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Energy Management Systems for Electric
Vehicles: A Comprehensive Review of
Technologies and Trends
MD SHAHIN MUNSI1, HICHAM CHAOUI2, (Senior Member, IEEE)
1Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA (e-mail: mmunsi@ttu.edu)
2Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA (e-mail: hicham.chaoui@carleton.ca)
Corresponding author: Md Shahin Munsi (e-mail: mmunsi@ttu.edu).
ABSTRACT As the demand for electric vehicles (EVs) continues to surge, improvements to energy
management systems (EMS) prove essential for improving their efficiency, performance, and sustainability.
This paper covers the distinctive challenges in designing EMS for a range of electric vehicles, such as
electrically powered automobiles, split drive cars, and P-HEVs. It also covers significant achievements and
proposed solutions to these issues. The powertrain concept for series, parallel, series-parallel, and complex
hybrid electric cars was also disclosed in this study. Much of this analysis is dedicated to investigating the
various control strategies used in EMS for various electric vehicle types, which include global-optimization
approaches, fuzzy rule based, and real-time optimization-oriented strategies. The study thoroughly evaluates
the strengths and shortcomings of various electric vehicle strategies, offering valuable insights into their
practical implementation and effectiveness across different EV models, such as BEVs, HEVs, and PHEVs.
INDEX TERMS HEV, PHEV, Power train, EMS, Fuzzy based EMS, SoC, Deep RL.
I. INTRODUCTION
THE energy problem and global warming are undoubt-
edly the most severe issues today. The excessive usage
of non-renewable energy continues to bring the planet to a
catastrophic event [1]. Using conventional energy sources is
the main contributor to greenhouse gas emissions [2] [3].
These energy resources are limited and consumed daily due
to rising energy consumption demands.
According to the Environmental Protection Agency (EPA),
the average passenger vehicle generates approximately 400
grams of carbon dioxide per mile and approximately 4.6
metric tons of carbon dioxide (CO2) annually. This assumes
that the average gasoline vehicle on the road today gets 22.2
miles per gallon and travels 11,500 miles per year. Every
gallon of gasoline burned emits approximately 8,887 grams
of greenhouse gases [4]. According to the IEA report, the
transportation sector contributed 7.98 gigatons (Gt) of carbon
dioxide (CO2) emissions in 2022, constituting roughly 23%
of total emissions. Figure 1 represents the percentage of
global total emissions in different sectors [5].
The California Air Resources Board adopted guidelines
in October 1990 requiring that 2% of all vehicles sold in
the state between 1998 and 2002 be emission-free and that
10% of vehicles placed on the market have zero emissions
by 2003 [6]. Since there is no potential for increasing the
fuel efficiency of typical vehicles and all suggested strategies
will be detrimental to the growth of the manufacturing sector,
creating new energy vehicles has been considered one of
the most probable and realistic options. [7]. As a result, the
entirety of the world is moving toward the usage of clean
energy. Electric vehicles (EVs) are sustainable, have minimal
FIGURE 1. Worldwide carbon dioxide (CO2) emissions (2022)
gasoline use, are pollutant-free, and are an innovative urban
transportation alternative [8]. Chargeable battery sets, which
frequently use lithium-ion, also known as Li-ion, batteries,
are the main source of energy needed by either one or several
motors powered by electricity (EMs) for propulsion in electric
vehicles (EVs). [9]. However, two critical challenges to com-
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Md Shahin Munsi et al.: Energy Management Systems for Electric Vehicles: A Comprehensive Review of Technologies and Trends
mercializing EVs exist low driving range and high initial cost.
The present EV energy source technologies cannot readily
overcome these challenges. Regardless of how environmen-
tally friendly it is, people may not purchase an EV if its range
between charges is 100-200 km [6].
To address the challenges associated with electromobile,
hybrid electric vehicles (HEVs) have been introduced. A
combination of an electricity-powered motor, battery pack,
and gasoline engine powers an electric hybrid vehicle. The
wheels can be powered by the electricity stored in the battery
unit, independently or in association with the combustion
engine. [9]. HEVs feature distinct advantages, which include
greatly extending the initial EV driving range by two to four
times and enabling the ease of quick refilling. Another advan-
tage is that HEVs require minimum changes to the current
energy infrastructure, and they release much less pollution
and consume less fuel than typical vehicle engines while
providing a comparable driving range. The fundamental lim-
itations of HEVs are the loss of the zero-emission principle
and increased complexity. Despite this, HEVs serve as a
bridge to zero-emission vehicles and a feasible strategy for
commercializing super-ultra-low-emission vehicles [6].
Plug-in hybrid electric vehicles (PHEVs) are electrically
powered automobiles that can be recharged by connecting
them to an external power source [10]. This feature allows
a PHEV to operate entirely on electricity until the IC engine
turns on when the state of the charge (SoC) of the battery
drops reduced to a predetermined lower threshold. PHEVs are
distinguished from standard HEVs in that they prioritize the
primary power source is the energy kept in the electric storage
system (ESS), while offering a novel approach to the electric
motor system (EMS) that improves fuel efficiency [11], [12].
Two types of vehicles from this category have been attainable
in the marketplace: blended and extended-range (EREVs)
PHEVs. EREVs usually utilize a series framework, in which
the conventional engine solely provides energy, and the in-
duction motor moves the drive train. One such instance is the
i3 from BMW with an extension of range that operates in this
manner, using the engine only when the battery is completely
drained. In contrast, blended PHEVs often have the engine
directly powering the car, with an electric-motor acting as
either a motor or generator depending on power demands and
the battery’s SoC, as demonstrated in the Chevrolet Volt [13].
The remaining tasks are structured in the following
manner:section II contains the power train configurations,
section III deals with HEV’s modes of functioning, Section
IV represents the mathematical modeling of HEV, section V
describes the various approaches to energy management that
electric vehicles employ, section VI reviews the literatures
based on EMS, and section VII contains the conclusion part
of this work.
II. POWER TRAIN CONFIGURATIONS
The primary issue when developing dual-power vehicles is
appropriately controlling energy transfer from sources to
loads while avoiding energy losses, a factor heavily influ-
enced by driving patterns. It contains various electrical com-
ponents, including embedded power-train controllers, power
electronics, continuously variable transmissions, and electric
machinery [14]. Three powertrain configurations are recog-
nized for modern electric vehicles: parallel, series, and power-
split (series-parallel). [15]. The customer’s preferences usu-
ally determine the configuration used. As a result, the critical
difficulty in HEV development lies in finding the most effec-
tive approach to distribute power while attaining the needed
performance within the system’s limits [7].
A. SERIES HEVS
In series HEVs, the primary mode of propulsion is pro-
vided by traction motors, with the reciprocating engine (ICE)
serving as a generator, and these motors draw power from
the battery [16]. In this configuration, no direct mechanical
link is enclosed by the ICE and the propelling axle of the
automobile. As a consequence, the traditional fuel-engine can
run in its most efficient range despite the speed and the re-
quired power, which makes the series hybrid powertrain more
straightforward in terms of both its configuration and energy
management [17]. Additionally, the new configuration boasts
a broader operational area and greater effectiveness than the
typical one. Figure 2 shows the Series Hybrid Electric Vehicle
schematic. However, they can experience significant energy
FIGURE 2. Schematic for a Series Hybrid Electric Drive
conversion losses because All the power generated by the ICE
needs to be initially changed through electric power [18]. The
battery reserves a part of the energy, while the rest drives the
M/G and runs the vehicle. Despite the relatively enhanced
effectiveness of the Electric Motors (EMs) and the high effi-
ciency of the ICE, several transformations in generated power
lead to a reduction in performance overall. [19]. Furthermore,
the aforementioned arrangement demands a larger induction
machine to meet the torque requirements since it is the sole
source of traction [20]. Various automobile manufacturers,
including Mitsubishi, Volvo, and BMW, have delved into the
potential development of series HEVs. However, even with
extensive research, the practical adoption of series HEVs
remains largely confined to extremely durable cars. While
this configuration generally offers the advantage of optimal
engine functioning with exceptional efficiency, that benefit
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is often counterbalanced by the necessity for robust and ex-
pensive accumulative sources exhibiting a significant energy
concentration. Robust storage devices are essential since, in
many situations, the electric driver may need to provide 50%
of the required power on its own. [21] [22].
B. PARALLEL HEVS
In the case of parallel HEVs, the gasoline-powered engine
and the induction machine are mechanically linked to the
vehicle’s output shaft, enabling them to contribute power
to propel the vehicle simultaneously [7]. The ICE connects
to a mechanical path, while the energy storage system path
is termed the electrical path, permitting bidirectional power
transmission [16]. The available EM optimizes engine per-
formance by adjusting its operational parameters to a range
that enhances efficiency. When there is little need for energy,
it operates as a generator; when more power is demanded, it
acts as an electric motor. [23]. Figure3 shows the power train
connection for Parallel-HEV. When the Energy storage packs
FIGURE 3. Diagram illustrating a Parallel Hybrid Electric Vehicle
reach the whole power level, the IC engine and the EM may
operate the car separately or simultaneously, depending on
the riding circumstances. During periods of minimal SoC, a
portion of the rotational force produced by the heat engine
is redirected to propel the electric motor, which functions
as a generator to replenish the battery pack [16]. This de-
sign’s primary advantage lies in its versatility in selecting the
capacity of the ESS and EM to be installed, as the highest
angular motion for the automobile is supplied in coordination
with the internal combustion engine, which can be operated
at a greater degree of performance compared to a traditional
vehicle [20].
However, this configuration may not be the most efficient
because of the persistent coupling that is mechanical across
the internal combustion driver and the output spindle [24].
Moreover, the electric power drive is unable to both refuel
the battery and contribute to propelling the transport concur-
rently. To maintain a balance between power assistance and
electric vehicle (EV) operations and avoid battery depletion,
careful control is required [25]. This issue becomes more
pronounced during city driving, where frequent start-and-stop
cycles can deplete the battery and lead the engine to oper-
ate in its less efficient range. Consequently, parallel HEVs
have a relatively minor market share despite the availability
of various models [20]. The Insight model, which Honda
launched and is categorized as a parallel-configured HEV, is
one particular example of a hybrid electric vehicle.
C. POWER SPLIT (SERIES-PARALLEL) CONFIGURATION
The setup resembles a parallel HEV, essentially resembling
a vehicle utilizing a Parallel structure with a smaller series
configuration integrated inside its layout [26]. Such a com-
bination effectively brought the benefits of the other two
architectures to mitigate its drawbacks. As an illustration,
the challenge of appropriately selecting the Energy Storage
System and Electric Motor in series configuration is resolved
as this design’s underlying principle is compatible with par-
allel structure [27]. Simultaneously, the difficulty of driving
in constantly changing traffic, which is disadvantageous to
parallel HEVs, is addressed by the capability to continue
recharging the power source even though the car idles [28].
Those achievements become achievable because of an energy
splitter, for instance, the planetary distribution installed in the
Prius car made by Toyota. As a result of these characteristics,
the series-parallel HEV has become the option preference
for numerous automakers in modern times. [26]. Figure 4
represents the schematic of the above-discussed arrangement.
One or more gear pairs are commonly employed in this ar-
rangement to connect the driveshaft, two electric power units,
and the CI machine [29]. The power-split hybrid powertrain,
also known as a power split device (PSD), is centered around
these PG sets. The PSD functions as a continuously variable
transmission (CVT), severing the ICE’s connection to the ve-
hicle’s speed and guaranteeing effective engine performance.
Therefore, the term "electronic-CVT" (E-CVT) is occasion-
ally used to refer to the PSD cars [13]. Compared to both
series and parallel HEVs, power-split HEVs can often achieve
higher fuel economy because of this decoupling capability,
especially when traveling an urban situations [30].
The power-splitting apparatus makes energy sent to the
transmission from the engine easier via the electrical or me-
chanical paths [31]. Like a series HEV, the PSD functions
in the electrical path by first converting some of the internal
combustion engine’s power into electrical potential using a
converter. This potential is subsequently applied to power the
motor or charge the battery. The PSD enables the framework
to function in the mechanical path as a parallel HEV, letting
the ICE send power straight to the driveshaft. As a result,
the benefits of the other two configurations are combined in
power-split HEVs [32], [33].
However, the electrical route experiences higher energy
loss compared to the mechanical pathway due to additional
energy conversions taking place. More ICE power is trans-
mitted across the electrical channel, which results in in-
creased energy loss brought on by the PSD [34]. Energy
transfer is most effective when the speed of either EM is
zero, which results in zero power transmission along the
engine-generator-motor path. This state is referred to as the
mechanical point. Power-split HEVs can occasionally show
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Md Shahin Munsi et al.: Energy Management Systems for Electric Vehicles: A Comprehensive Review of Technologies and Trends
greater energy losses than parallel HEVs due to the release of
power inside the electrical network, especially while cruising
at high speeds [35].
The Hybrid Synergy Drive (HSD) is an outstanding repre-
sentation of an input-split power-split hybrid system [35].
FIGURE 4. Schematic of Power Split Hybrid Electric Vehicle
III. HEV’S METHOD OF FUNCTIONING
The energy-storage system’s SoC fluctuates over time, in-
fluenced by the energy source that supplies the power for
propulsion. The SoC’s behavior is employed to indicate the
specific mode in which the energy-storage system operates,
such as charge-depleting (CD), electric vehicle (EV), and
charge-sustaining (CS) modes [36], [37].
The electric vehicle (EV) mode involves running the vehicle
exclusively on electricity from the electric machine till attains
a predefined level of charge or completes a specified session.
In this mode, the battery depletes quickly but can be replen-
ished by regenerative energy during braking. If the electric
machine cannot meet the vehicle’s power requirements, it will
trigger a mode change, causing the engine to start [38].
In Charge Depleting (CD) mode, the car could be run by
the combustion chamber and motor simultaneously, and the
engine also recharges the battery. However, SoC of the battery
will gradually decrease. If needed, cars running in this mode
can switch to CS option to charge the ESS [39].
Charge-sustaining (CS) mode bears similarities to CD
mode; the primary distinction lies in the preservation of the
State of Charge (SOC) of the Energy Storage System. This
means that the ICE supplies the typical force required for
shifting the vehicle while the battery provides the extra power
needed for acceleration and other dynamic demands [40].
IV. MATHEMATICAL MODELING OF HEV
1) State of Charge (SoC)
It is specified as a measure of its residual charge relative to
its total capabilities [41]. Efficient battery usage is reflected
in a better SoC profile. Maintaining a high SoC is always
preferable [42]. The performance of a battery is significantly
influenced by its SoC, which is mathematically expressed as
[43]:
SoCbat =VOCV −pV2
OCV −4Rinter Pbat
2Cinitial Rinter
(1)
In this equation (1), VOCV is the the battery’s voltage in an
open circuit, Cinitial represents initial charge volume, Rinter
denotes the inner resistance of the storage systems, while Pbat
stands for the output power of the battery.
2) Battery Model
There are many ways to model batteries, from simple
schematics to intricate ones [44]. A resistor and a constant
voltage source are coupled in series in the most basic model.
More complex models include extra parts to capture addi-
tional characteristics of batteries, like capacity and discharge
rate. Rather than analyzing the battery itself, these models
are primarily employed to evaluate the operation of circuits
coupled to the battery [45].
In a broader context, there are three fundamental categories
of battery models: those based on runtime, impedance, and
Thevenin principles. Impedance-based Models are particu-
larly effective since they accurately capture the active be-
havior of energy storage systems. These models leverage the
relationship between battery impedance and its state, which is
influenced by factors like state of charge (SoC), temperature,
life cycle, and charge/discharge current [46]. An illustration
of the improved battery cell model’s equivalent circuit is
presented in figure 5 [44]. The voltage measured in a battery
FIGURE 5. Schematic of improved battery cell
unit without a load connected is represented by VOCV , while
Rbc denotes the cell’s internal resistance. The electromagnetic
short-term double-layer effect is characterized by two param-
eters: resistance ( Rem) and capacitance (Cem ). The electro-
chemical long-term mass transport effect is also characterized
by two parameters: resistance (Rec) and capacitance (Cec ).
The battery cell’s inductance is denoted by Lbc, and the load
current is represented by IL[47]. The battery cell’s terminal
voltage (Vo) can be calculated using the following equation:
Vo=VOCV −IL×Rbc−1
Cem Z(I−Iem)dt −1
Cec Z(I−Iec)dt
−Lbc
dIL
dt(2)
3) The Fuel Cell
The electrical circuit model that represents the fuel cell (FC)
is shown by the following mathematical equation. In this
model, ˜
EOCV is the voltage of the FC when no current is
flowing, ˜
Rla and ˜
Rtr are the resistances of the FC layers and
the restriction during transfer. ˜
Cla is the capacitance of the
double layer [48] [49].
˜
Vout =˜
EOCV −˜
Vtr −˜
Rla˜
Ifc
˜
Ifc =˜
Cla d˜
Vtr
dt+˜
Vtr /˜
Rtr
(3)
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Md Shahin Munsi et al.: Energy Management Systems for Electric Vehicles: A Comprehensive Review of Technologies and Trends
4) The ultracapacitor
There are several ways to model ultracapacitors (UCs) in the
scientific literature. Three standard models are the distributed
constant model, the localized constant model, and the behav-
ioral model with two branches [50], [51]. The generalized
model consists of a capacitance, ˜
Cuc, and a series resistance,
˜
Ruc. The mathematical equation that describes this model is
shown below:
˜
Vuc =˜
Vuc0−˜
Ruc˜
Iuc
˜
Iuc =˜
Cuc d˜
Vuc0
dt
(4)
5) The Modeling of Power demand
In the event that the velocity of the vehicle is known before-
hand, one can compute the necessary power for its propulsion
by applying the subsequent formula. [52]:
Preq = (δm˜
m˜
a+˜
Cad Af
21.15 ˜
v2+˜
mg˜
fr)˜
v(5)
The representation of the equivalent inertia from the revolving
components of the axles and transmission system is indicated
by δm. The weight of the vehicle is ˜
m, The expediting is ˜
a,
and the coefficient of the aerodynamic is ˜
Cad . The transport’s
anterior area is Af, the force of gravity is g, and the coefficient
of traction resistivity is ˜
fr
HEVs utilize a combination of mechanical and electrical
powertrains, which can be used together or separately to
power the driving shaft [53]. The formula for the overall
power delivered to the car is:
˜
Pdemand = (˜
Peng +˜
Pbat ˜ηm)˜ηT(6)
In this context, ˜
Peng stands for the generated power by the
engine, ˜
Pbat represents the potential delivered by the ESS,
˜ηmsignifies the effectiveness of the induction M/G, and ˜ηT
indicates the axle’s and transmission’s capacity. [52].
V. ENERGY MANAGEMENT STRATEGIES FOR HEV
Efficient power distribution in electric vehicles relies heavily
on executing successful approaches for Energy Management.
It plays a central role in both meeting the vehicle’s power
requirements and optimizing its power system. The EMS dic-
tates how each energy source responds to power demands, fa-
cilitating the efficient distribution of power [54]. The key ob-
jectives of energy management strategies encompass ensur-
ing satisfactory performance in terms of acceleration, noise,
range, and handling, as well as meeting power demands,
maximizing fuel efficiency, reducing emissions, and mini-
mizing the overall cost of the propulsion system [55]. These
objectives act as guiding principles while developing energy-
saving maneuvers for hybridized automobiles. Consequently,
an effective EMS is critical for achieving efficient power
distribution and improving the overall functionality of electric
vehicles. This EMS is integrated into the vehicle’s central
controller, which continuously monitors operating conditions
and makes decisions to control components and adjust their
operational parameters accordingly [56]. Different types of
control approaches have been used for electric vehicles. Fig-
ure 6 indicates the trends and advancements in Electric Vehi-
cle energy management strategies over time [57].
Traditional actions to control the power flow in electrified
SUVs are focused on the same basic principle of adjusting
intake information to generate resultant signals [58]. This
uniformity carries both advantages and disadvantages. On the
one hand, it makes these strategies very reliable. On the other
hand, due to this fixed nature, they are less able to adjust
to modifications in the drivetrain specifications of the car.
As a result, traditional strategies are unable to handle the
uncertainties, which leads to inefficient use of power and
poor fuel economy [38]. In the past few years, a selection
of new energy management strategies for PHEVs have been
created that rely on locally known vehicle variables and are
optimized for real-world and real-time driving conditions.
However, most of these strategies have only been evaluated
using standard driving cycles, such as the U.S. EPA’s city
and highway cycles, which are used for fuel economy testing.
Therefore, their effectiveness under real-world driving condi-
tions is not well suited [59].
FIGURE 6. Evolution of scientific literature based on electric vehicle
powertrain control systems
A. THE CLASSIFICATION OF EMS
Several control schemes have been implemented to maximize
HEVs and PHEVs’ functioning. These fall into two cate-
gories, commonly referred to as rule-based and optimization-
based techniques. These two primary groups encompass all
additional subcategories. [56]. An in-depth analysis of several
electric car control schemes, including their contributions
and shortcomings, is given in the following section. Figure
7 symbolizes a cluster of energy-saving measures in hybrid
electric car systems.
1) Rule-based Energy Management Strategy
This strategy is referred to as real-time control tactics that
rely on a preset set of rules derived from individual skill,
intuition, and the properties of the power line. [56]. These
strategies are computationally efficient and easy to implement
but lack mathematical analysis and theoretical basis, making
it difficult to define accurate thresholds and rules. A great deal
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FIGURE 7. Classification of the Energy Management Strategies for HEV
System
of parameter validation and adjusting is needed to increase
performance for specific driving cycles. It does not involve
attenuation or maximization, and their solutions cannot be
guaranteed to be optimal. In order to maximize rule-based
strategies, several techniques have been proposed. These
include hybrid energy management strategies, which combine
ECMS, and blending strategies, such as instantaneous and
rule-based strategies [7]. Using rule-based control strategies,
the power generated sources run at their most efficient phases
to maximize fuel utilization, reliability, and emissions for a
particular driving cycle. However, their ability to determine
the global minimum and optimize the vehicle holistically is
limited. Subcategories of this kind of methodology include
deterministic rule-based and fuzzy rule-based. [38]. Figure 9
showcasing the trajectory of two distinct tactics [60].
Deterministic control strategies operate based on prede-
fined rules and state machines, often depicted in flowcharts
and control parameter tables, along with real-time inputs. So,
prior knowledge of future speed vs. time profiles is optional
[61]. State-machine-based control logic is used, with different
states representing various vehicle operation modes. These
strategies aim to balance the load among the ICE and the
traction electric device to maximize fuel economy, efficiency,
and emissions. The electric propulsion system adjusts the
ICE’s operating points to match the power demand, either
by providing additional propulsion energy or taking in extra
power to replenish the onboard storage [62]. An example
of such a controller is the thermostat controller, which uses
the SoC of the cells and circulatory force demands to turn
the engine on and off. While this approach works for some
hybrid vehicles, it may not be suitable for optimizing modern
electrified transport systems. The most popular is the widely
discussed rule-based strategy known as the "power follower,"
which is employed by automobiles like the Honda Insight
HEV and Toyota Prius. The power follower’s primary goal
is keeping the ESS charged. It is well-suited for parallel
hybrid topologies where the electric motor assists with torque.
However, it is not ideal for PHEV applications as it is not
adaptable to various drive cycles and cannot handle uncer-
tainties resulting from powertrain model errors [63], [64].
The fuzzy EMS approach, an expansion of the conventional
deterministic rule-based strategy, is well-suited for managing
energy in dynamic, nonlinear systems like PHEV drivetrains
due to its advantages in robustness, adaptability, and ease of
fine-tuning. These strategies reduce the complexity of assess-
ing and offer an extra level of abstraction [7]. Nonetheless,
they rely on predefined rules and are primarily optimized
to specific driving scenarios. This method involves several
stages, In the first step, fuzzification is employed to transform
the input data into a precise value or a linguistic variable.
The precise input could be the vehicle’s power demand or
the battery’s SoC. For the fuzzification process, three main
types of fuzzifiers are utilized: Gaussian fuzzifier, Singleton
fuzzifier, Triangular/trapezoidal fuzzifier [65]. To simplify
calculations and ensure a smooth transition, trapezoidal mem-
bership functions (TMFs) are employed for fuzzification in-
stead of Singleton fuzzifier or Gaussian-type functions. This
precise value or fuzzy set is then utilized to develop a rule in
the inference-making block, emulating the human decision-
making process. It is the most crucial part of the fuzzy logic
controller, which controls the effectiveness of a fuzzy rule-
based EMS. It has two components: the membership function
and the fuzzy rule. The formulation of fuzzy logic rules and
their associated membership functions is an iterative process
that draws upon human knowledge, experience, and intuition
that introduce uncertainty into control performance [66].
Figure 8 represents the FLC’s fundamental block diagram.
Various optimization techniques such as proportional fac-
FIGURE 8. Block diagram representation of Fuzzy Logic controller
tors, genetic algorithms, particle swarm optimization, and
bat algorithms are used to enhance performance. To bolster
robustness and adaptability further, the strategy incorporates
adaptive neural fuzzy inference systems, machine learning
algorithms, and the recognition of driving patterns [67]. The
final step, defuzzification or inverse fuzziness, transforms
linguistic variables into numerical values or precise outputs.
The most widely used method in practice is the center of
gravity (COG) approach [68].
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Md Shahin Munsi et al.: Energy Management Systems for Electric Vehicles: A Comprehensive Review of Technologies and Trends
2) Optimization-based Control Strategy
Designers have shifted to optimization-based controllers be-
cause rule-based approaches are too inflexible. These con-
trollers use a cost function to find the best way to control
the PHEV, taking into account the vehicle and component pa-
rameters, as well as the desired performance (emissions, fuel
consumption, and torque) [38]. Different cost functions lead
to different optimization problems, and various techniques
based on optimization have been suggested to address these
issues [69].
Despite extensive research aimed at enhancing the effi-
ciency of optimization-driven EMS, it is difficult to strike
a balance between optimal and implementation [70]. An
accessible, pragmatic optimization-driven power distributed
method is still not available [7].
Optimization-focused control techniques can be categorized
into two primary groups: global and real-time optimization.
FIGURE 9. Illustrating the evolution of two contrasting strategies
Global optimization energy management strategies for
HEVs aim to find the best way to use the vehicle’s energy
sources (e.g., engine, battery, electric motor) to reduce fuel
usage and emissions throughout a specified driving pattern.
They do this by considering the physical constraints of the
vehicle (e.g., engine capacity, battery capacity, electric motor
power) as well as driving conditions (e.g., speed, acceleration,
traffic) [7]. These approaches necessitate an understanding of
the driving cycle in advance [71]. Hence, they are referred to
as non-causal control methods. Unless there’s an accurate pre-
diction of forthcoming driving conditions, these approaches
cannot be directly executed in a practical scenario. Addi-
tionally, global optimization energy management strategies
are more computationally expensive than rule-based energy
management strategies [72]. Despite these challenges, among
the various energy management strategies for HEVs, global
optimization remains the most extensively researched. Figure
10 proves the progress of global optimization-based strategies
throughout time [73]. Common optimization algorithms used
for this purpose include linear programming, dynamic pro-
gramming, and genetic algorithms [74].
Real-time optimization refers to using controllers that can
optimize themselves in real-time, allowing PHEVs to achieve
their maximum potential. These controllers use past infor-
mation to develop a cost function and continuously optimize
it in real-time [38]. Consequently, it is directly applicable
to real-time control systems [75]. This strategy should be
simple enough to be implemented with limited computational
resources and avoid manual control parameter tuning. ECMS
and MPC are two prominent real-time optimization strate-
gies that have gained significant research attention in energy
management applications. ECMS exhibits responsiveness to
the driving cycle, whereas MPC necessitates foreknowledge
of future driving details [76]. Hence, navigational details are
crucial for these approaches. Overall, real-time optimization
aims to stabilize the energy distribution in PHEVs while
maintaining the ESS charge [77].
VI. OVERVIEW OF THE LITERATURE CONCENTRATING ON
ENERGY MANAGEMENT APPLICATIONS
A. RULE-BASED ENERGY MANAGEMENT STRATEGIES
1) Fuzzy rule Strategies
Yu introduced a fuzzy logic and genetic algorithm (GA) based
EMS for plug-in hybrid electric cars. This approach takes
the torque request and Charge levels as input and calculates
the engine torque as output. The GA improved the fuzzy
rules to achieve better gasoline consumption. The simulation
indicated an improvement in the expenditure of fuel by 4.41%
in contrast to the its predecessor fuzzy logic approach. The
charge level in storage devices was more balanced, with a
decrease of 0.062 and 0.035 before and after optimization,
respectively. The torque output of the vehicle is also more
stable after optimization [78]. Traore et al. implemented an
EMS based on fuzzy logic control, Lyapunov control, and
frequency separation for an electric vehicle’s multi-source
system in which Lyapunov controls the total energy flow
to keep the DC-bus voltage steady. The approach combines
FLC with frequency separation to maximize the use of ESS
and ensure effective EMS in the system. Low-pass filters are
also incorporated into the plan to protect the fuel cell and
batteries from strong current dynamics. The results of the
testing demonstrate that the suggested method can maintain a
steady DC-bus voltage of 150V, and regulate the transmission
of electricity [48]. Ishaque and others applied Fuzzy logic
controller and the ultra-power transfer algorithm (UPTA) to
manage the power supply flow where UPTA managed the
energy transmission within the principal and auxiliary energy
storage systems (ESSs). The proposed design was compared
to the proportional-integral (PI) controller regarding perfor-
mance and stability. Numerous metrics, including integral
squared error (ISE), integral absolute error (IAE), and integral
time-weighted absolute error (ITAE) were used to compare
the results. The comparison was carried out at two different
SoCs of the battery: 45% and 95%. At 45% battery SoC,
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the FLC had a better ISE value (0.41) than the PI controller
(0.55). Similarly, the IAE and ITAE values for FLC were also
better than the PI controller. The same trend was observed
at 95% battery SoC. The FLC also had a smaller rise time,
response time, and overshoot than the PI controller, except
for settling time. Overall, the proposed design with FLC had
a better response and stability than the PI controller in the
EMS of HEVs [65]. Dawei and collaborators proposed fuzzy
logic control strategy for the uniaxial PHEV uses a method
based on genetics to maximize the membership functions
and control rules, which improves the strategy’s performance
compared to the electric auxiliary strategy. Intelligent control
approaches optimize the membership functions and regulate
algorithms to guarantee that the high-efficiency zone is where
the majority of the motor functioning areas are located, which
prevents the motor from producing peak torque [79]. Jin et
al. suggested an FLC-based energy distribution approach.
The strategy limits the battery’s power and engages the ultra-
capacitor during acceleration and regenerative braking. The
resultant data showed that the fuzzy logic control strategy
significantly reduces battery degradation by 17%, but the
system encounters significant fluctuations in power levels
when the vehicle speed is high [80]. Hajimiri and Salmasi
introduced a fuzzy logic control strategy that relies on fore-
casting the future state of a vehicle and the health condi-
tion of the batteries to safeguard the battery from severe
harm. Their proposed approach, named the Predictive and
Protective Algorithm (PPA), regulates the battery’s recharge
and discharge in accordance with its SOH, aiming to pro-
long the battery’s lifespan. The simulation outcomes revealed
that the Predictive Algorithm curtailed fuel consumption,
reducing it from 0.202 Lit/mile (PFA) to 0.189 Lit/mile, and
mitigated emissions of CO, HC, and NOx. [81]. Yin et al.
introduced the Adaptive FLC Based Energy Management
Strategy (AFEMS) to offer a holistic control solution tailored
for congested urban and highway driving scenarios. Simula-
tion findings indicate that, on average, AFEMS outperforms
the Limited Tolerance Method (LTM), Thermo-stat Method
(TM), and Average Load Demand (ALD) method in the case
of system efficiency, battery current fluctuation, and the dis-
crepancy in ultracapacitor State of Charge (SoC) [68]. Suhail
et al. presented an ANFIS control for PHEVs. The advanced
controller adjusts the value of the forward gain, resulting
in improved energy management and battery performance
in hybrid electric vehicles. The results carried out on the
simulation showed that the ANFIS controller with Gaussian
membership function was the most energy-efficient, with the
highest SoC value at 84% and a smooth SoC curve compared
to the ECMS, Adaptive-ECMS, and Fuzzy A-ECMS [42].
Kakouche and other co-authors proposed a novel Fuzzy-
MPDTC controller to lower the torque and flux ripples of
the system by predicting future behavior and optimizing the
cost function. The proposed MPDTC method improved the
performance of the overall system by reducing torque and flux
ripples by 54.54% and 77%, respectively [82].
FIGURE 10. Depicting the development of global optimization-based
approaches over time
2) Blended rule based energy management strategies
Bianchi et al. proposed a blended method to extract rules
from dynamic programming to devise a rule-based strategy
that can be put into practice. The proposed approach involves
analyzing the results of DP to identify recurring formations
in its choices and then extracting standards that may be
used to create a sub-optimal rule-based controller. Simulation
results proved that the SoC profiles are similar in shape
to DP programming and have a higher mean value [83].
Chen et al. also used Dynamic Programming optimization
technique to optimize current flow in fuel cell. Additionally,
Genetic Algorithms were applied to lower the instantaneous
cost [84]. Wang and colleagues developed a rule-based power
distribution strategy considering demanded power, residual
energy, and power capability. The remaining capacity and
power capabilities of the batteries and supercapacitors were
calculated using the Bayes Monte Carlo method. Employing
this strategy achieves a 6.81% decrease in hydrogen usage
relative to the strategy that does not involve SOP estimation
and also minimizes the instances of starting and stopping a
fuel cell. Additionally, the DEM strategy lowers the power
fluctuations of the battery and fuel cell systems [85]. Peng
and co-authors introduced an innovative technique to im-
prove rules-driven energy estimation by leveraging optimal
solutions derived from the DP algorithm. The key novelty
of this approach is its development of a fresh method to
fine-tune existing rule-based control strategies using glob-
ally optimized outcomes obtained from sophisticated intelli-
gent algorithms. Experiments conducted in hardware-in-loop
(HIL) demonstrated a prolonged CD mode and an enlarged
engine working spectrum. Moreover, they achieved a 10.45%
decrease in diesel consumption and a 4.75% reduction in bat-
tery charge consumption per 100 kilometers. [86]. Similiarly,
Li and collaborators developed a strategy termed Optimal-
LTCS using the pseudospectral method, which outperformed
the conventional Logic Threshold Control Strategy (LTCS) in
simulation studies. The developed Optimal-LTCS effectively
utilized the positive aspects of the ultra-capacitor, suppressed
battery currents to less than 1C, decreased energy losses,
minimized voltage fluctuations, and perhaps increased the
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driving range [87]. Hofman et al. introduced an EMS which is
the combination of Rule-based and Equivalent Consumption
Minimization Strategies (RB-ECMS) having various types of
driving modes selected from different states and conditions.
When evaluating the Toyota Prius, model 1998, using the RB-
ECMS for fuel economy and control strategy, it was found
that the default strategy in ADVISOR could be considerably
improved (12%). The RB-ECMS results were also very close
to the best possible outcome calculated with DP (within 1%)
[88]. Padmarajan et al. introduced an innovative acausal rule-
based EMS that effectively manages battery energy usage
and engine operation, minimizing energy conversion losses
by integrating driving information and estimated trip energy.
This strategy, based on the blended charge depletion (BCD)
principle, has the potential to be implemented in various plug-
in hybrid architectures. The proposed EMS adapts to uncer-
tain trip requirements, resulting in an 18.4% enhancement in
fuel efficiency and a substantial decrease in engine stop-and-
start instances compared to the typical CS-CD approach [89].
B. OPTIMIZATION-ORIENTED STRATEGIES FOR ENERGY
MANAGEMENT
1) Real time optimization based approaches
Paganelli and his colleagues present the idea of equivalent
gasoline utilization as part of the methodology for the man-
agement of energy (ECMS). This notion sees the battery as a
supplementary fuel tank that receives charge from the gaso-
line engine and discharges it to alleviate the ICE workload,
thereby economizing fuel. ECMS computes the overall fuel
usage by summing the actual ICE energy expenditure and
the induction actuator’s equivalent fuel usage. Through the
use of a unified representation, an instantaneous optimization
issue becomes the replacement for the global optimization
problem, simplifying its solution and reducing computational
complexity [56]. ECMS calculates equivalent fuel consump-
tion dynamically, considering the present system variables,
eliminating the need for future predictions, and reducing the
number of required control parameters. ECMS can compen-
sate for uncertainties in dynamic programming and provide
an immediate optimal resolution for the power distribution
plan, making it suitable for operating in actual time. However,
this strategy doesn’t ensure the long-term sustainability of the
system’s charge. The equivalent factor (EF) significantly im-
pacts the control strategy’s torque distribution and is crucial
for achieving the optimization effect of ECMS [90].
According to the ECMS control strategy’s fundamental
principles, the overall instantaneous equivalent fuel usage can
be expressed in the following manner:
˜
Peqv =˜
Pice +˜
sט
Pbat
˜
Pice =˜
Mice ט
Qlcv
(7)
Herein, ˜
Peqv represents the total instantaneous equivalent
fuel consumption. ˜
Pice denotes the power associated with the
fuel consumed by the engine, ˜
srepresents the equivalence
factor, ˜
Pbat represents the power drawn from the battery, ˜
Mice
represents the engine’s fuel consumption, and ˜
Qlcv represents
the low calorific value of diesel [90].
ECMS approaches can be categorized based on their EF
adaptation strategies. There are two main types of ECMS
methodologies: (1) offline design utilizing global optimiza-
tion algorithms and (2) online adaptation that adjusts EF in
real time.
Offline EF design ECMS, also known as basic ECMS, neces-
sitates prior knowledge of the route to achieve overall effi-
ciency. The best possible efficiency factor remains unchanged
throughout the path because there is no mechanism to adjust
it. Moreover, the EF needs to be adjusted specifically for
every unique driving profile, further complicating the process
[91].
Adaptive ECMSs (A-ECMSs) incorporate online EF adap-
tation capabilities that consider elements like desired and
restricted battery charge levels, present SoC readings, and
current and upcoming driving circumstances. Firstly, ECMSs
must take into account the battery state-of-charge limitations,
such as ensuring charge longevity and adhering to higher
and lower SoC boundaries. Therefore, EF adjustments are
necessary based on SoC-related parameters. To achieve the
desired EF adjustments, a variety of control techniques can be
implemented, including weighting functions, PID controllers,
rule-based techniques, neural network adaptability, and meth-
ods based on linear regression [92].
Sciarretta et al. proposed ECMS strategy that utilized the
concept of fuel equivalent for electrical energy to optimize
energy management. For the MVEG-A, and the ECE urban
driving cycle, the suggested approach demonstrated a reduc-
tion in fuel consumption that could reach 30% and around
50% compared to conventional methods, respectively. This
performance improvement did not affect charge sustainabil-
ity, as SoC variations remain within 2% for the specified
cycles. The robustness of the ECMS was validated under
varying energy horizon scenarios. Additionally, introducing
an extra cost term to minimize frequent engine status changes
resulted in full qualitative agreement [24]. Pisu and Rizzoni
compared three strategies for controlling a parallel- SUV:
rule-based control, A-ECMS, and H-infinity control. They
discovered that A-ECMS proved to be the most effective
approach, closely resembling the optimal solution identified
through dynamic programming. A-ECMS is also more robust
and easier to drive than the other two strategies, but it requires
more computation and a hierarchical control structure. Rule-
based control and H-infinity control are simpler to implement,
but they require more calibration effort and are less efficient
[93]. Tulpule et al. recommended the ECMS energy man-
agement system in PHEV to minimize the usage of fuel by
obtaining the lowest possible battery SoC. Under conditions
of extended travel and substantial energy storage, simulations
demonstrated that the proposed ECMS achieved outcomes
equivalent to those of the DP [94]. Li and his colleagues
developed a strategy for optimizing energy usage in FCHEVs
using an ECMS, including two other sources (battery and
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ultracapacitor), where the three sources were taken into the
objective function. The suggested ways underwent testing
in various driving patterns and the outcomes showed that
ECMS consumed less hydrogen and maintained the fuel cell’s
longest durability compared to both the RBCS and the HEOS
[95]. Chen and collaborators presented a novel P-ECMS
methodology for managing energy distribution in PHEVs,
assuming the availability of two levels of traffic information
(segmented traffic information and detailed velocity infor-
mation). To evaluate the strategy, its effectiveness was as-
sessed in comparison to the Adaptive-ECMS approach. The
comparative results found that the proposed method reduced
fuel consumption (9.7%), provided robust performance, and
minimized the standard deviation (96%) in terms of fuel
usage when contrasted with the A-ECMS measures [96]. Lee
and Cha developed a new strategy that utilizes reinforcement
learning (RL) in conjunction with the ECMS, in which the
comparable variable was ascertained by the RL substances
and the driving situation’s interaction. The proposed method
was compared to the A-ECMS and found that the recom-
mended system could attain a solution close to optimal, reach-
ing 96.7% similarity to the DP outcome, and it enhanced per-
formance by an average of 4.3%citelee2020reinforcement.
Wang and others recommended a Fuzzy Adaptive-ECMS de-
pendent energy management system to adjust the proportional
element. A comparative result validated the effectiveness and
robustness of the proposed controller among other controllers
[97]. Zeng et al. offered an optimization-oriented A-ECMS
scheme. This strategy used a local optimization process to
periodically update the equivalent factor. Simulations reveal
that compared to current alternatives, the recommended EMS
techniques are more reliable and productive. It can save fuel
and extend the battery life [98].
FIGURE 11. The percentage of three real-time optimization-based
journals published since 2019
Model predictive control (MPC) is a sophisticated con-
trol algorithm designed to enhance the efficiency of a regu-
lated method. The primary concept of this controller is to fore-
cast the forthcoming actions of the system based on its current
state and a reference framework of the system to calculate an
optimal regulate signal that will minimize a defined function
of cost [99]. The cost function usually comprises elements
representing the difference between predicted and real system
outputs, constrained by the limits of the control signal [100]
[101]. Three stages comprise the MPC programs’s operation:
(i) utilize the system’s dynamic model to predict future out-
puts within the specified horizon of the fine-tuning; (ii) ana-
lyze the associated charges for each set of predicted system
outcomes; and (iii) implement the initial component of the
control tactic that minimizes the predicted cost. Figure 12
represents the block diagram of Model Predictive controller.
Concerning a specific HEV, the comprehensive mathemat-
ical representation of its behavior is described by the equation
below,
˜
x(m+ 1) = A(m)˜
x(m) + B(m)˜
u(m) + ˜
n(m)
˜
y(m) = C(m)˜
x(m) + D(m)˜
u(m) + ˜
v(m)
(8)
In the above equation, ˜
xrepresents the system’s states at
time m,˜
udenotes the inputs of the controller, ˜
y(m)presents
the outputs of the system at time m,˜
n(m)represents the state
noise that affects the system’s dynamics, and ˜
v(m)represents
the disturbance in evaluations that reduce the accuracy the
observed outputs [99] [102].
FIGURE 12. Block diagram representation of MPC controller
He et al. mentioned an MPC controller to manage the en-
ergy of a PHEV where dynamic programming was employed
to fix an issue of optimization and Markov prediction to
estimate navigation circumstances. They compared the MPC
controller to rule-based and DP controllers and found that the
MPC controller performed better and was close to the DP
controlling scheme [103]. Bordons and the rest used an MPC
to improve energy use in an SUV. The MPC’s effectiveness
in meeting the power demands of the driver was verified
through simulations while minimizing fuel usage and meet-
ing operational constraints [104]. Amin and so on designed
an approach to limit the rate of change of current (current
slope) in fuel cells and batteries and to stabilize the potential
difference in a DC bus at a desired value. They showed that
the MPC controller was able to achieve both of these goals
effectively [105]. Zhang and so forth applied the model-
oriented predictive controller (MPC) with a receding time
horizon to determine the battery pack’s output power. The
advanced controller forecasts the torque requirement for the
upcoming ten seconds and employs DP-based programming
to determine the optimal battery output current [106]. Xiang
et al. identified a new methodology to manage the energy
that used a combination of nonlinear model predictive control
(NMPC) and proportional-integral-derivative (PID) control.
This methodology was created to enhance fuel-efficiency,
10 VOLUME 11, 2023
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preserve the state of charge of the battery, and accommo-
date driving needs. The outcomes demonstrated that the sug-
gested EMS surpasses alternative approaches. [107]. Wang
and others suggested a new way to improve the powertrain
efficiency of a tracked bulldozer with a hybrid electric power
system using an MPC controller. They compared their MPC
controller to two other methods and found that the MPC
controller’s performance surpassed that of the rule-based con-
troller in achieving the desired outcomes and was almost
similiar to the DP controller [108]. Guo and collaborators
planned a new Hybrid Electric Vehicle energy management
algorithm that combines the Gauss pseudospectral method
and model predictive control. the new algorithm is more
accurate and computationally efficient than the Euler method
[109]. Golchoubian and Azad proposed a nonlinear model
predictive controller (NMPC) for fast-changing systems like
electric vehicles. The NMPC controller outperformed without
foreknowledge of the forthcoming trip [110]. Pereira and the
rest proposed a nonlinear MPC and an RNN control strategy,
which is applied to model the fuel cell accurately. The system
was tested on a low-cost development board and showed that
it can meet the vehicle’s energy needs while operating the
fuel cell at its most efficient point. The system also reduces
fuel cell degradation and provides better fuel economy than
other control systems [111]. Machacek et al.introduces a
new online-based MPC controller. The proposed controller
demonstrates the capability to recapture 70% of the opti-
mality loss of a state-of-the-art predictive controller, and
its performance is nearly indistinguishable from that of dy-
namic programming optimization [112]. Chen and colleagues
employed a reinforcement learning-driven stochastic Model
Predictive Controller to establish the ideal storage power
within the anticipated timeframe [113]. Jia et al. considered
A-MPC controller to maximize the load current between the
Energy storage devices in real-time. The AMPC-centered
EMS underwent assessments via hardware-in-the-loop tests,
revealing its superior performance in hydrogen consumption,
stability in FC current fluctuation, and its minimal optimality
gap when juxtaposed with an offline dynamic programming-
based optimal EMS [114]. Sun and associates introduced
a DL algorithm-driven MPC strategy aimed at enhancing
fuel efficiency and resilience. This framework underwent
assessment across three distinct driving cycles, showcasing
resemblances with the DP-based program [115].
2) Global Optimization based Energy Management
Strategies
Dynamic programming is a mathematical approach to solv-
ing optimization problems that involve multiple decision-
making steps over time [116]. These problems, known as
multi-stage decision problems, can be broken down into a
series of interconnected stages, where each stage requires a
decision that influences both the immediate outcome and the
initial state for the next stage. Dynamic programming aims to
identify a sequence of decisions that minimizes the total cost
across all stages. The technique was pioneered in the 1950s
by Richard Bellman, who originally introduced the concept
of the optimality principle [117].
Two methods for implementing Bellman’s dynamic pro-
gramming approach are the forward dynamic programming
approach and the backward recursive method. Working from
the problem’s final state backward to its beginning state is
known as the backward recursive technique. This technique
is typically applied when the problem can be segmented
into a group of interdependent subproblems, where the so-
lution to each sub-problem depends solely on the solutions
to the sub-problems that come later in the sequence [118].
Dynamic programming involves working forward from the
initial formulation of the problem to its ultimate resolution.
This approach is often employed when the methodology can
be divided into a series of sub-issues, where the solution to
each sub-problem depends solely on the solutions to the sub-
problems that come earlier in the sequence [23].
Dynamic programming provides the benefit of being appli-
cable to a wide range of systems, including both non-linear
and linear systems, problems with constraints and without
constraints [56]. However, it also encounters two limitations:
the requirement to have complete information about the entire
driving cycle beforehand, the challenge of dealing with a
large number of variables, and also a significant workload
caused by computation. Hence, the control solutions obtained
from dynamic programming are primarily utilized as ref-
erence points for assessing other controllers or as building
blocks for creating and enhancing alternative optimization-
based approaches [23].
The following is the generalized cost function of a HEV for
dynamic Programming strategy:
Jcost =
l=N−1
X
l=0
[˜
G(˜
x(n),˜
u(n))] + ˜
G(˜
x(N))
=
N−1
X
l=0
[gasoline(n) + α.Nox(n) + β.PM (n)emi]
+γ(SoC(N)ini −SoCfinal )2
(9)
To alleviate the computational burden and enhance the
tractability of the DP scheme, the vehicle model was simpli-
fied by considering only three state variables: speed of the
vehicle, no. of gear used for transmission, and battery’ SoC.
where the state vector at time step nis represented by ˜
x(n),
control vector is expressed by ˜
u(n), comprising the target
torque output provided by the engine/motor and instructions
for gear shifting in the distribution. In the context where N sig-
nifies the length of the transmission route, and ˜
Gdenotes the
immediate cost function encompassing gasoline utilization as
well as engine-out NOx and PM emissions, SoCfinal stands
for the targeted state of charge after the specified duration.
Additionally, α,β, and γare constructive weighting factors
[119].
Chen and his fellows considered driving pattern recognition-
based dynamic programming to maximize fuel efficiency
and battery protection for range-extended electric vehicles.
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It was observed that the proposed strategy outperforms con-
ventional thermostat control strategies concerning both ESS
protection and fuel savings [120]. Larsson et al. used an
approximation of the cost-to-go to find the optimal torque
split decision at each point in time and state. This approach
did not require quantizing the torque split or interpolating
in the cost-to-go. The results suggested that this strategy
significantly decreased calculation time and memory storage
requirements [121]. Liu and his fellows developed a com-
putationally efficient DP-dependent EMS that can operate
in real-time without GPS data. The proposed EMS utilizes
a hybrid trip model and a SoC search range optimization
algorithm to achieve its objectives [122]. Lee and cowork-
ers evaluated the fuel efficiency of an RL-based dynamic
programming strategy against other control algorithms. The
comparison revealed that the proposed strategy achieved
superior global optimality [123]. Liu et al. submitted an
online strategy for the purpose of energy utilization based
on heuristic dynamic programming to alleviate the energy
absorption of a P-HEV while accounting for the vehicle’s
nonlinear dynamic behavior. Experimental outcomes proved
the superiority of the suggested method in precision and speed
tracking accuracy, achieving over 98% accuracy. However,
it consumed around 4% more fuel than the offline global
optimization energy management technique [124]. Peng and
his mates arranged a reconfiguration procedure to fortify the
effectiveness of a rule-based system by integrating findings
from the DP heuristic. The HIL simulation findings revealed
that the adjusted system decreased diesel consumption per
100 kilometers from 25.46 liters to 22.80 liters [86]. Ro-
maus and other collaborators explored the application of
SDP to boost the management of power flow in energy
storage systems during live operation. They compared the
performance of SDP-based control to the optimal strategy
determined during system dimensioning [125]. Li and his
colleagues proposed an approximate dynamic programming
(ADP) oriented plan to design a fuel-optimal control system
for vehicles, eliminating the need for prior knowledge of
future driving conditions. The control strategy relies solely
on real-time system information and optimizes fuel consump-
tion, emissions, and battery charge balance. The ADP-based
approach demonstrated superior performance compared to
traditional rule-based control strategies [126]. Zhang and
Xiong utilized Dynamic Programming to develop optimal
control strategies for various driving scenarios, facilitating
the implementation of adaptive EMS for real-world driving
paths [127]. Chen and other co-authors developed an EMS
concentrating on DP controller to increase energy savings.
Two NN modules were developed on optimal findings from
DP approaches incorporating the length of the trip and time
frame. Depending on these two factors, the controller selects
the appropriate module from NN to produce efficient battery
current instructions for the distribution of energy [116].
Linear programming (LP) is a straightforward and efficient
optimization technique that employs first-order polynomial
and linear equality or inequality constraints to model and
solve issues with minimal computational expense. It is widely
used in series HEVs to optimize fuel efficiency. Using LP to
formulate the fuel efficiency optimization issue can result in
achieving the best possible solution globally [56]. While LP
and its variants can solve some fundamental problems, their
simple structure cannot handle complex nonlinear systems
with nonlinear objective functions such as deviation variance.
As a result, they appear to be unsuitable for solving PEV
charging optimization problems, and alternative program-
ming techniques must be employed [128]. The general linear
programming problem is formulated as follows:
min
kf
X
j=k0
(˜
f(k)) (10)
depending on vehicle dynamics constraints such as
˜
f(k)≥mi.Pengine(k) + ni{i= 1, ..., N}(11)
where, the instantaneous fuel consumption is denoted by
˜
f(k), and the engine/generator set power output is denoted
by Pengine(k)[119].
Umetani his fellows designed a novel approach to schedul-
ing the charging and discharging of electric cars using a
time-space network model and an LP-based heuristic algo-
rithm. This algorithm enables effective scheduling within a
limited computation time. To address the uncertainty in EV
demand and departure times, an improved two-stage heuristic
algorithm was also developed. Computational experiments
demonstrated that the two-stage heuristic algorithm effec-
tively reduces peak load while handling uncertain EV demand
and departure times within a limited computation time [129].
Ghandriz and others developed a new method for prophetic
EMS using a sequential linear program (SLP). The pro-
posed SLP was faster and simpler than traditional sequential
quadratic programming (SQP) methods and provided near-
optimal trajectories. The proposed method’s performance was
tested and compared to SQP approaches [130]. Fanti and
collaborators proposed an LP approach to enhance day-ahead
energy purchasing and real-time energy consumption. The
LP formulation aims to maximize the utilization of day-
ahead purchased energy while minimizing real-time addi-
tional costs. This approach was implemented in a Demand-
Side Energy Management System (DEMS) to achieve the
desired optimization goals [131]. Wu and others implied
ROEMS to manage FC-HEVs under unpredictable driving
conditions effectively. The ROEMS utilizes an offline linear
programming-based method to establish a benchmark solu-
tion [132]. Pirouzi et al. successfully applied Mixed-Integer
Linear Programming (MILP) to three distribution networks
of varying sizes: 33-bus, 69-bus, and 133-bus. Their pro-
posed model demonstrated superior performance, achieving
the lowest energy cost and energy loss among the alternatives.
Additionally, it maintained an optimal voltage profile within
a reasonable calculation time. This highlights the effective-
ness of MILP in optimizing distribution network operations
12 VOLUME 11, 2023
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3371483
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
Md Shahin Munsi et al.: Energy Management Systems for Electric Vehicles: A Comprehensive Review of Technologies and Trends
[133]. Venkitaraman and Kosuru employed linear program-
ming principles and Bayesian theory to optimize the electric
vehicle (EV) charging distribution network. Applying these
mathematical approaches, the numerical results demonstrated
that the proposed strategy effectively minimizes the impact
on the power grid while ensuring a safe and cost-efficient EV
charging infrastructure [134].
Stochastic control strategies are employed to model and
optimize problems involving uncertainty, mainly when in-
put data is probabilistic rather than deterministic. This tech-
nique defines the power requirement from the driver as an
arbitrary Markov chain and implements it to an infinite-
horizon stochastic dynamic optimization problem [135]. This
model forecasts forthcoming power requirements by em-
ploying probability distributions, eliminating the need for
past decision knowledge. Following that, stochastic dynamic
programming (SDP) is applied to identify the most suitable
approach, resulting in a stationary full-state feedback control
law for direct implementation. This method optimizes the
control policy across various driving patterns rather than a
single deterministic cycle, enhancing adaptability and robust-
ness in uncertain environments [136]. The advantage of SDP
in optimizing power split maps lies in its ability to com-
prehensively and robustly address optimization challenges.
Unlike traditional methods that optimize for a single de-
terministic drive cycle, SDP considers a probabilistic dis-
tribution of drive cycles, accounting for real-world driving
variability and uncertainty. By modeling driver demand as
a discrete-time stochastic dynamic process, SDP generates
probability distributions for future power demands. This en-
ables the optimization algorithm to adapt and respond to
different driving patterns. SDP’s application in power split
optimization provides a more realistic and practical solution,
improving hybrid vehicle performance and efficiency [38]
[137]. Figure 13 represents the steps for optimization using
the Stochastic Control Strategy. Zeng and Wang introduced a
FIGURE 13. Stochastic Optimization Algorithm
stochastic dynamic programming (SDP) algorithm for offline
optimization of the energy management strategy, followed
by its real-time implementation through a lookup table. The
findings indicated that after 24 hours of rides on the defined
track, the suggested strategy consumes just 1.8% additional
energy compared to the optimal outcome, significantly out-
performing alternative casual energy management strategies
[138]. Vagg et al. used the same approach (SDP) to implement
and test the controller in the real world. They also addressed
practical considerations for the robust implementation of
the SDP algorithm. This method led to a 13% decrease in
strain on the electrical powertrain during dynamometer test-
ing, maintaining fuel savings without compromising perfor-
mance [139]. Payri and colleagues enhanced the established
ECMS technique by incorporating a stochastic assessment of
upcoming driving conditions. This involved estimating the
future probability distribution of power demands using past
power requirements. They determined the parameter neces-
sary to keep the anticipated ESS’charge level at a specified
value after a given time horizon. Simulations demonstrate
that this approach facilitates sustainable charging and yields
results close to optimality [140]. Marefat and his co-authors
implemented Stochastic Dynamic Programming to predict
power demand using Markov chain assumptions and actual
driving information. The SDP approach constructed a Tran-
sition Probability Matrix (TPM) from training cycles and
simulates power demand based on test drive cycles. SDP
demonstrated superior performance and substantial computa-
tional cost savings compared to existing methods [141]. Liu
and his associates suggested a cluster-based SDP to enhance
energy recovery through regenerative braking. The strategy
involved employing the K-means algorithm to divide driving
conditions into clusters and constructing static Markov chains
for each cluster to model the probabilities of future braking
torque demand changes. Real-time identification of driving
conditions was accomplished using a support vector machine
(SVM). Both the hardware and software experiments were
conducted, and the results revealed that the SDP-based strat-
egy outperforms no-downshifting and rule-based approaches
regarding energy recovery during regenerative braking [142].
Chen and the rest implemented an S-MPC approach that
relies on reinforcement learning (RL) to optimize the gasoline
sustainability of PHEVs. Integrating an RL controller into the
stochastic MPC framework determines the efficient battery
power for the defined time frame. A number of simulations
confirm the efficacy of this method, showcasing fuel econ-
omy [113]. Yang et al. applied a novel stochastic predictive-
EMS, employing fast rolling optimization to enhance ef-
ficiency. Simulations and real-world testing were used to
evaluate the proposed approach, comparing its performance
against SMPC optimized using DP and ECMS. The proposed
controller outperformed ECMS in terms of computational
speed and energy consumption [135]. Ripaccioli introduced
a stochastic method to address power distribution challenges
in series hybrid electric vehicles (HEVs). They modeled the
driver’s power requirements as the model of the Markov
chain, constructed using data from diverse route statistics,
VOLUME 11, 2023 13
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3371483
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Md Shahin Munsi et al.: Energy Management Systems for Electric Vehicles: A Comprehensive Review of Technologies and Trends
and utilized it to construct a set of scenarios within the
SMPC framework. The practicality of the proposed stochas-
tic technique was successfully shown by simulation results,
even without precise knowledge of future power requirements
[143].
VII. CONCLUSION
The increasing popularity of HEVs, driven by the promise
of better fuel efficiency and vehicle performance, has at-
tracted significant research interest from both academic and
industry experts. Various power management strategies have
been developed to address the energy requirements of dif-
ferent HEV configurations. This paper comprehensively re-
views all control techniques employed to achieve the best
power allocation between main and secondary energy gener-
ations in HEVs/PHEVs. This in-depth analysis aims to shed
light on the reviewed techniques’ control structure, novelty,
and contributions. EMS are typically categorized based on
their mathematical approach. While easy to implement, rule-
based controllers can lead to suboptimal performance; power
consumption optimization should encompass the entire trip.
Achieving global optimality requires a priori trip informa-
tion. While optimization-based techniques do not directly
allow for real-time energy management, an instantaneous
cost function-based approach could allow it. Strategies should
prioritize minimal computational time, global optimality, and
compatibility with dynamic simulation environments.
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VOLUME 11, 2023 15
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content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3371483
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MD SHAHIN MUNSI received his BSc in Mecha-
tronics Engineering from Rajshahi University of
Engineering & Technology (RUET). Since then,
he has been engaged in roles within academic and
industrial environments, concentrating on control
and energy systems. From 2019 until 2023, he
worked as an engineer and lecturer. He is pursuing
a Ph.D. in Electrical and Computer Engineering
at Texas Tech University in Lubbock, Texas. His
research focuses on electric vehicles, motor drives,
and energy conversion and storage systems.
HICHAM CHAOUI (SeniorMember, IEEE) re-
ceived the Ph.D. degree in electrical engineer-
ing (with honors) from the University of Quebec,
Trois-Rivières, QC, Canada, in 2011. His career
has spanned both academia and industry in the
field of control and energy systems. From 2007
to 2014, he held various engineering and man-
agement positions in the Canadian industry. He
is currently an Associate Professor at Texas Tech
University, TX, USA, and also at Carleton Uni-
versity, Ottawa, ON, Canada. His scholarly work has resulted in over 175
journal and conference publications. Dr. Chaoui is a registered professional
Engineer in the province of Ontario. He is also an Associate Editor of
IEEE TRANSACTIONS ON POWER ELECTRONICS, IEEE TRANSAC-
TIONS ON VEHICULAR TECHNOLOGY, IEEE TRANSACTIONS ON
AUTOMATION SCIENCE AND ENGINEERING, and several other jour-
nals. He is a recipient of the Best Thesis Award and the Governor General
of Canada Gold Medal Award. He is also a recipient of the FED Research
Excellence Award, the Early Researcher Award from the Ministry of Colleges
and Universities, and the Top Editor Recognition from the IEEE Vehicular
Technology Society.
VOLUME 11, 2023 17
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3371483
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
