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Abstract

The transportation sector is seeing an increased adoption rate of electric vehicles (EV), which comprise battery electric vehicles (BEV), hybrid electric vehicles (HEV) and plug-in hybrid electric vehicles (PHEV). This mode of transportation is likely to displace automobiles powered by internal combustion engines (ICE) in the nearest future, as indicated by the current trend. Each of the key EC components is home to a variety of technologies that are either currently in use or will be in the near future. Electric vehicles have the potential to have a significant impact on a variety of fields, including the economy, the energy infrastructure, and the natural environment. As a result of the production of carbon dioxide gas from the combustion of fossil fuels, transportation is the sector that is responsible for the second-highest amount of greenhouse gas emissions. Electric vehicles, often known as EVs, are getting a lot of attention as a potentially game-changing solution to this issue. It is possible for electric vehicles to produce fewer emissions of carbon dioxide (CO2) than conventional automobiles due to the fact that an electric motor serves as the vehicle's propeller rather than an internal combustion engine. EVs have the potential to become zero-emission vehicles if they are paired with renewable energy sources. This document presents an overview of the numerous types of EV drive circuits, covering their design as well as the benefits and drawbacks associated with each type. The current state of battery technology, particularly that pertaining to the batteries utilized in electronic vehicles, is discussed in this paper. This article also discusses electric motor efficiency, power density, fault tolerance, dependability, cost, and the best electric motor for EVs. Then, a thorough study of future EV implementation's obstacles and prospects is done. Charging times and battery performance are examples of technological challenges, but government regulation of EVs continues to be a substantial non-technical hurdle.
Review Not peer-reviewed version
A Comprehensive Review for
Electric Vehicles Drive Circuits
Technology, Operations and
Challenges
Mlungisi Ntombela * , Kabeya Musasa , Katleho Moloi
Posted Date: 29 June 2023
doi: 10.20944/preprints202306.2040.v1
Keywords: Electric Vehicles; Electric Motors; Batteries; Internal Combustion Engine; Motor Speed
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Review
A Comprehensive Review for Electric Vehicles Drive
Circuits Technology, Operations and Challenges
Mlungisi Ntombela *, Kabeya Musasa and Katleho Moloi
Department of Electrical Power Engineering, Faculty of Engineering and the Built Environment, Durban
University of Technology, Durban 4000, South Africa; MusasaK@dut.ac.za (K.M.); katlehom@dut.ac.za (M.K.)
*Correspondence: 21210920@dut4life.ac.za
Abstract: The transportation sector is seeing an increased adoption rate of electric vehicles (EV), which
comprise battery electric vehicles (BEV), hybrid electric vehicles (HEV) and plug-in hybrid electric vehicles
(PHEV). This mode of transportation is likely to displace automobiles powered by internal combustion engines
(ICE) in the nearest future, as indicated by the current trend. Each of the key EC components is home to a
variety of technologies that are either currently in use or will be in the near future. Electric vehicles have the
potential to have a significant impact on a variety of fields, including the economy, the energy infrastructure,
and the natural environment. As a result of the production of carbon dioxide gas from the combustion of fossil
fuels, transportation is the sector that is responsible for the second-highest amount of greenhouse gas
emissions. Electric vehicles, often known as EVs, are getting a lot of attention as a potentially game-changing
solution to this issue. It is possible for electric vehicles to produce fewer emissions of carbon dioxide (CO2) than
conventional automobiles due to the fact that an electric motor serves as the vehicle's propeller rather than an
internal combustion engine. EVs have the potential to become zero-emission vehicles if they are paired with
renewable energy sources. This document presents an overview of the numerous types of EV drive circuits,
covering their design as well as the benefits and drawbacks associated with each type. The current state of
battery technology, particularly that pertaining to the batteries utilized in electronic vehicles, is discussed in
this paper. This article also discusses electric motor efficiency, power density, fault tolerance, dependability,
cost, and the best electric motor for EVs. Then, a thorough study of future EV implementation's obstacles and
prospects is done. Charging times and battery performance are examples of technological challenges, but
government regulation of EVs continues to be a substantial non-technical hurdle.
Keywords: Electric Vehicles; Electric Motors; Batteries; Internal Combustion Engine; Motor Speed
1. Introduction
The transportation industry is seeing a rise in interest in EVs, which includes battery electric
vehicles (BEV), hybrid electric vehicles (HEV), plug-in hybrid electric cars (PHEV), and fuel cell
electric cars (FCEV). The current trend suggests that this mode of transportation will eventually
displace automobiles powered by internal combustion engines (ICE), and this could happen rather
soon. Each of the essential components of EC makes use of a range of technologies, and in the not-
too-distant future, they will continue to do so [1,2]. EVs might significantly impact the environment
and other businesses like the electrical system. Transportation emits the second-most greenhouse
gases after agriculture because fossil fuels release carbon dioxide. Many people believe that electric
vehicles, sometimes known as EVs, are an excellent solution to this problem [3]. Electric vehicles have
the potential to produce fewer emissions of carbon dioxide due to the fact that, rather than having an
internal combustion engine, an electric motor serves as the vehicle's propeller. EVs when combined
with alternative forms of energy, have the potential to become emission-free automobiles. This article
offers a summary of the numerous varieties of electric vehicle drive circuits, including the design of
each type as well as the benefits and drawbacks associated with each [4]. In addition, information
regarding the effectiveness, power density, fault tolerance, dependability, and cost of electric motors,
as well as the electric motor that is the most effective when used to EV s, is provided. An in-depth
discussion on future EV deployment obstacles and rewards follows. Charging time and battery
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© 2023 by the author(s). Distributed under a Creative Commons CC BY license.
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performance are technological hurdles, but government regulation remains a major non-technical
obstacle for EVs [5–7].
As a result of the widespread use of EVs, a number of studies have looked into the many types
of EVs. Researchers Braun et al. examined the efficiency of electric vehicles by contrasting a battery
electric vehicle (BEV) with an internal combustion engine (ICE) passenger vehicle and driving them
under a variety of scenarios [8,9]. At Erfurt Germany researchers analyzed how different driving
styles, as well as rush hour traffic, affect energy use [10]. Based on the findings, it was determined
that the BEV is 69.2 percent more fuel efficient than conventional autos. This significant advantage is
the result of the components of the BEV's powertrain that are only activated when traction is
necessary to be provided. They convert the kinetic energy created while braking into electrical
energy, which is then used to charge the batteries regenerative braking [11,12]. As a result of these
characteristics, the BEV is able to capitalize on the varying speeds of the vehicle.
After the development of the electric motor itself came the idea of employing electric motors to
power the propulsion of vehicles. Throughout the years 1897 to 1900, EVs were more popular than
cars powered by internal combustion engines (ICEs) and made up 28 percent of all automobiles [13].
After that, however, internal combustion engine (ICE) types gained speed, and with exceptionally
cheap oil prices, they soon won the market, quickly becoming much more mature and advanced,
while EVs sank into oblivion. A glimmer of hope was provided by the EV1 prototype, which was
initially released by General Motors in 1996 and almost instantly became incredibly popular [14].
Electric vehicles have also been produced by a number of other major automakers, including Ford,
Toyota, and Honda, amongst others. The Toyota Prius was the world's first mass-produced hybrid
electric vehicle (HEV), and it made its debut in the Japanese market in 1997 [15–18]. In its first year
of production, 18,000 of these cars were sold. Nearly none of these EVs are manufactured or sold
today, with the notable exception of the Toyota Prius, which is still in production albeit in an
improved form. At the moment, the Nissan Leaf, the Chevrolet Volt, and the Tesla Model S are the
most successful vehicles on the market.
This research investigates the most current developments in electric vehicle technology, focusing
on such topics as the vehicles' capabilities, the energy sources they draw from, and the prospects for
EVs in the years to come. Because the technologies that are associated with electric vehicles and their
energy system (production, storage, and use) are always developing, this study investigates the most
recent electric vehicle technologies, including those that are connected to autonomous driving and
battery storage [19]:
The first part of this article covers electric vehicle batteries and motors. Moreover, information
regarding electric vehicle kinds, battery capacities, and motor drives can be found in this area.
A complete analysis of battery technology from lead-acid to LIB is also provided [20]. This
section discusses battery technologies, especially electric car batteries. The most prevalent
electric motors in EVs and vehicles are presented. EV owners can use this information to select
a motor that best suits their needs in terms of energy economy, power density, speed,
dependability, size, and cost.
The second section, which investigates the different configurations of electric vehicles, offers a
summary of the numerous categories of electric vehicles, including BEVs, HEVs, and PHEVs.
They incorporate the technology as well as the framework of electric automobiles [21].
The third section makes projections about the future of transportation and discusses the
challenges that will be faced by electric vehicles. These challenges include the need for
improvements in battery performance, charging times, law and regulation, and an open market
for power. By doing so, it is anticipated that updated EV technology will be made available.
These challenges are necessary for obtaining a new point of view on EVs and the growing
movement towards the future [22].
2. Batteries and Electric Motors
2.1. Battery Engineering
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The battery is the primary source of energy for electric vehicles, other sources of energy include
the energy produced by regenerative braking, the energy produced by fuels, and the energy
produced by various power storages such as a super capacitor [23,24]. The battery features a versatile
architecture that allows it to be assembled in either series, parallel, or series-parallel configurations,
depending on the required amount of voltage and current. In addition, the battery incorporates the
three standard forms of electric vehicle cells, which are cylindrical, pouch, and prismatic cells. While
shopping for battery-powered equipment, be sure to give equal consideration to the product's
expected lifespan, power density, energy density, capacity, and state-of-charge (SOC). The most
potent power sources for EVs are rechargeable batteries like lithium-ion [25]. The lithium-ion battery
(LIB) was invented in 1970, the lead-acid battery in 1858, and the nickel-iron alkaline battery in 1908.
Compared to the other two batteries, the LIB had a higher specific energy and energy density.
Rechargeable batteries were developed as a result.
Lead-acid batteries have a specific gravimetric energy density of 30–50 Wh/kg, making them the
least efficient. The lifespan of a lead-acid battery is 500–1000 cycles [26,27]. To go two hundred
kilometers, a lead-acid battery that weighs at least five hundred kilo-grams is needed to generate one
kilo-watt-hour (kWh) of electricity. Lead-acid batteries are inexpensive (varying from $300 to $600
per kilowatt-hour) and recyclable, which is one of the most significant aspects of any battery
technology. Low-performance, tiny cars can use lead-acid batteries. Since their invention, lead-acid
batteries have been recycled. As usual. This battery's recycling rate is close to 100% in Western
countries and elsewhere [28]. Lead-acid batteries use 85% of the world's lead, and 60% of it is
recycled. Lead-acid batteries are easily damaged; thus their components can fall out of their plastic
containers with their acid. Ni-MH batteries outperform lead-acid batteries. This battery has a
gravimetric energy density between 40 and 110 Wh/kg, far higher than lead-acid batteries. In the
early 1990s, Ni-MH batteries were widely used in EVs (Prius) due to their environmental friendliness.
The main drawbacks of this battery technology are its poor cold performance and memory effects.
Another issue is the battery's long recharge time and high self-discharge rate when idle. The battery's
poor charge and discharge efficiency is the biggest issue [29–31].
Ni-Cd batteries need high charge and discharge rates and are memory-prone. The substance is
toxic and possesses 60–80 Wh/kg specific energy density. Recharging nickel-hydrogen (Ni-H)
batteries was studied by Chen and colleagues. It was difficult to develop a low-cost grid storage
material with a longer battery cycle and calendar lifespan. Material needs more cycles. This paper
proposed a 10,000-cycle manga-nese-hydrogen battery for grid energy storage. Mn2+/MnO2 redox
cathodes and H+/H2 gas anodes comprise the battery [32,33]. The battery's areal capacity loading was
projected to improve tenfold to 35 mAh/cm2 by replacing the Mn2+/MnO2 redox with a nickel-based
cathode. In place of an expensive platinum catalyst, a less expensive nickel-molybdenum-cobalt alloy
was used to catalyze the evolution of hydrogen into oxygen in alkaline electrolytes for the anode. The
Ni-H battery is recommended since it has a gravimetric energy density of 140 Wh/kg and can be
recharged more than 1500 times. Both specifications are included in the following Table 1 [34,].
The sodium-nickel chloride (Na-NiCL2) batteries, also known as the Zero Emissions Batteries
Research Activity (ZEBRA) batteries, are regarded as safe and inexpensive. Additionally, they are
able to have nearly all of their capacity depleted without having a negative impact on the amount of
time they will last. In addition, the energy that is contained within the battery. a value that is around
150 Wh/kg. Because a ZEBRA battery may operate at temperatures ranging from 245 to 350 degrees
Celsius, the thermal management and safety challenges associated with this battery are under a
significant amount of strain [35]. As a storage source, ZEBRA batteries are a good example. Due to
the cell's chemical reactions' intrinsic safety, multiple tests, including immersion in 900 liters of
saltwater with a 5% salt content, seismic and vibratory testing, and a 30-minute external fire expo-
sure test that did not harm the modules or cells, showed that fire risk is low. So, it's suitable for
stationary energy storage. This technique is good for load leveling, voltage management, time
shifting, and renewable energy power swing reduction due to its three-hour rate discharge length
[36].
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The latest battery technology is lithium. Its energy, light weight, low cost, non-toxicity, and rapid
charging make them the most promising batteries. These batteries have a gravimetric energy density
ranging from 118 to 250 Wh/kg; however, their specific energy capacity is now being improved so
that it can be increased even further [37]. Anode electrodes in lithium-ion batteries are typically made
of silicon nanoparticles (SiNPs) due to the high energy density of this material. Lithium batteries have
the lowest equivalent mass and maximum electrochemical potential. It's also efficient and durable.
However, it costs over 700 USD per kWh and can cause fires and property damage if overheated [38].
Mass transport constraints in the electrolyte and electrodes will cause severe polarization in lithium
batteries with improved performance. Polarization is affected differently by each activity due to the
dynamic and kinetic properties of the material, as well as the design of the battery and the mechanism
for charging and discharging it. To reduce solid phase diffusion polarization, Chen and colleagues
reduced the active material's particles. If half of the active material particles were present, LIB
concentration may be significantly reduced [39]. When the active material particles were twice as
large, the Li-ion concentration difference was much greater.
Several lithium-ion batteries (LIBs) have been made worldwide. LTO, LCO, LMO, NMC, and
LFP are some of them (LFP). LIBs employ a different electrolyte than lithium-polymer batteries (Li-
Po). The LIB, in contrast to the LB, possesses a higher energy density, a cheaper cost, and does not
have a memory effect. LIBs are cheaper and memory-free [40–42]. In contrast, the Li-Po battery
features a structure that is both flexible and adaptable, as well as a low profile and a reduced chance
of electrolyte leakage. Because doing so improves the efficiency of packaging, it is typically cut into
multiple different sizes. On the other hand, Li-Po batteries have a lower energy density, a shorter
lifespan, and a manufacture cost that is significantly higher than average. The characteristics of
electric vehicle batteries that are now in use are outlined in Table 1, which may be found here. Figure
6 also illustrates a correlation between the batteries' specific power and specific energy levels.
Table 1. A comparison of the energy storage capacities of the various batteries found in EVs.
SPECIFICATION LEAD-ACID
BATTERY
NI-MH
BATTERY
NA-NICL2 BATTERY LIBS
Nominal voltage (V) 2.00 1.20 2.40 3.60
Energy efficiency (%) >80 70 80 >95
Volumetric energy
density (Wh/L)
100 180–220 160 200–
400
Gravimetric energy
density (Wh/kg)
30–50 40–110 150 118–
250
Lifecycle 500–1000 <3000 >1200 2000
Cost (USD/kWh) 100.00 853–1700 482–1000 700.00
References [53–55] [56,57] [58–61] [62–67]
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Figure 1. Graphs showing the power output versus the energy output of a given battery storage
device [68].
2.2. Electric Motor Engineering
Due to the fact that it is a necessary component, electric motors are impossible to produce
without the electric motor. To convert electrical energy from its work form into its mechanical form
and vice versa, what is needed is something called an electric motor. The transmission or differential
may receive high power and torque from an electric motor, which may subsequently be put to use
for the vehicle's propulsion [69]. Because the electric motor in EVs may be able to provide
instantaneous power and torque in comparison to the internal combustion engine (ICE), the
transmission may not be necessary in EVs [70]. In addition, electric motors have an energy conversion
efficiency that is significantly higher than that of internal combustion engines (between 80% and 95%
efficient), making them the more desired of the two options. Propulsion in EVs can come from a wide
variety of different types of electric motors as follows;
IM=Induction motor
PM-SM=Permanent magnet synchronous motor
PM-BLDC=Permanent magnet-brushless DC motor; and
SRM=Switching reluctance motor
Because of the high levels of efficiency and power density that they provide, IM and PM-SM
motors are regarded as the most appealing possibilities for usage in EVs. This is due to the fact that
they are the most common types of motors used in EVs. Electric motors are assessed and compared
with regard to their installation space, power density, machine weight, dependability, efficiency,
torque-speed relationship, overload capability, and cost before they are used in EVs [71,72].
IM is well-known for its effectiveness, starting torque, power, simplicity, inexpensiveness,
roughness, and little amount of required maintenance. IMs can operate in any hazardous
environment without speed limits. The IM's complex control system struggles with power density
[73]. Iron, copper, commutation, and stray losses in the magnetic circuit, windings, converter, and
mechanical components affect this motor's energy efficiency. IM motor losses were examined. In
order to determine the effectiveness of an IM motor, they utilized a finite element research to map
out the losses. According to the findings of the study, the motor's efficiency map was decided by each
loss map. To improve the performance of the IM motor, one researcher advises reducing the spins of
the stators by one-half 0.75,2.25, and 3.7 kW IM motors were employed [74,75]. So, the new motor
control is more efficient than the previous one, which led to an increase in motor performance. The
0.75 kW motor changed from having a power output of 78% to 85.39%, the 2.25 kW motor went from
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having an output of 83.23% to having an output of 86.22%, and the 3.7 kW motor went from having
an output of 86.25% to having an output of 87.62% [76,76,77].
By utilizing PM-SM, users are able to achieve consistent torque while also achieving high
efficiency, high power density, and low energy consumption. By enhancing motor efficiency by 10%,
PM-SM ensures reliable performance and electrical balance. PM-SM mechanical packages are smaller
than those of previous variants [78]. Since it has no coils or brushes, the PM-SM rotor doesn't generate
much heat. PM-highly SM's conductive materials and high-permeability permanent magnets make it
ideal for electric and hybrid electric cars. Nevertheless, because it has a permanent magnet, this
engine is more expensive to buy initially, and PM material supplies are few and expensive. Moreover,
energy loss during PM-to-SM conversion has yet to be solved. Double Fourier integral analysis can
quantify fundamental and harmonic losses to construct a unique global loss model of PM-SM,
according to a study. To improve electric car performance, this research sought to reduce total energy
loss (including fundamental iron loss, fundamental copper loss, harmonic iron loss, and harmonic
copper loss) (EVs). 94% efficiency lost the least energy, according to this study [79].
Another form of motor, known as PM-BLDC, is one that is started by rectangular AC and
features significant pulsing in its torque output. This motor might be able to deliver the highest torque
in the constant-torque area because it keeps the flux angle between the stator and the rotor relatively
close to 90 degrees [80]. Maintaining constant power can be accomplished through careful
manipulation of the phase-advance angle. High power density, efficiency, and heat dissipation
characterize the PM-BLDC motor. This motor's traits are these. The PM-BLDC motor's initial cost is
considerable due to the magnet in the rotor, and the device's field-weakening capability is limited by
the permanent magnetic field. This method was applied to the two motors that show the most
promise for usage in hybrid electric vehicles (HEVs) by means of a sophisticated software application
that simulates vehicles (IM and PM-BLDC) [81]. The fuel usage of each motor was 11.8 liters per 100
kilometers; the PMBLDC used 11.7 liters, and the IM used 11.9 liters. In addition, PM-BLDC had
fewer overall pollutant emissions than IM did, which came up at 2.68 g/km compared to 2.72 g/km
for IM. According to the findings, the PM-BLDC motor is more suitable for application in hybrid EVs
than the IM motor is.
The SRM is the newest motor type that can be found in EVs. This arrangement is simpler than
the others. It has a rotor (moving part) and a stator (non-moving part), with windings exclusively on
the stator. SRM motors are more cost-effective than PM motors since they do not have a permanent
magnet [82]. In addition to this, SRM is fault-tolerant, which means that if there is a problem with
one phase, it will not influence the functioning of the other phases. SRM is still regarded as a
physically robust choice for electric vehicles and hybrid electric vehicles (HEVs) due to its low cost
and sturdy design, despite the fact that it needs to overcome concerns such as acoustic noise, torque
ripple, converter topology challenges, and electromagnetic interference (EMI). In [83] a study
investigated the functionality of SRM 10/8 (SRM 5 phases) drives for EVs when subjected to abnormal
conditions such as open- and short-circuit failures. The SRM is designed to be fault resistant and
possesses outstanding dynamic reactivity. When evaluating the performance of SRM-powered EVs,
speed, torque, and state of charge were taken into consideration. Under normal circumstances, SRM
was able to accomplish the reference speed in 1.23 seconds [84]. While the SOC dropped by 0.04% at
1.26 seconds into a 1-phase short circuit scenario, the torque remained the same at 485.3 Nm
throughout the whole event. The benefits and drawbacks of the electric motor are summarized in
Table 2, and the efficiency maps of the SRM motor, the IM motor, and the PM-SM motor are shown
in Figure 7.
Table 2. The electric motors utilized in EVs each have their own set of advantages and
disadvantages.
PARAMETERS IM PM-SM PM-BLDC SRM
Size +++ +++ +++ +++
Torque ripple + + + +++++
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Efficiency +++ ++++ +++++ ++++
Power density +++ ++++ ++++ ++
Acoustic noise + + + ++
Reliability ++++ +++ +++ ++++
Fault-tolerant ++ ++ +++ ++++
Simple
construction
++ ++ +++ +++++
Cost +++ +++++ +++++ ++++
Technological
maturity
+++++ ++++ ++++ ++++
Opportunity Automotive
market
penetration
EVs and HEVs'
preferred option
EV drivers' first
choice.
Attracting
scientists
and
industry.
Challenge A novel
technology
control for
minimizing
fault
tolerance
and slide.
Precision torque
ripple position
feedback
External
transmission devices
like chain drives and
fixed gears are
needed.
Non-linear
control
current
switching
angle
identification
References [85–
87]
[88–90]] [91,92]] [93–
97]]
Figure 2 shows that every electric motor has a unique best efficiency area for both the driving
and braking cycles. A study analyzed the different types of electric vehicle (EV) motors and drives in
terms of their effectiveness, maximum speed, relative cost, and level of dependability (IM, PM-BLDC,
PM-SM, SRM). The PM-BLDC motor was the most efficient type of motor, while the SRM motor had
the highest possible speed Figure 3 [100]. Nonetheless, the brushless DC motor and the induction
motor were the types of motors that were used the most frequently, and the induction motor was the
type of motor that was the type of motor that was the type of motor that was the most cost-effective.
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Figure 2. The effectiveness of electric motors and its components [100].
Figure 3. Comparative analysis of the speeds of several motors [101].
2. Configurations of EVs
EVs have the ability to run solely on electric propulsion or in conjunction with an internal
combustion engine (ICE). The simplest sort of electric vehicle (EV) relies just on batteries as its source
of energy; however, there are many variants that make use of a variety of other types of energy
sources. These automobiles are hybrid electric models (HEVs) [102–104]. The Technical Committee
69 Electric Road Vehicles (ERV) of the International Electrical Technical Commission proposed that
cars with two or more forms of energy source, storage, or converters can be classified as HEVs as
long as at least one of them provides electrical energy [105,106]. This recommendation was made in
response to a question posed by the Technical Committee 69 ERV of the International Electrical
Technical Commission. This specification makes it possible to combine ICE and batteries, batteries
and flywheels, batteries and capacitors, batteries and etc. in a number of hybrid electric vehicle
configurations. As a result, regular people and industry professionals started referring to hybrid
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electric cars (HEVs), ultra-capacitor-assisted electric vehicles (FCEVs), and fuel cell electric vehicles
(FCEVs) to describe automobiles that have both an internal combustion engine and an electric motor.
These terminologies have garnered a significant amount of support, and on the basis of this standard,
EVs can be categorized as follows [107–109]:
Electric Battery Vehicle (BEV)
Hybrid Electric Vehicle (HEV)
Plug-in Electric Hybrid Vehicle (PHEV)
2.1. Batteries Electric Vehicles (BEVs)
Given that a battery is the only source of energy for the powertrain of a BEV Figure 1, the range
that may be achieved by such a vehicle is directly proportional to the capacity of the battery. A BEV
is completely carbon dioxide (CO2) emission free because it does not have a tailpipe or other source
of exhaust emissions. BEVs have the potential to go between 100 and 250 kilometers on a single
charge, while using 15 to 20 kWh for every 100 kilometers driven [110–113]. This range is determined
by the characteristics of the vehicle. There is a range of between 300 and 500 kilometers for battery
electric vehicle models that have larger battery packs. However, battery electric vehicles (BEVs), in
comparison to other types of electric vehicles (EVs), have a substantial disadvantage due to their
significantly reduced driving range and dramatically increased charging periods. The most effective
way to address this issue would be to design and implement an EMS that is suitable for BEVs
[114,115].
One study devised a three-wheel electric vehicle regenerative braking method that extended
range to around 20 km/kWh compared to complete mechanical braking (19.2 km/kWh), serial
regenerative braking (19.3 km/kWh), and parallel regenerative braking (19.5 km/kWh). Compared to
three previous braking techniques, this one increased range to 20 km/kWh. This innovative braking
technique could increase range by 4.16 km/kWh compared to mechanical braking alone. One
technique to expand the range of battery electric vehicles (BEVs) is to increase the battery pack's
capacity [116–119]. However, it is possible that a battery pack with a large capacity is not useful
because it requires a significant amount of space and significantly increases the weight of the vehicle.
This has a direct impact on the vehicle's performance as well as its fuel economy, and it also raises
the total cost of the vehicle [120]. An electric three-wheel vehicle that is fully loaded (300 kg) and has
a lithium-ion battery pack (LIB) that is 16 kWh has a range that is approximately 12.5% less than it
would have with a half-load (150 kg) (from 200 to 175 km).
Figure 4. The structure of BEV circuit.
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Examining different driving styles is another method that can be used to extend the range of a
battery-electric vehicle (BEV) without having to increase the capacity of the battery. Controlling the
flow of energy and power is one way that one might put this method into action when driving.
Runtime power management was developed by in [121,122] order to extend the range of battery
electric vehicles. An algorithm was proposed to cut down on journey time and the amount of gasoline
used [123,124]. The fact that this technique is based on a multi-objective algorithm enables it to
produce results that are superior to those produced by other algorithms that have been examined. In
[125] a study suggested a velocity profile optimization-based optimal control method to reduce
energy consumption. The proposed algorithm was able to cut energy consumption by between 8 and
10%, thanks to its management of driving duration and speed. These citations provide a workable
answer to the problem of lowering battery capacity while maintaining a lower overall energy
consumption.
2.2. Hybrid Electric Cars (HEVs)
The International Electro-Technical Commission's Technical Committee 69 (Electric Road
Vehicles) defines a hybrid electric vehicle (HEV) as a vehicle with two or more energy sources,
storage, or converters, at least one of which generates electricity. HEVs use two or more energy
sources, storage, or converters [126]. Because BEVs have a limited driving range, hybrid electric
vehicles (HEVs), which combine a traditional internal combustion engine (ICE) with a battery system,
have become an appealing option. An electric motor is the only source of propulsion for a series
hybrid electric vehicle, as shown in Figure 2a. In contrast, both an internal combustion engine (ICE)
and an electric motor are connected to the gearbox of a parallel hybrid electric vehicle (HEV), which
transmits power to the wheels simultaneously (see Figure 2b). Many studies have been conducted to
determine the amount of fuel that parallel and series hybrid electric vehicles consume as well as how
efficiently they use their fuel (HEVs). In [127] for instance, compared the amount of gasoline that was
consumed by series and parallel HEV road sweeper trucks while keeping the same amount of power
and traveling the same amount of distance.
Based on the findings of the comparison, the series hybrid design (3.8 L/h) had a lower fuel
consumption rate than the parallel hybrid design (6.2 L/h). When the vehicle was operating in the
series hybrid mode, the internal combustion engine (ICE) kept its speed constant throughout the
transport mode. On the other hand, when the vehicle was operating in the parallel hybrid mode, the
engine speed fluctuated. By altering the hybridization factor, Li demonstrated in a separate study
that parallel HEV topologies are more energy-efficient than series HEV designs (HF) [128,129]. Due
to the fact that there are three different conversions that take (place mechanical, electric, and
mechanical), parallel HEVs are theoretically considered to have smaller power conversion losses than
series HEVs do. When the power splitting mode is engaged, it is possible to cut losses in the drive
train, the engine, and the braking system. This could lead to a gain in fuel economy that ranges from
0.3 to 36.7% [130]. In addition to this, the fuel efficiency of parallel HEVs can be up to 68 percent
better than that of a traditional automobile. This substantial improvement in fuel efficiency was made
possible, in part, by the implementation of regenerative braking, which refers to the recuperation of
energy that would have otherwise been lost. As a consequence of these studies, series hybrid electric
vehicles (HEVs) have been successfully deployed in transportation mode. On the other hand, parallel
hybrid electric vehicles (HEVs) require further changes to the drive train in order to achieve improved
energy efficiency [131].
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(a)
(b)
Figure 5. (a) The structure of series HEV circuit and (b) The structure of parallel HEV circuit.
Mild hybrid electric cars, also known as MHEVs, are another form of hybrid electric vehicle
(HEV) that are equipped with an electric motor and a battery that has a capacity that is on the lower
end of the spectrum (10–20 kW). Although the hardware components of this form of EV and other
types of HEVs are identical, the control algorithms used by each of these categories of vehicles are
very different. Because the internal combustion engine is responsible for the majority of the
production of the vehicle's propulsion energy, a gasoline-powered hybrid electric vehicle (MHEV) is
distinguished from other types of HEVs by having a lower hybridization power approximately 15%
and smaller driving electric components. This is due to the fact that the internal combustion engine
is responsible for the majority of the production of the vehicle's propulsion energy [132]. When it
comes to energy management, the most difficult obstacle for HEVs to overcome is likely going to be
the combination of many energy sources and optimization. In order to determine a pattern of a
driving cycle's energy consumption, a comprehensive modeling system, data from test runs, and
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simulator software that has been approved for commercial use are required. In addition, the data
from the test runs are necessary in order to obtain the energy consumption.
2.3. Plug-In Electric Hybrid Cars (PHEVs)
The range of HEVs may be increased, which led to the development of PHEVs. Like HEVs, plug-
in hybrid electric vehicles (PHEVs) have an internal combustion engine (ICE), an electric motor, a
generator, and a battery [133,134]. Regenerative braking can be replaced with utility grid charging.
PHEVs are BEV/HEV hybrids. Figure 3a,b show different plug-in hybrid electric automobiles
(PHEVs). Hybrid electric vehicles use "series" or "parallel" ICEs to charge the battery or provide
traction (HEVs).
(a)
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(b)
Figure 6. (a) The structure of series PHEV circuit (b) The structure of parallel PHEV circuit.
Unlike HEVs, plug-in hybrid electric vehicles (PHEVs) may get their battery charges from the
grid, which results in larger battery packs. HEVs are required to operate in charge sustenance mode
(CS), which caps the amount of charge the battery can hold (SOC). Plug-in hybrid electric vehicles
(PHEVs) have the capability of operating in either pure electric or blended mode when in charge
depletion (CD) mode (prioritize using the electric motor over ICE) [135,136]. A study for charge
depletion mode to reduce parallel PHEV fuel consumption. The urban dynamometer driving
schedule (UDDS) reduced parallel PHEV fuel consumption by 7.1% over 64 km, 6.3% over 48 km,
and 5.6% over 32 km [137]]. This study found that the PHEV's CD control technique effectiveness
increased proportionally with the test distance.
In the same way as with BEVs, when the battery capacity of PHEVs increases, the primary issue
shifts to the charging time; as a result, charging strategies are required to maintain the vehicle's
performance. A fast charger can give a higher DC current capacity for car charging. Rapid DC
charging methods like CHAdeMO (charge de move) and Combo may charge up to 80% of the
capacity in 30 minutes, depending on power delivery rate (6–200 kW) [138]. CHAdeMO and Combo
also have promising vehicle-to-grid (V2G) technology futures. Both standards support quick
charging. In [139] a study developed, implemented, and tested the V2G system. A vehicle with a fully
functional CHAdeMO inter-face (VCI) at the physical and protocol levels was able to control
communication and electrical transfer between the car and charger. The VCI was fully implemented
at both the physical and protocol standards. Plug-in hybrid electric vehicles and battery electric
vehicles could shape the future of transportation by storing energy from the grid in their batteries
and feeding it back into the distribution network when needed [139].
Power losses, stability systems, and resilience are some of the additional difficulties that are
linked with PHEVsThe SCS Algorithm, a smart-charging scheduling algorithm, may alleviate these
problems, especially robustness. It was possible to achieve optimal charging timing for plug-in
hybrid electric vehicles (PHEVs) by synchronizing a number of plug-in hybrid electric vehicles
(PHEVs) inside a smart grid system. The findings revealed that it had an adequate level of robustness
and provided values with a standard deviation that was less than 1 (= 0.8425) [140]. Figure 4 illustrates
the configuration of the powertrain used in series-parallel hybrid electric vehicles and plug-in hybrid
electric vehicles. HEVs and PHEVs that run in a series-parallel mode are able to take use of all of the
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benefits that are associated with running in either the series or the parallel mode. These benefits
include increased fuel economy, increased range, and increased efficiency. A study on the efficiency
of fuel usage in series-parallel plug-in hybrid electric vehicles was carried out by Zhao and Burke
[141].
According to the findings of their investigation, the rate of fuel consumption for a series-parallel
PHEV utilizing the UDDS (city driving) strategy was 18.1 kilometers per liter less than that of a series
shaft PHEV of the same kind, which was 20.4 kilometers per liter. This information was derived from
comparing the two types of PHEVs using the same driving strategy. The HW-Interstate method
(highway driving up to 77 miles per hour; 120,7 kilometers per hour) demonstrated the same
conclusion, with a series-parallel PHEV achieving a lower fuel consumption efficiency. An additional
study that used the approach of blended power-split mode to investigate the energy efficiency of
series-parallel plug-in hybrid electric vehicles (PHEVs) found a considerable improvement [142]. As
a result of energy allocation and power management in a drive system, it provided a real-world
example of the control method for series-parallel plug-in hybrid electric vehicle (PHEV) power
management. This was possible since it was based on a drive system. The result brought the overall
system's efficiency up by 27.50 percentage points, from 19.3 to 24.6 km/L. Nonetheless, this type of
vehicle is heavier, has a less sophisticated look, and carries a higher price tag [143].
(a)
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(b)
Figure 7. Series-parallel hybrid electric vehicle circuit structure: (a) series-parallel HEV (b) series-
parallel PHEV.
Another type of plug-in hybrid electric car is an extended-range electric vehicle (EREV). In
contrast to other types of PHEVs, the electric motor always powers the wheels, and the internal
combustion engine doubles as a generator to keep the vehicle's battery charged whenever it runs low
or when the vehicle is in motion [40]. The EREV has strong preferences for reducing the use of mineral
resources and fossil fuels to the absolute minimum possible [144]. According to the findings of Liu et
al, the consumption of mineral resources by EREV is 14.68 percentage points lower than that of HEV,
and the consumption of fossil fuels by EREV is 34.72 percentage points lower than that of ICEV. It's
possible that the decreased consumption of mineral resources is due to the vehicle's smaller size and
the fact that there are fewer components overall [145]. It is possible to achieve minimal fuel
consumption since the gasoline is only required to run the generator. This generator has a constant
rotational speed and torque for charging the batteries, so it does not require much fuel. The fact that
the generator is the solitary component that is used in the process of providing electricity to the
vehicle makes this outcome conceivable. The generator's speed and torque can both be adjusted to
achieve the highest possible levels of energy efficiency in order to cut down on the amount of money
spent on fuel. Because of the range ex-tender, EREVs are able to travel further than BEVs;
nevertheless, in order to compete with BEVs in terms of energy efficiency, they need to be much more
compact [146].
3. Upcoming Opportunities and Challenges of EVs
EVs are considered the vehicles of the future for several reasons, including their expanding
market share, their environmental friendliness, and their cutting-edge technology. The value of EV-
related stocks rose from $3.7 million in 2019 to $13 million in 2020, and the International Energy
Agency (IEA) predicts it will reach $130 million by 2030. Also, during the period under consideration,
it is estimated that sales of EVs will rise by an average of 24 percent. The number of EVs sold climbed
from 1.4 million in 2019 to 4 million in 2020, and it is anticipated that the number would reach 21.5
million by the year 2030 [147,148]. Notwithstanding the obvious benefits that EVs offer over
traditional automobiles, this means of transportation must nevertheless overcome the five key
challenges listed below:
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The automobile as a type of emerging technology
Charging times and technology for connecting vehicles to power grids;
An increase in the efficiency of batteries;
Public policy and regulatory frameworks; and
A free and open market for power.
3.1. The Automobile as a Type of Emerging Technology
The EV which is widely regarded as the vehicle of the future, presents a wealth of opportunities
for the development of cutting-edge technologies that can enhance both its performance and its
ability to communicate with both other EVs and the surrounding environment. The internet-of-cars
paradigm is a feature that ought to be included in EVs. This capability allows integrated systems
between all internal components, roadside unit connections, and vehicle-to-vehicle connections. By
the year 2020, Mansour et al. hoped to have created an intelligent self-parking vehicle Autonomous
Parallel Car Parking [149]. This prototype was able to identify an available parking spot and parallel
park itself, but it required a large number of sensors, including infrared and ultrasonic sensors, in
order to gather information about its surroundings. This may reduce accidents, human error safety,
mobility for elderly, inexperienced, and disabled drivers, and driving time. In 2020 an intelligent car
with a smart car control system was introduced. The remote wireless control terminal in this vehicle
was a smartphone. The intelligent vehicle acts as a bridge between Bluetooth and a microcontroller
(MCU) [150]. It may provide automatic direction control, gravity induction control, speech control,
an automatic tracking system, automatic collision avoidance, and other support features. This inquiry
cut the pricing of equipment with wired and remote controls. In [151] study created a new analytical
approach for analyzing the safety of crosswalks in 2021. This methodology is based on the multiple
behaviors of vehicles and pedestrians, as well as the components that are in the surrounding area. In
the case of an accident in the future, the safety of pedestrians and cyclists may be improved thanks
to this model.
Companies producing automobiles such as Volvo, BMW, and Nissan are some examples of
businesses that have improved the performance of their vehicles by applying technology that is
exclusive to "smart cars”. A Volvo's sensors can detect lanes and vehicles behind it, and the pilot
assist feature lets the system know when the adaptive controller is being used. A Volvo's sensors can
also identify vehicles in front of it. Machine-controlled driving is standard on new BMW I-Series cars.
Wi-Fi connectivity, high-definition digital maps, sensor technology, cloud technology, and
processing facilities are among these capabilities [152]. Last but not least, all Nissan cars come
standard with shield-shaped safety equipment that can scan a 360-degree area around the vehicle for
potential risks, warn the driver, and take any necessary precautions. If accidents must be avoided,
this technology can help the driver [153].
3.2. Charging Times and Technology for Connecting Vehicles to Power Grids
The wall-box, which functioned as an additional semi-fast charger, was an essential component
in the system for managing the chargers. It came pre-configured with features such as energy control
with real-time observing, charge planning, remote control (lock/unlock and output current), settings
for individual tariffs, energy consumption and cost statistics data, and connectivity with the
operation of iOS and Android system devices [154–156]. These features allowed wall-box to play a
significant role in the management of chargers. It might make charging with a maximum capacity of
22 kW possible and assist consumers in controlling the amount of energy their EVs use. Researchers
have developed a method of rapid recharging for battery-equipped EVs in order to alleviate the strain
that the increased demand for EV charging is having on the power grid and to circumvent the
exorbitant costs associated with charging during peak hours. The findings helped station builders
and operators understand industry economics, requirements, operation, future demands, and
maintenance approaches. While constructing charging stations, three more elements, including the
dimensions of the station and its equipment, maintenance procedures, and operation strategies,
should be taken into consideration [157].
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3.3. An increase in the Efficiency of Batteries
With ultra-quick charging, EVs can charge up to 80% of their batteries in 15 minutes. A study
evaluated whether public buses will have ultra-fast charging infrastructure with 450 kW in in the
nearest future. Because of this, they were able to determine the capacity of the battery [158]. In this
particular situation, a single terminal equipped with a 450-kW charging station traveled 100 different
routes that were between 10 and 20 kilometers in length. In this scenario, the bus traveled 17.22
kilometers and consumed 413.28 kWh of energy. If the terminal featured an ultra-fast charging station
of 450 kW, the bus would only require one charge with a total capacity of 190.63 kWh. According to
the findings of our research, the battery capacity might be reduced from 413.28 kWh to 222.64 kWh
[159,160]]. This reduction in travel time can be accomplished through the utilization of an ultra-fast
charging station at the terminal.
Pulsed current is being considered to increase LIB battery performance. Huang et al. pulsed a
LIB cell at 0.05 Hz to activate it. By changing duty cycles, C-rates, and ambient temperatures, the
pulsed current's influence on battery charging may be determined. Pulsed current, when compared
to steady current, increased charging capacity by 30.63 percent while simultaneously reducing the
maximum temperature rise by 60.37 percent [161]. There is also the possibility of preheating the
battery at a low temperature in order to improve its performance and reduce the risk of accidents
involving the battery. An external pre-heating method was developed by Biao and colleagues. The
battery's electro thermal plate and a temperature field distribution are used in this procedure. The
battery's inside and outside case were different temperatures due to this method. The battery's
temperature was modest despite the case's high temperature [162].
3.4. Public Policy and Regulatory Frameworks
EVs have emerged as a viable option in recent years and continue to garner significant interest.
So, the diffusion of EVs is significantly dependent on governmental action, which is often justified
primarily for the purpose of stimulating technological innovation features aimed at reducing
negative externalities (such as emissions reduction). In the meanwhile, the rate of adoption of EVs is
largely dictated by the infrastructure support for EVs as well as the legislation surrounding EVs. The
Asia-Pacific Economic Cooperation (APEC) examined electric vehicle (EV) policy, focusing on the
charging network, boosting demand for these vehicles, industrialization, research, and development
initiatives, and the incorporation of EVs into sustainable mobility plans [163]. This paper describes
APEC public policies to overcome barriers to electric vehicle adoption to improve policy instruments
for new energy cars. The goal of this effort is to increase the efficacy of policy instruments for new
energy cars. In a separate piece of research, Researchers investigated and assessed two other forms
of policy: the priority placed on purchase subsidies, and the expansion of charging infrastructure.
These rules will have an effect on the rate of EV uptake and use in the future. So, the expansion of the
market for EVs necessitates the creation of new charging infrastructure, technological advancements,
and government restrictions [164]. Customers' fears would be alleviated further if charging stations
had adequate coverage and capabilities, and appropriate government laws may encourage the
expansion of the electric EV sector.
3.5. A free and Open Market for Power
The need for electricity to charge EVs is expected to rise in tandem with the growing popularity
of EVs. In open electricity markets, where prices fluctuate daily, the power system of electric vehicle
charging stations raises energy prices. When compared to the price of electricity during off-peak
hours, the price of electricity during peak hours is typically three times higher. In order to solve this
problem, it will be necessary to install charging stations for EVs that make use of a hybrid energy
system. This type of system includes both conventional and renewable energy sources, such as
photovoltaic panels and energy markets [165]. To prevent environmental contamination, renewable
energy sources must be used, but they are scarce. To improve system performance, they are often
employed alongside traditional components. The use of such a system presents a challenge due to
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the fact that achieving its optimal operation calls for a significant amount of research, which, in turn,
brings down the price of charging an EV. EVs can have their charging and discharging processes
made more efficient thanks to a market mechanism that has been devised in [166]. based on the block
chain technology. A multi-mode optimization method was proposed by. This charging point is a
hybrid of a traditional charging station and a photovoltaic system. Both components work together
to provide power. The effectiveness of the strategy was demonstrated by the fact that initial costs
were cut by more than fifty percent as a direct consequence of its implementation.
4. Concluding Remarks
EVs have the potential to not only become the future of transportation but also to save the globe
from the oncoming calamities related to global warming. Conventional automobiles, which are
wholly reliant on the ever-decreasing reserves of fossil fuels, have little chance of competing with
these vehicles, which offer a workable alternative. In this article, the several types of EVs, their
configurations, energy sources, motors, power conversion, and charging systems are dissected in
great detail. The fundamental technologies of each segment as well as the characteristics of those
technologies have been analyzed and outlined. The implications that EVs have on a variety of
businesses have also been investigated, as have the tremendous prospects that EVs bring for
promoting a cleaner and more efficient energy system by collaborating with smart grids and making
it easier to include renewable sources. The shortcomings of currently available EVs have been
outlined, as have some of the potential solutions for addressing those shortcomings. In addition to
that, the many optimization approaches and control methods that are now in use have been
presented. This study provides a condensed overview of the current electric vehicle market.
Following an analysis of current tendencies and potential avenues for future growth is a discussion
of the article's conclusions, which serve to summarize the preceding material, paint an accurate
picture of the sector in question, and highlight the research gaps that still need to be filled.
Author Contributions:.
Funding:.
Institutional Review Board Statement:.
Informed Consent Statement:.
Data Availability Statement:.
Conicts of Interest:.
References
1. May, G.; Davidson, A.; Monahov, B. Lead batteries for utility energy storage: A review. J. Energy Storage
2021, 15, 145–157.
2. Liang, Y.; Zhao, C.; Yuan, H.; Chen, Y.; Zhang, W.; Huang, J.; Yu, D.; Liu, Y.; Titirici, M.; Chueh, Y.; et al. A
review of rechargeable batteries for portable electronic devices. InfoMat 2019, 1, 6–32.
3. Koehler, U. General Overview of Non-Lithium Battery Systems and Their Safety Issues; Elsevier B.V.:
Amsterdam, The Netherlands, 2020; ISBN 9780444637772.
4. Qazi, S. Fundamentals of Standalone Photovoltaic Systems; Elsevier: Amsterdam, The Netherlands, 2019; ISBN
9780128030226.
5. Chen, W.; Jin, Y.; Zhao, J.; Liu, N.; Cui, Y. Nickel-hydrogen batteries for large-scale energy storage. Proc.
Natl. Acad. Sci. USA 2020, 115, 11694–11699.
6. Li, G.; Lu, X.; Kim, J.Y.; Meinhardt, K.D.; Chang, H.J.; Canfield, N.L.; Sprenkle, V.L. Advanced intermediate
temperature sodium-nickel chloride batteries with ultra-high energy density. Nat. Commun. 2019, 7, 1–6.
7. Benato, R.; Cosciani, N.; Crugnola, G.; Sessa, S.D.; Lodi, G.; Parmeggiani, C.; Todeschini, M. Sodium nickel
chloride battery technology for large-scale stationary storage in the high voltage network. J. Power Sources
2020, 293, 127–136
8. Juangsa, F.; Budiman, B.; Sambegoro, P.; Darmanto, P.; Nozaki, T. Synthesis of Nanostructured Silicon
Nanoparticles for Anodes of Li-Ion Battery. In Proceedings of the International Conference on Electric
Vehicular Technology (ICEVT), Bali, Indonesia, 18–21 November 2020.
Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 29 June 2023 doi:10.20944/preprints202306.2040.v1
19
9. Schuster, S.; Brand, J.; Berg, P.; Gleissenberger, M.; Jossen, A. Lithium-ion cell to cell variation during
battery electric vehicle operation. J. Power Sources 2019, 297, 242–251.
10. Amiribavandpour, P.; Shen, W.; Kapoor, A. An improved theoretical electrochemical thermal modeling of
lithium-ion battery packs in electric vehicles. J. Power Sources 2020, 284, 328–338.
11. Rahardian, S.; Budiman, B.; Sambegoro, P.; Nurprasetio, I. Review of Solid-State Battery Technology
Progress. In Proceedings of the International Conference on Electric Vehicular Technology (ICEVT), Bali,
Indonesia, 18–21 November 2019.
12. Tie, S.; Tan, C. A review of energy sources and energy management system in electric vehicles 2013, 20.
Renew. Sustain. Energy Rev. 2020, 20, 82–102.
13. Rahman, M.; Wang, X.; Wen, C. A review of high energy density lithium-air battery technology. J. Appl.
Electrochem. 2014, 44, 5–22.
14. Chang, C. Factors Affecting Capacity Design of Lithium-Ion. MDPI, Basel, Switz. 2019. Batteries 2019, 5, 58.
15. Liu, Y.; Gene Liao, Y.; Lai, M.C. Lithium-ion polymer battery for 12-voltage applications: Experiment,
modelling, and validation. Energies 2020, 13, 638.
16. Mongird, K.; Viswanathan, V.; Balducci, P.; Alam, J.; Fotedar, V.; Koritarov, V.; Hadjerioua, B. Energy
Storage Technology and Cost Characterization Report|Department of Energy; Pacific Northwest National Lab.
(PNNL): Richland, WA, USA, 2019.
17. Review of Battery Techonologies for Automotive Applications; ACEA: Brussels, Belgium, 2014.
18. Sharma, A.; Sharma, S. Review of power electronics in vehicle-to-grid systems. J. Energy Storage 2019, 21,
337–361
19. Zang, X.; Shen, C.; Sanghadasa, M.; Lin, L. High-Voltage Supercapacitors Based on Aqueous Electrolytes.
ChemElectroChem 2019, 6, 976–988.
20. Afif, A.; Rahman, S.M.H.; Tasfiah Azad, A.; Zaini, J.; Islan, M.A.; Azad, A.K. Advanced materials and
technologies for hybrid supercapacitors for energy storage—A review. J. Energy Storage 2019, 25, 100852.
21. Kate, R.S.; Khalate, S.A.; Deokate, R.J. Overview of nanostructured metal oxides and pure nickel oxide
(NiO) electrodes for supercapacitors: A review. J. Alloy. Compd. 2020, 734, 89–111.
22. Zhang, Q.; Deng, W.; Zhang, S.; Wu, J. A Rule Based Energy Management System of Experimental
Battery/Supercapacitor Hybrid Energy Storage System for Electric Vehicles. J. Control Sci. Eng. 2018, 2018,
6828269.
23. Lü, X.; Qu, Y.; Wang, Y.; Qin, C.; Liu, G. A comprehensive review on hybrid power system for PEMFC-
HEV: Issues and strategies. Energy Convers. Manag. 2020, 171, 1273–1291.
24. Li, T.; Liu, H.; Zhao, D.; Wang, L. Design and analysis of a fuel cell supercapacitor hybrid construction
vehicle. Int. J. Hydrog. Energy 2018, 41, 12307–12319.
25. Balali, Y.; Stegen, S. Review of energy storage systems for vehicles based on technology, environmental
impacts, and costs. Renew. Sustain. Energy Rev. 2021, 135, 110185.
26. Cheng, M.; Sun, L.; Buja, G.; Song, L. Advanced electrical machines and machine-based systems for electric
and hybrid vehicles. Energies 2022, 8, 9541–9564.
27. Eldho Aliasand, A.; Josh, F.T. Selection of Motor foran Electric Vehicle: A Review. Mater. Today Proc. 2020,
24, 1804–1815.
28. López, I.; Ibarra, E.; Matallana, A.; Andreu, J.; Kortabarria, I. Next generation electric drives for HEV/EV
propulsion systems: Technology, trends and challenges. Renew. Sustain. Energy Rev. 2019, 114, 109336.
29. Finken, T.; Felden, M.; Hameyer, K. Comparison and design of different electrical machine types regarding
their applicability in hybrid electrical vehicles. In Proceedings of the 2021 18th International Conference on
Electrical Machines, Vilamoura, Portugal, 6–9 September 2021; pp. 1–5.
30. Bharadwaj, N.V.; Chandrasekhar, P.; Sivakumar, M. Induction motor design analysis for electric vehicle
application. AIP Conf. Proc. 2020, 2269, 10–14.
31. Mahmoudi, A.; Soong, W.L.; Pellegrino, G.; Armando, E. Efficiency maps of electrical machines. In
Proceedings of the 2022 IEEE Energy Conversion Congress and Exposition (ECCE), Montreal, QC, Canada,
20–24 September 2022; pp. 2791–2799.
32. Lumyong, P.; Sarikprueck, P. A Study on Induction Motor Efficiency Improvement for Implementing in
Electric Vehicle. In Proceedings of the 2020 21st International Conference on Electrical Machines and
Systems (ICEMS), Jeju, Korea, 7–10 October 2020; pp. 616–619.
33. Loganayaki, A.; Bharani Kumar, R. Permanent Magnet Synchronous Motor for Electric Vehicle
Applications. In Proceedings of the 2019 5th International Conference on Advanced Computing &
Communication Systems (ICACCS), Coimbatore, India, 15–16 March 2019; pp. 1064–1069.
34. Chiba, A.; Kiyota, K. Review of research and development of switched reluctance motor for hybrid
electrical vehicle. In Proceedings of the 2022 IEEE Workshop on Electrical Machines Design, Control and
Diagnosis (WEMDCD), Turin, Italy, 26–27 March 2022; pp. 127–131.
35. Guo, Q.; Zhang, C.; Li, L.; Zhang, J.; Wang, M. Maximum Efficiency Control of Permanent-Magnet
Synchronous Machines for Electric Vehicles. Energy Proc. 2019, 105, 2267–2272.
Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 29 June 2023 doi:10.20944/preprints202306.2040.v1
20
36. Wang, W.; Fu, R.; Fan, Y. Electromagnetic Parameters Matching of Permanent Magnet Synchronous Motor
for Hybrid Electric Vehicles. IFAC-PapersOnLine 2020, 51, 407–414.
37. Karki, A.; Phuyal, S.; Tuladhar, D.; Basnet, S.; Shrestha, B.P. Status of pure electric vehicle power train
technology and future prospects. Appl. Syst. Innov. 2020, 3, 35.
38. Thakar, D.U.; Patel, R.A. Comparison of Advance and Conventional Motors for Electric Vehicle
Application. In Proceedings of the 2019 3rd International Conference on Recent Developments in Control,
Automation & Power Engineering (RDCAPE), Noida, India, 10–11 October 2019; pp. 137–142.
39. Sharifan, S.; Ebrahimi, S.; Oraee, A.; Oraee, H. Performance comparison between brushless PM and
induction motors for hybrid electric vehicle applications. In Proceedings of the 2022 Intl Aegean Conference
on Electrical Machines & Power Electronics
(ACEMP), 2022 Intl Conference on Optimization of Electrical & Electronic Equipment (OPTIM) & 2022 Intl
Symposium on Advanced Electromechanical Motion Systems (ELECTROMOTION), Side, Turkey, 2–4
September 2022; pp. 719–724.
40. Gan, C.; Wu, J.; Hu, Y.; Yang, S.; Cao, W.; Guerrero, J.M. New Integrated Multilevel Converter for Switched
Reluctance Motor Drives in Plug-in Hybrid Electric Vehicles with Flexible Energy Conversion. IEEE Trans.
Power Electron. 2019, 32, 3754–3766.
41. Kumar, R.; Saxena, R. Simulation and Analysis of Switched Reluctance Motor Drives for Electric Vehicle
Applications using MATLAB. In Proceedings of the 2019 4th International Conference on Electrical,
Electronics, Communication, Computer Technologies and Optimization Techniques (ICEECCOT),
Mysuru, India, 13–14 December 2019; pp. 23–28.
42. Chang, H.C.; Jheng, Y.M.; Kuo, C.C.; Hsueh, Y.M. Induction motors condition monitoring system with fault
diagnosis using a hybrid approach. Energies 2019, 12, 1471.
43. Ganesan, S.; David, P.W.; Balachandran, P.K.; Samithas, D. Intelligent Starting Current-Based Fault
Identification of an Induction Motor Operating under Various Power Quality Issues. Energies 2021, 14, 304.
44. Pindoriya, R.M.; Rajpurohit, B.S.; Kumar, R.; Srivastava, K.N. Comparative analysis of permanent magnet
motors and switched reluctance motors capabilities for electric and hybrid electric vehicles. In Proceedings
of the 2020 IEEMA Engineer Infinite Conference (eTechNxT), New Delhi, India, 13–14 March 2020; pp. 1–
5.
45. Rahman, M.S.; Lukman, G.F.; Hieu, P.T.; Jeong, K.-I.; Ahn, J.-W. Optimization and Characteristics Analysis
of High Torque Density 12/8 Switched Reluctance Motor Using Metaheuristic Gray Wolf Optimization
Algorithm. Energies 2021, 14, 2013.
46. Ronanki, D.; Kelkar, A.; Williamson, S.S. Extreme fast charging technology—Prospects to enhance
sustainable electric transportation. Energies 2019, 12, 3721.
47. IEA. Global E V Outlook. Towards Cross-Modal Electrification; International Energy Agency: Paris, France,
2020.
48. Faizal, M.; Feng, S.; Zureel, M.; Sinidol, B.; Wong, D.; Jian, K. A review on challenges and opportunities of
electric vehicles (evs). J. Mech. Eng. Res. Dev. JMERD 2019, 42, 130–137.
49. Suarez, C.; Martinez, W. Fast and Ultra-Fast Charging for Battery Electric Vehicles—A Review. In
Proceedings of the 2019 IEEE Energy Conversion Congress and Exposition (ECCE), Baltimore, MD, USA,
29 September–3 October 2019; pp. 569–575.
50. Mansour, M.B.M.; Said, A.; Ahmed, N.E.; Sallam, S. Autonomous parallel car parking. In Proceedings of
the 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4),
London, UK, 27–28 July 2020; pp. 392–397.
51. Wang, Y.; Sun, W.; Lu, Y. Research on application in intelligent vehicle automatic control system. J. Phys.
Conf. Ser. 2021, 1828, 012046.
52. Noh, B.; Park, H.; Yeo, H. Analyzing vehicle-pedestrian interactions: Combining data cube structure and
predictive collision risk estimation mode. Accid. Anal. Prev. 2021, 152, 105970.
53. Elma, O.; Adham, M.I.; Gabbar, H.A. Effects of Ultra-Fast Charging System for Battery Size of Public
Electric Bus. In Proceedings of the IEEE 8th International Conference on Smart Energy Grid Engineering
(SEGE), Oshawa, ON, Canada, 12–14 August 2020. 104. Brenna, M.; Foiadelli, F.; Leone, C.; Longo, M.
Electric Vehicles Charging Technology Review and Optimal Size Estimation. J. Electr. Eng. Technol. 2020,
15, 2539–2552.
105. Das, H.S.; Rahman, M.M.; Li, S.; Tan, C.W. Electric vehicles standards, charging infrastructure, and impact
on grid integration: A technological review. Renew. Sustain. Energy Rev. 2020, 120, 109618.
106. Sun, X.; Li, Z.; Wang, X.; Li, C. Technology development of electric vehicles: A review. Energies 2019, 13,
90.
107. Huda, M.; Tokimatsu, K.; Aziz, M. Techno economic analysis of vehicle to grid (V2G) integration as
distributed energy resources in Indonesia power system. Energies 2020, 13, 1162.
108. Aziz, M.; Budiman, B.A. Extended utilization of electric vehicles in electrical grid services. In Proceedings
of the 2019 4th International Conference on Electric Vehicular Technology (ICEVT), Bali, Indonesia, 2–5
October 2019; pp. 1–6.
Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 29 June 2023 doi:10.20944/preprints202306.2040.v1
21
109. Kriukov, A.; Gavrilas, M. Energy/Cost efficiency study on V2G operating mode for EVs and PREVs. In
Proceedings of the 2019 8th International Conference on Modern Power Systems (MPS), Cluj, Romania, 21–
23 May 2019.
110. Małek, A.; Caban, J.; Wojciechowski, Ł. Charging electric cars as a way to increase the use of energy
produced from RES. Open Eng. 2020, 10, 98–104.
111. Aziz, M.; Oda, T.; Ito, M. Battery-assisted charging system for simultaneous charging of electric vehicles.
Energy 2018, 100, 82–90.
112. Kurtz, J.; Bradley, T.; Winkler, E.; Gearhart, C. Predicting demand for hydrogen station fueling. Int. J.
Hydrog. Energy 2020, 45, 32298–32310.
113. Wang, D.; Muratori, M.; Eichman, J.; Wei, M.; Saxena, S.; Zhang, C. Quantifying the flexibility of hydrogen
production systems to support large-scale renewable energy integration. J. Power Sources 2020, 399, 383–
391.
114. Huang, X.; Li, Y.; Meng, J.; Sui, X.; Teodorescu, R.; Stroe, I. The Effect of Pulsed Current on the Performance
of Lithium-ion Batteries. In Proceedings of the 2020 IEEE Energy Conversion Congress and Exposition
(ECCE), Detroit, MI, USA, 11–15 October 2020; pp. 5633–5640.
115. Biao, J.; Fangfang, L.; Zhiwen, A.; Zhiqiang, X.; Bin, J. Thermal Simulation of Power Lithium-ion Battery
under Low Temperature and Preheating Condition. In Proceedings of the 2020 5th International
Conference on Smart Grid and Electrical Automation (ICSGEA), Zhangjiajie, China, 13–14 June 2020; pp.
51–54.
116. Deng, J.; Bae, C.; Denlinger, A.; Miller, T. Electric Vehicles Batteries: Requirements and Challenges. Joule
2020, 4, 511–515.
117. König, A.; Nicoletti, L.; Schröder, D.; Wolff, S.; Waclaw, A.; Lienkamp, M. An overview of parameter and
cost for battery electric vehicles. World Electr. Veh. J. 2021, 12, 1–29.
118. Hardman, S.; Chandan, A.; Tal, G.; Turrentine, T. The effectiveness of financial purchase incentives for
battery electric vehicles—A review of the evidence. Renew. Sustain. Energy Rev. 2019, 80, 1100–1111.
119. Lévay, P.Z.; Drossinos, Y.; Thiel, C. The effect of fiscal incentives on market penetration of electric vehicles:
A pairwise comparison of total cost of ownership. Energy Policy 2019, 105, 524–533.
120. Curtin, J.; McInerney, C.; Ó Gallachóir, B. Financial incentives to mobilise local citizens as investors in low-
carbon technologies: A systematic literature review. Renew. Sustain. Energy Rev. 2019, 75, 534–547.
121. Automotive Dialogue. The Impact of Government Policy on Promoting New Energy Vehicles (NEVs): The
Evidence in APEC Economies; APEC: Singapore, 2019.
122. Brückmann, G.; Bernauer, T. What drives public support for policies to enhance electric vehicle adoption?
Environ. Res. Lett. 2020, 15, 094002.
123. Abuelrub, A.; Al Khalayleh, A.R.; Allabadi, A. Optimal operation of electric vehicle charging station in an
open electricity market. Int. J. Smart Grid Clean Energy 2019, 8, 495–499.
124. Yan, Q.; Zhang, B.; Kezunovic, M. Optimized operational cost reduction for an EV charging station
integrated with battery energy storage and PV generation. IEEE Trans. Smart Grid 2019, 10, 2096–2106.
125. Huang, Z.; Li, Z.; Lai, C.S.; Zhao, Z.; Wu, X.; Li, X.; Tong, N.; Lai, L.L. A novel power market mechanism
based on blockchain for electric vehicle charging stations. Electron 2021, 10, 307.
126. Cheng, Y.H.; Lai, C.M.; Teh, J. Application of particle swarm optimization to design control strategy
parameters of parallel hybrid electric vehicle with fuel economy and low emission. In Proceedings of the
2020 International Symposium on Computer, Consumer and Control (IS3C), Taichung, Taiwan, 6–8
December 2020; pp. 342–345.
127. Zhou, S.; Chen, Z.; Huang, D.; Lin, T. Model Prediction and Rule Based Energy Management Strategy for
a Plug-in Hybrid Electric Vehicle with Hybrid Energy Storage System. IEEE Trans. Power Electron. 2021, 36,
5926–5940.
128. Lee, H.; Nakasaku, S.; Hirota, T.; Kamiya, Y.; Ihara, Y.; Yamaura, T. Analysis of Energy Consumption and
Possibility of Further Reduction of a Fuel Cell Garbage Truck. In Proceedings of the 21st International
Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA, 4–7 November 2020.
129. Lee, D.Y.; Elgowainy, A.; Dai, Q. Life cycle greenhouse gas emissions of hydrogen fuel production from
chlor-alkali processes in the United States. Appl. Energy 2020, 217, 467–479.
130. Nagasawa, K.; Davidson, F.T.; Lloyd, A.C.; Webber, M.E. Impacts of renewable hydrogen production from
wind energy in electricity markets on potential hydrogen demand for light-duty vehicles. Appl. Energy
2019, 235, 1001–1016.
131. Fan, X.; Liu, B.; Liu, J.; Ding, J.; Han, X.; Deng, Y.; Lv, X.; Xie, Y.; Chen, B.; Hu, W.; et al. Battery Technologies
for Grid-Level Large-Scale Electrical Energy Storage. Trans. Tianjin Univ. 2020, 26, 92–103.
132. Kim, S.; Oguchi, H.; Toyama, N. A complex hydride lithium superionic conductor for high-energy-density
all-solid-state lithium metal batteries. Nat. Commun. 2019, 10, 1081.
133. Cox, B.; Bauer, C.; Mendoza Beltran, A.; van Vuuren, D.P.; Mutel, C.L. Life cycle environmental and cost
comparison of current and future passenger cars under different energy scenarios. Appl. Energy 2020, 269,
115021.
Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 29 June 2023 doi:10.20944/preprints202306.2040.v1
22
134. Grunditz, E.A.; Thiringer, T. Performance analysis of current BEVs based on a comprehensive review of
specifications. IEEE Trans. Transp. Electrif. 2018, 2, 270–289.
135. Islameka, M.; Kusuma, C.; Budiman, B. Influence of Braking Strategies for Electric Trike Energy
Consumption. Int. J. Sustain. Transp. Technol. 2020, 3, 20–25.
136. Hong, J.; Park, S.; Chang, N. Accurate remaining range estimation for electric vehicles. In Proceedings of
the 2018 21st Asia and South Pacific Design Automation Conference (ASP-DAC), Macao, China, 25–28
January 2018.
137. Wachter, C. Electric three-wheelers as an alternative to combustion-engined autorickshaws in Dar es
Salaam—Generation of a standard drive cycle, Power Train modeling, and simulation of the energy
demand of light electric vehicles. In Proceedings of the Fifteenth International Conference on Ecological
Vehicles and Renewable Energies (EVER), Monte-Carlo, Monaco, 10–12 September 2020.
138. Baek, D. Runtime Power Management of Battery Electric Vehicles for Extended Range with Consideration
of Driving Time. IEEE Trans. Very Large Scale Integr. Syst. 2019, 27, 549–559.
139. Dovgan, E.; Javorski, M.; Tušar, T.; Gams, M.; Filipicˇ, B. Discovering driving strategies with a
multiobjective optimization algorithm. Appl. Soft Comput. J. 2014, 16, 50–62.
140. Ozatay, E.; Ozguner, U.; Michelini, J.; Filev, D. Analytical Solution to the Minimum Energy Consumption Based
Velocity Profile Optimization Problem with Variable Road Grade; IFAC: New York, NY, USA, 2014; Volume 19,
ISBN 9783902823625.
141. Singh, K.V.; Bansal, H.O.; Singh, D. A comprehensive review on hybrid electric vehicles: Architectures and
components. J. Mod. Transp. 2019, 27, 77–107.
142. Canbolat, G.; Yas¸ar, H. Performance Comparison for Series and Parallel Modes of a Hybrid Electric
Vehicle. Sak. Univ. J. Sci. 2019, 23, 43–50.
143. Li, X.; Williamson, S.S. Comparative investigation of series and parallel Hybrid Electric Vehicle (HEV)
efficiencies based on comprehensive parametric analysis. In Proceedings of the 2007 IEEE Vehicle Power
and Propulsion Conference, Arlington, TX, USA, 9–12 September 2007; pp. 499–505.
144. Chung, I. Fuel Economy Improvement Analysis of Hybrid Electric Vehicle. Int. J. Automot. Technol. 2019,
20, 531–537.
145. Al-Samari, A. Study of emissions and fuel economy for parallel hybrid versus conventional vehicles on
real-world and standard driving cycles. Alex. Eng. J. 2019, 56, 721–726.
146. Grün, T.; Doppelbauer, M. Comparative concept study of passive hybrid energy storage systems in 48 v
mild hybrid vehicles varying lithium-ion battery and supercapacitor technologies. World Electr. Veh. J. 2019,
10, 71.
147. Singh, P.A.; Singh, P.L.; Pundir, A.K.; Saini, A. Mild Hybrid Technology in Automotive: A Review. Int. Res.
J. Eng. Technol. 2021, 8, 4405–4410.
148. Kusuma, C.; Budiman, B.; Nurprasetio, I. Simulation Method for Extended-Range Electric Vehicle Battery
State of Charge and Energy Consumption Simulation based on Driving Cycle. In Proceedings of the
International Conference on Electric Vehicular Technology (ICEVT), Bali, Indonesia, 18–21 November 2019.
149. Taherzadeh, E.; Dabbaghjamanesh, M.; Gitizadeh, M.; Rahideh, A. A New Efficient Fuel Optimization in
Blended Charge Depletion/Charge Sustenance Control Strategy for Plug-in Hybrid Electric Vehicles. IEEE
Trans. Intell. Veh. 2020, 3, 374–383.
150. Jar, B.; Watson, N.; Miller, A. Rapid EV Chargers: Implementation of a Charger. In Proceedings of the EEA
Conference & Exhibition, Wellington, New Zealand, 22–24 June 2018; pp. 1–17.
151. Rumale, S.; Al Ashkar, H.; Kerner, T.; Koya, F.; Eitzenberger, M. Design and Implementation of an On-
Board Vehicle CHAdeMO Interface for Vehicle-to-Grid Applications. In Proceedings of the 2020 IEEE
International Conference on Power Electronics, Smart Grid and Renewable Energy (PESGRE2020), Cochin,
India, 2–4 January 2020; pp. 1–6.
152. Das, S.; Acharjee, P.; Bhattacharya, A. Charging Scheduling of Electric Vehicle incorporating Grid-to-
Vehicle (G2V) and Vehicle-toGrid (V2G) technology in Smart-Grid. In Proceedings of the 2020 IEEE
International Conference on Power Electronics, Smart Grid and Renewable Energy (PESGRE2020), Cochin,
India, 2–4 January 2020; pp. 1–6.
153. Xu, L.; Li, Z.; Sun, H.; Fan, J.; Bai, Q.; Ou, Y.; Wang, P.; Deng, B. Study on Control Strategy for Series-Parallel
Hybrid Electric Vehicles. J. Phys. Conf. Ser. 2020, 1617, 012059.
154. Zhao, H.; Burke, A. Modelling and Analysis of Plug-in Series-Parallel Hybrid Medium-Duty Vehicles. In
Proceedings of the European Battery, Hybrid and Fuel Cell Electric Vehicle Congress, Brussels, Belgium,
2–4 December 2022.
155. Ding, N.; Prasad, K.; Lie, T.T. The Design of Control Strategy for Blended Series-Parallel Power-Split
PHEV—A Simulation Study 2 Optimization Design for Series- parallel Power-split PHEV 3 Simulation
Study. Int. J. Transp. Syst. 2019, 2, 21–24.
156. Puma-Benavides, D.S.; Izquierdo-Reyes, J.; Calderon-Najera, J.D.D.; Ramirez-Mendoza, R.A. A systematic
review of technologies, control methods, and optimization for extended-range electric vehicles. Appl. Sci.
2021, 11, 7095.
Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 29 June 2023 doi:10.20944/preprints202306.2040.v1
23
157. Liu, Y.; Qiao, J.; Xu, H.; Liu, J.; Chen, Y. Optimal vehicle size and driving condition for extended-range
electric vehicles in China: A life cycle perspective. PLoS ONE 2020, 15, e0241967.
158. Reksowardojo, I.K.; Arya, R.R.; Budiman, B.A.; Islameka, M.; Santosa, S.P.; Sambegoro, P.L.; Aziz, A.R.A.;
Abidin, E.Z.Z. Energy management system design for good delivery electric trike equipped with different
powertrain configurations. World Electr. Veh.J. 2020, 11, 76.
159. Camacho, O.M.F.; Mihet Popa, L. Fast Charging and Smart Charging Tests for Electric Vehicles Batteries
using Renewable Energy. Oil Gas Sci Technol. 2018, 71, 13–25.
160. Park, S.; Nam, S.; Oh, M.; Choi, I.J.; Shin, J. Preference structure on the design of hydrogen refueling stations
to activate energy transition. Energies 2020, 13, 3959.
161. Fathabadi, H. Fuel cell hybrid electric vehicle (FCHEV): Novel fuel cell/SC hybrid power generation
system. Energy Convers. Manag. J. 2020, 156, 192–201.
162. Hu, X.; Murgovski, N.; Johannesson, L.M.; Egardt, B. Optimal dimensioning and power management of a
fuel cell/battery hybrid bus via convex programming. IEEE/ASME Trans. Mechatron. 2022, 20, 457–468.
163. El Fadil, H.; Giri, F.; Guerrero, J.M.; Tahri, A. Modeling and nonlinear control of a fuel cell/supercapacitor
hybrid energy storage system for electric vehicles. IEEE Trans. Veh. Technol. 2014, 63, 3011–3018.
164. Li, Q.; Yang, H.; Han, Y.; Li, M.; Chen, W. A state machine strategy based on droop control for an energy
management system of PEMFC-battery-supercapacitor hybrid tramway. Int. J. Hydrog. Energy 2018, 41,
16148–16159.
165. Halimah, P.; Rahardian, S.; Budiman, B. Battery Cells for Electric Vehicles. Int. J. Sustain. Transp. Technol.
2019, 2, 54–57.
166. Sharma, S.; Panwar, A.K.; Tripathi, M.M. Storage technologies for electric vehicles. J. Traffic Transp. Eng.
Engl. Ed. 2020, 7, 340.
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