org/10.1049/els2.12017

[13] Z. Song, J. Hou, H. Hofmann, et al. Sliding-mode
and Lyapunov function-based control for
battery/supercapacitor hybrid energy storage system
used in electric vehicles. Energy 122:601–612, 2017.
https://doi.org/10.1016/j.energy.2017.01.098

[14] F. S. Garcia, A. A. Ferreira, J. A. Pomilio. Control
strategy for battery-ultracapacitor hybrid energy
storage system. In 2009 Twenty-Fourth Annual IEEE
Applied Power Electronics Conference and Exposition,
pp. 826–832. IEEE, 2009.
https://doi.org/10.1109/APEC.2009.4802757

[15] S. Tibude, G. Goyal, A. Ranjan, S. Bodkhe. Advanced
energy management strategies for hybrid energy storage
systems in electric vehicles: A comprehensive review. In
2025 Fourth International Conference on Power,
Control and Computing Technologies (ICPC2T), pp.
855–860. IEEE, 2025. https:
//doi.org/10.1109/ICPC2T63847.2025.10958756

[16] S. Bansal, S. Dey, M. Khanra. Energy storage sizing
in plug-in electric vehicles: Driving cycle uncertainty
effect analysis and machine learning based sizing
framework. Journal of Energy Storage 41:102864, 2021.
https://doi.org/10.1016/j.est.2021.102864

[17] F. Jin, M. Wang, C. Hu. A fuzzy logic based power
management strategy for hybrid energy storage system
in hybrid electric vehicles considering battery

392

https://doi.org/10.1016/j.jpowsour.2015.03.056
https://doi.org/10.1016/j.jpowsour.2017.11.040
https://doi.org/10.1007/s41872-022-00184-5
https://doi.org/10.1016/j.isatra.2014.09.011
https://doi.org/10.1016/j.est.2021.102940
https://doi.org/10.1016/j.jpowsour.2019.04.083
https://doi.org/10.1016/j.apenergy.2018.05.049
https://doi.org/10.1016/j.est.2022.105033
https://doi.org/10.1016/j.enconman.2017.04.036
https://doi.org/10.1016/j.ijhydene.2019.06.158
https://doi.org/10.1109/TIE.2022.3186369
https://doi.org/10.1049/els2.12017
https://doi.org/10.1016/j.energy.2017.01.098
https://doi.org/10.1109/APEC.2009.4802757
https://doi.org/10.1109/ICPC2T63847.2025.10958756
https://doi.org/10.1109/ICPC2T63847.2025.10958756
https://doi.org/10.1016/j.est.2021.102864


vol. 65 no. 4/2025 Energy management for electric vehicles with battery and supercapacitor

degradation. In 2016 IEEE transportation electrification
conference and expo (ITEC), pp. 1–7. IEEE, 2016.
https://doi.org/10.1109/ITEC.2016.7520207

[18] J. Hu, D. Liu, C. Du, et al. Intelligent energy
management strategy of hybrid energy storage system
for electric vehicle based on driving pattern recognition.
Energy 198:117298, 2020.
https://doi.org/10.1016/j.energy.2020.117298

[19] Y. Yang, Y. Xu, H. Zhang, et al. Research on the
energy management strategy of extended range electric
vehicles based on a hybrid energy storage system.
Energy Reports 8:6602–6623, 2022.
https://doi.org/10.1016/j.egyr.2022.05.013

[20] Z. Bai, Z. Yan, X. Wu, et al. H∞ control for
battery/supercapacitor hybrid energy storage system
used in electric vehicles. International Journal of
Automotive Technology 20(6):1287–1296, 2019.
https://doi.org/10.1007/s12239-019-0120-x

[21] C. Qiu, G. Wang, M. Meng, Y. Shen. A new strategy
of controlling the regenerative braking system of electric
vehicles in safety-critical road situations. Energy
149:329–340, 2018.
https://doi.org/10.1016/j.energy.2018.02.046

[22] P. Thounthong, P. Sethakul, S. Rael, B. Davat.
Performance evaluation of fuel
cell/battery/supercapacitor hybrid power source for
vehicle applications. In 2009 IEEE Industry
Applications Society Annual Meeting, pp. 1–8. IEEE,
2009. https://doi.org/10.1109/IAS.2009.5324894

[23] K. Itani, A. De Bernardinis, Z. Khatir, et al.
Regenerative braking modeling, control, and simulation
of a hybrid energy storage system for an electric vehicle
in extreme conditions. IEEE Transactions on
Transportation Electrification 2(4):465–479, 2016.
https://doi.org/10.1109/TTE.2016.2608763

[24] C. S. Phutane, P. K. Katti, G. B. Shinde. Analysis of
hybrid energy storage system for electric vehicles. In
2022 IEEE 3rd Global Conference for Advancement in
Technology (GCAT), pp. 1–6. IEEE, 2022.
https://doi.org/10.1109/GCAT55367.2022.9972029

[25] A. K. Podder, O. Chakraborty, S. Islam, et al.
Control strategies of different hybrid energy storage
systems for electric vehicles applications. IEEE Access
9:51865–51895, 2021.
https://doi.org/10.1109/ACCESS.2021.3069593

[26] M. B. F. Ahsan, S. Mekhilef, T. K. Soon, et al.
Lithium-ion battery and supercapacitor-based hybrid
energy storage system for electric vehicle applications:
A review. International Journal of Energy Research
46(14):19826–19854, 2022.
https://doi.org/10.1002/er.8439

[27] L. Guo, P. Hu, H. Wei. Development of
supercapacitor hybrid electric vehicle. Journal of
Energy Storage 65:107269, 2023.
https://doi.org/10.1016/j.est.2023.107269

[28] Y. Li, X. Huang, D. Liu, et al. Hybrid energy storage
system and energy distribution strategy for four-wheel
independent-drive electric vehicles. Journal of Cleaner
Production 220:756–770, 2019.
https://doi.org/10.1016/j.jclepro.2019.01.257

[29] V. Mounica, Y. P. Obulesu. Hybrid power
management strategy with fuel cell, battery, and
supercapacitor for fuel economy in hybrid electric
vehicle application. Energies 15(12):4185, 2022.
https://doi.org/10.3390/en15124185

[30] B. Wang, J. Xu, B. Cao, X. Zhou. A novel
multimode hybrid energy storage system and its energy
management strategy for electric vehicles. Journal of
Power Sources 281:432–443, 2015.
https://doi.org/10.1016/j.jpowsour.2015.02.012

[31] Y. Li, O. P. Adeleke, X. Xu. Methods and applications
of energy saving control of in-wheel motor drive system
in electric vehicles: A comprehensive review. Journal of
Renewable and Sustainable Energy 11(6):062701, 2019.
https://doi.org/10.1063/1.5129070

[32] L. Hu, Q. Tian, C. Zou, et al. A study on energy
distribution strategy of electric vehicle hybrid energy
storage system considering driving style based on real
urban driving data. Renewable and Sustainable Energy
Reviews 162:112416, 2022.
https://doi.org/10.1016/j.rser.2022.112416

[33] M. A. Hannan, Z. Abd Ghani, A. Mohamed, et al.
An enhanced inverter controller for PV applications
using the dSPACE platform. International Journal of
Photoenergy 2010:457562, 2010.
https://doi.org/10.1155/2010/457562

[34] B. G. Pollet, I. Staffell, J. L. Shang. Current status
of hybrid, battery and fuel cell electric vehicles: From
electrochemistry to market prospects. Electrochimica
Acta 84:235–249, 2012.
https://doi.org/10.1016/j.electacta.2012.03.172

[35] M. A. Hannan, F. A. Azidin, A. Mohamed. Hybrid
electric vehicles and their challenges: A review.
Renewable and Sustainable Energy Reviews 29:135–150,
2014. https://doi.org/10.1016/j.rser.2013.08.097

[36] H. Rezk, A. Fathy. Combining proportional integral
and fuzzy logic control strategies to improve performance
of energy management of fuel cell electric vehicles.
International Journal of Thermofluids 26:101076, 2025.
https://doi.org/10.1016/j.ijft.2025.101076

[37] H. Maghfiroh, O. Wahyunggoro, A. I. Cahyadi.
Energy management in hybrid electric and hybrid
energy storage system vehicles: A fuzzy logic controller
review. IEEE Access 12:56097–56109, 2024.
https://doi.org/10.1109/ACCESS.2024.3390436

[38] M. A. Hannan, A. Mohamed. PSCAD/EMTDC
simulation of unified series-shunt compensator for power
quality improvement. IEEE Transactions on power
delivery 20(2):1650–1656, 2005.
https://doi.org/10.1109/TPWRD.2004.833875

[39] H. Jiang, Y. Zhao, S. Ma. Dual-layer energy
management strategy for a hybrid energy storage system
to enhance PHEV performance. Energies 18(7):1667,
2025. https://doi.org/10.3390/en18071667

[40] M. A. Hannan, F. A. Azidin, A. Mohamed.
Multi-sources model and control algorithm of an energy
management system for light electric vehicles. Energy
Conversion and Management 62:123–130, 2012.
https://doi.org/10.1016/j.enconman.2012.04.001

393

https://doi.org/10.1109/ITEC.2016.7520207
https://doi.org/10.1016/j.energy.2020.117298
https://doi.org/10.1016/j.egyr.2022.05.013
https://doi.org/10.1007/s12239-019-0120-x
https://doi.org/10.1016/j.energy.2018.02.046
https://doi.org/10.1109/IAS.2009.5324894
https://doi.org/10.1109/TTE.2016.2608763
https://doi.org/10.1109/GCAT55367.2022.9972029
https://doi.org/10.1109/ACCESS.2021.3069593
https://doi.org/10.1002/er.8439
https://doi.org/10.1016/j.est.2023.107269
https://doi.org/10.1016/j.jclepro.2019.01.257
https://doi.org/10.3390/en15124185
https://doi.org/10.1016/j.jpowsour.2015.02.012
https://doi.org/10.1063/1.5129070
https://doi.org/10.1016/j.rser.2022.112416
https://doi.org/10.1155/2010/457562
https://doi.org/10.1016/j.electacta.2012.03.172
https://doi.org/10.1016/j.rser.2013.08.097
https://doi.org/10.1016/j.ijft.2025.101076
https://doi.org/10.1109/ACCESS.2024.3390436
https://doi.org/10.1109/TPWRD.2004.833875
https://doi.org/10.3390/en18071667
https://doi.org/10.1016/j.enconman.2012.04.001


A. S. Bhagat, V. Kalkhambkar, P. P. Gupta, V. Prakash Acta Polytechnica

[41] R. S. Sankarkumar, R. Natarajan. Energy
management techniques and topologies suitable for
hybrid energy storage system powered electric vehicles:
An overview. International Transactions on Electrical
Energy Systems 31(4):e12819, 2021.
https://doi.org/10.1002/2050-7038.12819

[42] D. T. Tu. A novel concept of hybrid electric public
bus with power management system in Vietnam’s
condition. Journal of Advanced Manufacturing
Technology (JAMT) 17(2):71–88, 2023.
https://jamt.utem.edu.my/jamt/article/view/6528

[43] Y. Tong, I. Salhi, Q. Wang, et al. Bidirectional
DC-DC converter topologies for hybrid energy storage
systems in electric vehicles: A comprehensive review.
Energies 18(9):2312, 2025.
https://doi.org/10.3390/en18092312

[44] H. Rezaei, S. E. Abdollahi, S. Abdollahi, S. Filizadeh.
Energy management strategies of battery-ultracapacitor
hybrid storage systems for electric vehicles: Review,
challenges, and future trends. Journal of Energy
Storage 53:105045, 2022.
https://doi.org/10.1016/j.est.2022.105045

[45] A. S. Veerendra, M. R. B. Mohamed, F. P. García
Márquez. Energy management control strategies for
energy storage systems of hybrid electric vehicle:

A review. Energy Storage 6(1):e573, 2024.
https://doi.org/10.1002/est2.573

[46] N. Kumaresan, A. Rammohan. A comprehensive
review on energy management strategies of hybrid
energy storage systems for electric vehicles. Journal of
the Brazilian Society of Mechanical Sciences and
Engineering 46(3):146, 2024.
https://doi.org/10.1007/s40430-024-04736-x

[47] M. S. Ramkumar, S. Muthukrishnan, J. Giri, et al.
Optimizing battery and supercapacitor management in
electric vehicles: A hybrid approach for enhanced
performance and reduced harmonics. Case Studies in
Thermal Engineering 68:105815, 2025.
https://doi.org/10.1016/j.csite.2025.105815

[48] I. Kranthikumar, C. H. Srinivas, T. V. Kiran, et al.
A novel hybrid approach for efficient energy
management in battery and supercapacitor based
hybrid energy storage systems for electric vehicles.
Electrical Engineering 107(1):1–17, 2025.
https://doi.org/10.1007/s00202-024-02483-9

[49] O. A. AlKawak, J. R. R. Kumar, S. S. Daniel,
C. V. K. Reddy. Hybrid method based energy
management of electric vehicles using battery-super
capacitor energy storage. Journal of Energy Storage
77:109835, 2024.
https://doi.org/10.1016/j.est.2023.109835

394

https://doi.org/10.1002/2050-7038.12819
https://jamt.utem.edu.my/jamt/article/view/6528
https://doi.org/10.3390/en18092312
https://doi.org/10.1016/j.est.2022.105045
https://doi.org/10.1002/est2.573
https://doi.org/10.1007/s40430-024-04736-x
https://doi.org/10.1016/j.csite.2025.105815
https://doi.org/10.1007/s00202-024-02483-9
https://doi.org/10.1016/j.est.2023.109835

	Acta Polytechnica 65(4):371–394, 2025
	1 Introduction
	2 Modelling of system components
	2.1 Electric vehicle model
	2.2 Battery model
	2.3 Supercapacitor model
	2.4 Converter model

	3 Hybrid energy storage system
	3.1 HESS powertrain
	3.2 Necessity of HESS
	3.3 Challenges in HESS
	3.3.1 System design and sizing
	3.3.2 Topology and converter design challenges
	3.3.3 Control strategy and energy management challenge
	3.3.4 Thermal management
	3.3.5 Protection and fault tolerance
	3.3.6 Cost and space requirement
	3.3.7 Packaging


	4 Energy management in HESS
	4.1 Implementation of energy management strategy
	4.2 Control and maintenance
	4.3 Real time optimization
	4.4 Risk mitigation in dynamic and unpredictable driving environments

	5 Simulation and analysis of the proposed hybrid energy storage system
	6 Simulation results and discussions
	6.1 SC03 drive cycle results
	6.2 US06 drive cycle results
	6.3 HWFET drive cycle results
	6.4 Result summary

	7 Conclusion
	References