Electric vehicles, the future of transportation powered by machine learning: a brief review

Abstract

Over the past decade, the world has experienced a remarkable shift in the automotive landscape, as electric vehicles (EVs) have appeared as a viable and increasingly popular alternative to the long-standing dominance of internal combustion engine (ICE) vehicles and their ability to absorb the surplus of electricity generated from renewable sources. This paper presents a detailed examination of the different categories of EVs, charging methods and explores energy generation systems tailored for EVs. As vehicle complexity and road congestion increase with the growth of EVs, the need for intelligent transport systems to improve road safety and efficiency becomes imperative. Machine learning (ML), recognized as a powerful approach for adaptive and predictive system development, has gained importance in the vehicle domain. By employing a variety of algorithms, ML effectively addresses pressing issues related to electric vehicles, including battery management, range optimization, and energy consumption. This paper conducts a brief review of ML methods, including both traditional and applied approaches, to address energy consumption issues in EVs, such as range estimation and prediction, as well as range optimization.

Full text

Open Access
© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0
International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you
modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of
it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise
in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted
by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy
of this licence, visit http:// creat iveco mmons. org/ licen ses/ by- nc- nd/4. 0/.
REVIEW
Boudmenetal. Energy Informatics (2024) 7:80
https://doi.org/10.1186/s42162-024-00379-3
Energy Informatics
Electric vehicles, thefuture oftransportation
powered bymachine learning: abrief review
Khadija Boudmen1*, Asmae El ghazi1,2,4, Zahra Eddaoudi1, Zineb Aarab2,3 and Moulay Driss Rahmani1
Abstract
Over the past decade, the world has experienced a remarkable shift in the automotive
landscape, as electric vehicles (EVs) have appeared as a viable and increasingly popu-
lar alternative to the long-standing dominance of internal combustion engine (ICE)
vehicles and their ability to absorb the surplus of electricity generated from renewable
sources. This paper presents a detailed examination of the different categories of EVs,
charging methods and explores energy generation systems tailored for EVs. As vehicle
complexity and road congestion increase with the growth of EVs, the need for intel-
ligent transport systems to improve road safety and efficiency becomes imperative.
Machine learning (ML), recognized as a powerful approach for adaptive and predic-
tive system development, has gained importance in the vehicle domain. By employ-
ing a variety of algorithms, ML effectively addresses pressing issues related to electric
vehicles, including battery management, range optimization, and energy consump-
tion. This paper conducts a brief review of ML methods, including both traditional
and applied approaches, to address energy consumption issues in EVs, such as range
estimation and prediction, as well as range optimization.
Keywords: Electric vehicles, Machine learning, Energy generation system
Introduction
Air pollution has become a serious danger to our health, leading to both immediate and
long term problems. ese problems include emphysema, respiratory infections (e.g.,
pneumonia, bronchitis), cancer, asthma, and other chronic diseases. Increased human
activities have worsened air pollution, causing a buildup of greenhouse gases (GHGs).
is buildup results in unusual temperature increases. Additionally, air pollution can
make existing health conditions worse and impact our overall well-being.
e transportation sector significantly contributes to worldwide GHG emissions,
accounting for approximately 23% of the overall emissions (Yu etal. 2019). ese
emissions consist of carbon dioxide, hydrofluorocarbons (HFCs), nitrous oxide,
methane, hydrochlorofluorocarbons (HCFCs), and ozone, all of which contribute to
increasing concentrations of GHGs. e ICE is a major contributor to air pollution,
releasing about 35% of carbon monoxide (CO), 30% of hydrocarbons (HC), and 25%
of nitric oxides (NOx), as well as lead particles and particulate matter (PM2.5) directly
into the atmosphere 2 as Fig.1 shows (Macharia etal. 2023). Such a disconcerting
*Correspondence:
khadija.boudmen@um5r.ac.ma
1 LRIT, Associated Unit to CNRST
(URAC 29) Faculty of Sciences,
Mohammed V-Agdal University,
Rabat, Morocco
2 GENIUS Laboratory, SUPMTI
of Rabat, Rabat, Morocco
3 SIWEB, Mohamadia School
of Engineers, Mohammed
V University in Rabat, Rabat,
Morocco
4 EEIS Laboratory, ENSET
of Mohammedia, Hassan
II, University of Casablanca,
Mohammedia, Morocco
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 2 of 19
Boudmenetal. Energy Informatics (2024) 7:80
statistic led to the adoption of the ‘Paris Declaration on Electromobility and Climate
Change and Call to Action’, a worldwide effort to combat the greenhouse effect. e
declaration aims to limit the increase in global temperatures to 2°C, a crucial mile-
stone in mitigating the harmful impacts of climate change.
EVs have seen a rise in popularity as a greener alternative to conventional vehi-
cles powered by gasoline in the last years. as well as being promoted as an achiev-
able way to reduce carbon dioxide emissions (CO2) in the face of ongoing global fossil
fuel shortages and pollution (Xu etal. 2020; Koubaa etal. 2021). Countries world-
wide have established ambitious targets to encourage the adoption of EVs or are even
planning to ban the sale of petrol vehicles in the future (Chen etal. 2020). In coun-
tries where renewable energy is adopted as the primary source, the influence of elec-
tric vehicles (EVs) on the environment is more sustainable (Anwar etal. 2021; Sharif
etal. 2020). EVs symbolize a remarkable stride towards fostering a sustainable and
eco-friendly energy system (Vita and Koumides 2019). Renewable energy sources and
electric vehicles offer the opportunity to reduce carbon emissions from power gener-
ation and transport sectors (Lazarou etal. 2018; Richardson 2013). China plans to sell
7 million EVs per year by 2025, equivalent to one-fifth of the country’s total domestic
demand. Norway has set a target of 100% of new car sales to be electric by 2025. e
United States and the United Kingdom has promised that they will phase out petrol
vehicle sales by 2040. e automotive sector aims to make EVs the largest powertrain
in the automotive market by 2030 (Hertzke etal. 2018).
EVs come in multiple types such as battery electric vehicles (BEVs), plug-in hybrid
electric vehicles (PHEVs) and fuel cell electric vehicles (FCEVs). Each type has its own
unique charging methods, issues and challenges. For example, BEVs require longer
charging times and have a limited range, while PHEVs have a shorter electric-only range
and require both electric and petrol refueling. FCEVs, on the other hand, face challenges
with the availability of hydrogen infrastructure. To address these challenges, research-
ers and engineers have been developing various energy generation systems such as
regenerative braking systems, photovoltaic cell systems and fuel cell systems as well as
energy management strategies for EVs, these include rule-based and optimization-based
strategies (Li etal. 2019). Machine learning (ML) techniques have also been applied to
EVs, particularly in battery management, range optimization, and energy consumption
prediction.
Fig. 1 Internal combustion engine emissions
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 3 of 19
Boudmenetal. Energy Informatics (2024) 7:80
In this review article, we will present a comprehensive overview of electric vehicles
and their different types, charging methods and challenges. We will also discuss the
different power generation systems and energy management strategies used in EVs. In
addition, we will explore the use of ML in range optimization and energy consumption
prediction.
e subsequent sections of this paper are structured as follows: the employed meth-
odology of this review is outlined in “Methodology” section. “Electric vehicles” Section
provides a description of EVs types and charging methods. In “Challenges for fuel cell
EVs and hybrid EVs” section, we discuss some challenges for Fuel Cell EVs and Hybrid
EVs. In “Energy generation systems for EVs” section, we turn our focus to the energy
generation systems that are used for EVs like photovoltaic cell systems, fuel cell sys-
tems and regenerative braking systems. We address used energy management methods
for EVs in “Energy management strategies used in EVs” section, discussing rule-based
strategies and optimization-based strategies. “Electric vehicles and machine learning”
Section presents ML applications in EVs fields such as range optimization and energy
consumption. A discussion about the most used ML algorithms in EVs optimization and
management was discussed in “Discussion” section. e final part, which is “Conclusion
and perspectives” section, presents our conclusion about the research as well as our per-
spectives on ML in the EVs field.
Methodology
is paper will take a close look at EVs. It looks at different types of EVs, how they are
charged and the challenges they face. e paper also talks about making energy systems
specifically for EVs and how that affects things. It also talks about how EVs use energy.
In this paper, we look carefully at many ways of using ML. ese are like tools that help
computers learn and predict things. We use these tools to solve specific problems with
EVs, such as managing batteries (including checking their condition, detecting problems
and controlling charging) and guessing how much energy will be used for driving. e
paper focuses on comparing these different tools to see which ones work best for dif-
ferent tasks. e review incorporates criteria for inclusion and exclusion to ensure the
provision and evaluation of pertinent and current information.
e criteria for inclusion in this paper are specified as follows:
• Electric vehicles: is criterion guarantees that only articles that provide comprehen-
sive explanations of EVs types, charging methods and challenges are included in the
review.
• Energy generation systems: is criterion covers studies and articles that concentrate
on the energy generation systems using in EV field.
• Energy management strategies: is criterion includes papers that explore EMSs
used in HEVs, while there is still limited research on EMSs used in PEVs. However,
some EMSs developed for HEVs can also be adapted for use in PEVs.
• Machine learning algorithms used in electric vehicles between 2012 and 2023: is
criterion guarantees that only studies and articles discussing ML algorithms used in
EVs within the last years are included in the review. is guarantees that the infor-
mation presented remains current and pertinent to recent advancements in the field.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 4 of 19
Boudmenetal. Energy Informatics (2024) 7:80
e exclusion criteria are the following:
• General papers of machine learning and old sources (below 2012): General papers on
ML that don’t specifically discuss its application in the EVs domain are filtered out by
this criterion
• Non peer reviewed sources: A peer review is an essential phase in guaranteeing the
quality of scientific research. So that, it’s excluded by this criterion.
• Letters and reports: Letters and reports lacking original research or substantial addi-
tions to the domain are excluded by this criterion.
• Non-English sources: Papers that are not published in English are also excluded. is
guarantees that the information presented reaches a broader audience.
Electric vehicles
EVs have been gaining popularity in recent years due to their potential to reduce GHG
emissions and dependence on fossil fuels. Unlike traditional ICE vehicles, EVs are pow-
ered by electricity held in batteries and electric motors. ese vehicles have emerged as a
promising solution for sustainable transportation.
EVs can be classified into different types, such as BEVs, hybrid electric vehicles
(HEVs), PHEVs, and FCEVs. Each type has its own unique features, charging methods,
and challenges. Understanding the distinctions between these types is crucial to grasp
the diverse landscape of EVs.
Electric vehicle types
EVs come in different types, including BEVs, which run purely on a battery with no sec-
ondary energy source and emit no emissions. BEVs typically use large battery packs to
give the vehicle a satisfactory range. A typical BEV can travel between 160 and 250km
on a single charge, and some are even able to travel up to 500km before they need to be
recharged. An example of such a vehicle is the Nissan Leaf (Nissan Reveals LEAF e-Plus
2023), which runs entirely on electricity. It is currently equipped with a 62kWh battery,
which allows the user to travel up to 360km on a single charge.
Hybrid EVs are driven by a fusion of a conventional gasoline engine and an electric
motor. Contrary to PHEVs, HEVs do not have the capability to be plugged in for battery
charging. Instead, the battery that powers the electric motor is charged by the gasoline
engine and by the energy generated during braking. e fourth generation of the Toyota
Prius hybrid had a 1.3kWh battery. is gave it a theoretical all-electric range of 25km
(Toyota Prius PHV 2013).
PHEVs are an improved version of HEVs that can connect to the power grid for battery
charging. PHEVs are powered by a traditional gasoline engine and an electric motor that
is charged by an external electrical source. PHEVs can store sufficient electrical energy
from the grid to substantially decrease fuel usage during typical driving situations. e
Ford Escape PHEV (Ford Escape® 2024) is equipped with a 14.4kWh battery, enabling it
to travel approximately 60km using only electric power.
Although PHEVs were developed due to the limited mileage of EVs and a low num-
ber of public charging stations, BEVs are becoming increasingly popular. is is because
battery technologies are being improved to enhance their energy density. Currently,
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 5 of 19
Boudmenetal. Energy Informatics (2024) 7:80
two-thirds of existing EVs are BEVs, and they have a higher priority for charging at park-
ing lots or charging stations since they have a single energy source (Sadeghian et al.
2022). e growing popularity of BEVs underscores the need for further infrastructure
development to meet the demand.
Another type of EV is the FCEV or hydrogen EV, which uses hydrogen as its fuel
source. ese vehicles feature an electric motor that operates on a combination of com-
pressed hydrogen (H2) and oxygen (O2) from the air to generate electricity. e only by-
product of this process is water. FCEVs are zero-emission vehicles, but it is important to
note that most hydrogen is currently produced from natural gas, which is a fossil fuel.
e Toyota Mirai FCEV (Toyota Mirai 2023) exemplifies this type of vehicle, capable
of traveling 647km without refueling. e different types of EVs discussed above are
shown in Fig.2 below.
Charging methods
Besides autonomy, another important aspect of electric vehicles (EVs) is the charging
process. For EVs to be truly successful, users need to be able to charge their vehicles
quickly and easily. ere are three primary charging methods: battery exchange, wire-
less charging, and conductive charging. Conductive charging can be further divided into
pantograph charging and overnight charging as illustrated in Fig.3.
Battery swap station (BSS)
e technique known as ‘Battery Exchange’ involves paying a monthly rent for the
battery to the proprietor of the Battery Swapping Station (BSS). e gradual charging
process of the BSS helps to prolong the life of the battery (Ahmad etal. 2018). e inte-
gration of locally generated renewable energy sources (RES) such as wind and solar into
the BSS system is much easier. A key advantage of this approach is that the driver can
rapidly replace a discharged battery without having to leave the vehicle (Gschwendtner
etal. 2021).
However, BSSs can be more expensive than refueling ICE vehicles because of the ele-
vated monthly rental fees charged by the BSS owner. is is due to the fact that the BSS
owner owns the EV batteries. BSSs also necessitate several costly batteries and a large
Fig. 2 Different types of electric vehicles (Sadeghian et al. 2022)
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 6 of 19
Boudmenetal. Energy Informatics (2024) 7:80
amount of space to store them, which can be expensive in high-traffic areas. In addition,
a BSS may have a specific battery model, but EV batteries may have different standards
(Li etal. 2018; Erdinç etal. 2017).
Wireless power transfer (WPT)
Wireless power transfer (WPT) is a technology that uses two coils to transfer energy
without the need for a physical connection. WPT has attracted attention for use in elec-
tric vehicles because one coil is placed on the road surface, and another coil is positioned
inside the vehicle. is technology is valued for its safety, convenience, and lack of
requirement for a standard plug (though it does rely on standard coupling technology).
In addition, WPT can charge a vehicle while it is moving (Sanguesa etal. 2021).
However, WPT does have some challenges. Inductive power transfer is typically inef-
ficient, requiring an air gap of between 20 and 100 cm between the transmitter and
receiver coils for optimal power transfer (Chowdhury etal. 2023). Also, eddy current
losses can occur if the transmitter coil remains active. Finally, there is a risk of communi-
cation latency between the transmitter and the vehicle (Patil etal. 2017).
Conductive charging (CC)
Conductive charging (CC) necessitates an electrical link between the vehicle and charg-
ing port, providing different charging alternatives like level 1, level 2, and level 3 charg-
ing. is method boasts high charging efficiency owing to its direct connection. Public
charging stations commonly utilize power charging levels 2 and 3. e initial two levels
(Levels 1 and 2) exert a lesser impact on the distribution system.
CC allows for vehicle-to-grid (V2G) support, which can help to reduce grid loss, main-
tain voltage levels, prevent grid overloading, provide active power support, and com-
pensate for reactive power using the battery of the vehicle (Chowdhury etal. 2023; Patil
etal. 2017).
However, high-power conductive charging (level 3 charging) can have a number of
negative impacts on the distribution system, including voltage deviation, reduced system
reliability, and increased power losses (Dharmakeerthi etal. 2014). It can also increase
peak demand and reduce transformer lifespan (Dharmakeerthi etal. 2014; Habib etal.
2018). Additionally, Level 3 charging requires a standardized connector, access to
Fig. 3 EVs charging methods
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 7 of 19
Boudmenetal. Energy Informatics (2024) 7:80
electricity from the grid, and complex infrastructure (Yang etal. 2015). V2G technol-
ogy also necessitates robust communication between the grid and the vehicle, and it can
reduce battery lifespan because of frequent charging and discharging (Arif etal. 2021a).
Table1 summarizes the charging methods, which include BSS, WPT, and CC stations.
For electric buses and trucks with higher battery capacity and quick charging require-
ments, two main charging techniques are used:
Overnight depot charging: is system can be configured for slow or rapid charg-
ing and is typically installed at the terminus of bus routes for nighttime charging. Slow
charging is the is preferred as it minimally impacts the distribution grid (Arif etal. 2020,
2021b).
Pantograph charging: is is a type of opportunity charging that is used for vehicles
with higher battery capacity and power requirements. Lowering the bus investment cost,
pantograph charging reduces the investment in the bus battery, although it results in
higher costs for the charging infrastructure (Meishner etal. 2017). Pantograph charging
is further divided into two categories:
• Top-down pantograph: Referred to as an off-board top-down pantograph, this setup
is mounted on the roof of the bus stop. It provides high-power direct current and
has been implemented in Singapore, Germany, and the United States (Carrilero etal.
2018).
• Bottom-up pantograph: Known as an on-board bottom-up pantograph, this charging
method is suitable for applications where the charging equipment is already installed
in the bus (Carrilero etal. 2018).
Challenges forfuel cell EVs andhybrid EVs
Fuel cell EVs
Reducing manufacturing costs is essential for the commercialization of fuel cells. e
US Department of Energy (DOE) aims to minimize the price of fuel cells to $40/kW by
Table 1 Advantages and disadvantages of charging methods
Method References Disadvantage Advantage Year
BSS Arif et al. (2021a) The monthly rent to BSS makes it
more expensive than an Internal
Combustion
Engines (ICE) vehicle
Rapid battery replacement (fully
charged) 2021
Arif et al. (2021a) The significant costs needed for
both equipment and batterie BSS extends battery life by charg-
ing slowly 2021
Li et al. (2018) Many areas needed to accommo-
date the batteries Easy to integrate with the locally
generated Renewable Energy
Sources (RESs)
2018
CC Habib et al. (2018) Need a standard connector/
charging level Reduce grid losses while main-
taining voltage levels 2018
Yoldaş et al. (2017) Electricity grid restrictions Provide maximum efficiency 2017
Negarestani et al. (2016) Complex infrastructure Provide multiple charging levels 2016
WPT Arif et al. (2021a) In general, power transmission is
inefficient Standard connectors not
required 2018
Patil et al. (2017) The transmitter and EV should be
able to communicate in real time Recharge while driving 2017
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 8 of 19
Boudmenetal. Energy Informatics (2024) 7:80
2025, targeting a goal of $30/kW with the primary aim of completely replacing the tra-
ditional power system and ensuring sustained competitiveness in the long term (Borup
etal. 2018, 2020). Durability and performance are the other two critical evaluation cri-
teria for fuel cells, but reducing the load of expensive electrocatalysts to cut costs may
compromise their durability and performance. erefore, simultaneously achieving both
the durability and cost targets set by the DOE is a considerable challenge. Balancing cost
reduction with performance and durability remains a critical challenge in the commer-
cialization of fuel cell technology. Fuel cells are crucial for commercial uses in trans-
portation and stationary power generation. Introduced in 2017, Toyota’s Mirai, the first
commercially available FC vehicle, was priced around $60,000 and logged over 3000h
of real-world driving. However, it failed the DOE’s accelerated stress test protocol after
5000 cycles (Wang etal. 2020).
DOE aims to surpass 5000 working hours for commercial fuel cell vehicles by 2025,
with the ultimate objective being 8000h (METI Ministry of Economy, Trade and Indus-
try 2023). Manufacturers of stationary fuel cells aspire to achieve mass production, aim-
ing to reduce costs and enhance durability. To realize cost-effective and durable fuel
cell systems, ongoing progress in manufacturing processes and materials is imperative.
As evidenced by Panasonic’s fifth-generation stationary fuel cell, which weighs a mere
65kg, occupies an area of 1.7 m2, and boasts a durability of 90,000h (METI Ministry
of Economy, Trade and Industry 2023; Arias 2019), advancements are already under-
way. Japan’s hydrogen strategy aligns with ongoing enhancement, targeting an efficiency
exceeding 55% by 2025 (ultimate goal: over 65%) and a durability of 130,000h for com-
mercial stationary fuel cells (Arias 2019). ese ambitious objectives underscore the
ongoing efforts to elevate the performance and reliability of fuel cell technology.
Hybrid EVs
HEVs emerge as a promising prospect for the future of transportation, driven by the
substantial increase in crude oil prices over recent decades, prompting consumers to
explore alternative energy sources (Williamson etal. 2006). In comparison to hybrid
vehicles featuring ICEs, BEVs and PHEVs exhibit higher energy efficiency and nearly
zero hazardous emissions. A significant body of researchers has contributed to enhanc-
ing the efficiency and performance of PHEVs, showcasing their capability to perform
well within the HEV framework (Atabani etal. 2011). Existing research shows that these
technologies can enable high-performance HEVs. However, the reliability and intelligent
systems of HEVs still need improvement. erefore, there are many factors that must be
considered before HEVs can be fully embraced by the market, including the following
challenges (Ong etal. 2012):
• Renewable energy sources for vehicle applications have low energy and power densi-
ties. Exploring advanced energy storage technologies and improving energy density
are essential to overcome these limitations.
• HEVs are still expensive. Reducing manufacturing costs and increasing economies of
scale are necessary to make HEVs more affordable for consumers.
• e refueling station infrastructure for HEVs needs to be expanded. Light HEVs
require small storage tanks, while other HEVs may use an exchange storage tank
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 9 of 19
Boudmenetal. Energy Informatics (2024) 7:80
system. Developing a robust refueling infrastructure that supports different types of
alternative fuels and storage systems is crucial for the widespread adoption of HEVs.
• Recharging plug-in BEVs is time-consuming, so rapid recharging systems need to be
developed. e development of lithium-ion batteries, which are lightweight and have
a short recharge time, has enabled car manufacturers to produce BEVs and hybrid
vehicles. Continued advancements in battery technology and the development of
fast-charging infrastructure are key to addressing the issue of lengthy recharging
times and improving the convenience of plug-in BEVs.
Energy generation systems forEVs
Photovoltaic cell systems
Photovoltaic (PV) cells also known as solar cells, transform sunlight directly into electri-
cal energy. ey are widely used to harness renewable energy in various applications.
While individual PV cells have a low power output, typically 1–2 watts, they can be con-
nected in series and/or parallel chains to form modules or panels. ese panels can then
be grouped together to form PV arrays to meet greater power requirements (Kalantar
and Mousavi 2010). is modular configuration allows PV systems to be scaled and
customized to meet specific energy requirements. PV systems also necessitate a solar
inverter to convert the direct current (DC) produced by the PV cells into alternating
current (AC), along with mounting hardware, cabling, and other electrical components.
One of the main advantages of photovoltaic (PV) systems is their clean operation,
emitting no pollution or greenhouse gases. ey are also low-maintenance and have a
long lifespan (Lo Piano and Mayumi 2017; Advantages Disadvantages of Solar Power
2023). However, high initial costs and unpredictable availability are significant draw-
backs (Sukamongkol etal. 2002; Deshmukh and Deshmukh 2008). Anticipated improve-
ments in technology and the realization of economies of scale are poised to overcome
these cost barriers and enhance the reliability of PV systems in the future. PV cells can
be fabricated from crystalline silicon, the prevailing material in the market, or from thin
films incorporating substances like cadmium telluride (CdTe) as well as copper indium
diselenide (CIS). Although crystalline silicon exhibits higher efficiency, PV cells based
on thin-film technology are lighter and more cost-effective to produce (NREL 2012;
Photovoltaics Report 2023).
Researchers are developing new PV technologies to improve efficiency and reduce
costs. ird-generation PV cells are being developed using new materials such as solar
inks, solar dyes, and conductive plastics. ese advancements have the potential to make
PV systems even more performant and affordable. PV systems find practical applications
in various domains, including powering buildings, spacecraft, road lights, and even facil-
itating daytime charging for commuter vehicles (Birnie 2009). Although the direct inte-
gration of PV systems into commercial EVs remains challenging due to space constraints
and limited power generation, they can still contribute to improving vehicle efficiency
(10–20%) or maintaining comfortable temperatures inside the vehicle through the oper-
ation of the air conditioner (Richardson 2013).
e output current of a PV module can be presented as follows (Villalva etal. 2009;
Salmi etal. 2012):
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 10 of 19
Boudmenetal. Energy Informatics (2024) 7:80
where Ipv,n is the current generated by the PV module at the nominal condition of solar
radiation at 1000W/m2 and temperature at 25°C. KI is the indicates the short circuit
current temperature coefficient (A/°C). T and Tn is the actual and nominal temperatures
(K). G and Gn is the actual and nominal solar radiation (W/m2).
e saturation current I0 depends on the temperature and described as follows:
where Isc,n is the short circuit current (A). Voc,n is the open circuit voltage (V) at the
nominal conditions. KI is the current coefficient. KV is the voltage coefficient.
Regenerative braking systems
Regenerative braking systems allow vehicles to recover the kinetic energy generated
during braking and store it for later use. is energy can be converted into electrical,
hydraulic, or mechanical energy. Without a regenerative braking system, this kinetic
energy would be wasted as heat generated by the brakes. Currently, four methods are
employed for implementing regenerative braking systems: the electric M/G and batteries
or SC method, hydraulic P/M and HACCs, flywheel energy storage, and spring potential
energy storage (Clegg 1996; Valente and Ferreira 2008).
In terms of energy efficiency, charging and discharging ability, power density, and cost-
effectiveness, each method has its own advantages and disadvantages. Of these methods,
hydraulic and flywheel regenerative systems have the highest energy efficiency. Hydrau-
lic systems also demonstrate rapid charging and discharging capabilities, enhanced
power density, and a greater ability to recover maximum braking energy. However, bat-
tery-based systems are not ideal for frequent charging and discharge due to the risk of
overheating, reduced lifespan, or destruction. SC regenerative systems can be expensive.
Spring regenerative systems have the lowest energy efficiency (Li etal. 2019; Jiang etal.
2013; Hui etal. 2011; Zeiaee 2016). Additional research and development are required
to optimize regenerative braking systems and address their limitations for widespread
implementation in electric vehicles.
Fuel cell systems
Fuel cell (FC) systems convert chemical energy into electrical energy through chemical
reactions between hydrogen (or hydrocarbons like methanol or natural gas) and oxygen
from the air, aided by catalysts. e conversion process involves splitting hydrogen into
protons and electrons. e electrons then flow through a circuit, producing an electric
current, while the protons pass through the electrolyte. FCs are known for their quiet,
reliable, and environmentally friendly operation, as well as their high efficiency (Mekh-
ilef etal. 2012).
ere are six types of FCs, classified based on their choice of fuels and electrolytes:
direct methanol fuel cells (DMFCs), alkaline electrolyte fuel cells (AFCs), molten car-
bonate fuel cells (MCFCs), phosphoric acid fuel cells (PAFCs), solid oxide fuel cells
(1)
I
=IPV −I0

exp q(V+RSI)
N
S
KTa −1

−V+RsI
R
p
(2)
I
0=
I
sc,n+
K
I
(T
−
T
n
)
exp q[Voc,n+Kv(T−Tn)]
aNSKT
−
1
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 11 of 19
Boudmenetal. Energy Informatics (2024) 7:80
(SOFCs), and proton exchange membrane fuel cells (PEMFCs) (Mekhilef etal. 2012).
Each type of fuel cell has its own unique characteristics, advantages, and suitability for
specific applications. DMFCs, despite having high energy density, emit CO2 and exhibit
lower efficiency. MCFCs and SOFCs, operating at high temperatures (600–1000°C), are
typically employed in electric utilities and distributed power generation. For transpor-
tation, DMFCs, PEMFCs, AFCs, and PAFCs are common choices due to their normal
or moderate operating temperatures. PEMFCs, in particular, stand out with the highest
power density among FCs, offering benefits like a long lifespan, less-temperature opera-
tion, and rapid response, making them particularly appealing for transportation applica-
tions (Tie and Tan 2013; Hannan etal. 2017). Although FCs have a high initial cost, it
decreases as the market expands and economies of scale improve.
PEMFCs are the most promising FC source for use in plug-in electric vehicles (PEVs),
and empirical PEMFC models can be derived from the Nernst equation. Ongoing
research and technological advancements are focused on improving the efficiency, dura-
bility, and cost-effectiveness of fuel cell systems for wider adoption in electric vehicles.
e theoretical voltage produced by a typical fuel cell’s individual cell can be expressed
as (Hannan etal. 2014):
where E0 is the open circuit voltage of the cell at standard pressure. R is the universal gas
constant. F is the Faraday’s constant. T is the absolute operating temperature.
PH2
is the
partial pressure of hydrogen inside the cell.
PO2
is the partial pressure of oxygen inside
the cell.
PH2O
is the partial pressure of water vapor inside the cell.
However, due to factors like activation losses, internal current losses, resistive losses,
and concentration losses, the actual voltage produced by a single cell is less than the
ideal potential. erefore, the output voltage of the FC stack can be described as (Han-
nan etal. 2014; Andrea Calvo etal. 2006):
where N is the number of cells in the stack.
Pstd
is the standard pressure.
VL
is the voltage
losses.
Energy management strategies used inEVs
Energy management strategies (EMSs) are crucial for systems with multiple energy
sources as they control power distribution within powertrains, impacting vehicle perfor-
mance, efficiency, and component longevity (Sabri etal. 2016). While research on EMSs
for PHEVs is limited compared to HEVs, some EMSs developed for HEVs can also be
utilized in PHEVs. erefore, this section initially introduces EMSs commonly employed
in HEVs before discussing their potential adaptation for PHEVs. EMSs for HEVs are
broadly classified into two main categories, as shown in Fig. 4: ruled-based strategies
and optimization-based strategies.
(3)
E
cell =E0+
RT
2
F
ln
PH2

PO2
PH2O
(4)
V
FC =N




E0+
RT
2Fln




PH2PO2
Pstd 
1
2
PH2O




−V
L
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 12 of 19
Boudmenetal. Energy Informatics (2024) 7:80
Rule‑based strategies
Supervisory control of HEVs commonly employs Rule-based (RB) strategies, leverag-
ing heuristics, expert knowledge, and mathematical models (Salmasi 2007). RB strat-
egies can be divided into two categories: fuzzy and deterministic methods. Fuzzy RB
techniques, such as traditional, adaptive, and predictive control strategies, employ fuzzy
logic theory to handle approximate reasoning and are better suited for sophisticated or
intricate powertrain systems. Deterministic RB approaches like state machine-based,
power and modified power follower, and on/off thermostat strategies employ specific
rules to assess power distribution accurately (Enang and Bannister 2017). RB strategies
are immediate solutions known for their simplicity, strong reliability, and innate suitabil-
ity for online applications, requiring minimal computational overhead. However, craft-
ing RB strategies can prove time-intensive due to the challenge of establishing precise
rules, frequent parameter adjustments, and calibration needed to enhance vehicle per-
formance. Rules must be adjusted for diverse vehicle setups and evolving driving condi-
tions. Additionally, RB strategies do not prioritize minimization or optimization, thus
limiting their ability to optimize fuel economy to its fullest extent (Enang and Bannister
2017; Zhang etal. 2015).
Optimization‑based strategies
One approach to improving energy efficiency in hybrid vehicles (HVs) and EVs is the
deployment of optimization-based strategies. ese strategies seek to decrease fuel con-
sumption or emissions by calculating optimum reference torques and gear ratios based
on a minimizing cost function (Ramachandran and Stimming 2015). ere are two main
types of optimization solutions: global and real-time. Global optimization solutions aim
to reduce energy losses over the whole driving cycle but can’t be used in real-time energy
management. ese solutions are useful as control benchmarks compared to other strat-
egies. However, real-time optimization solutions can be implemented online and involve
the reduction of global optimization challenges into a sequence of local optimization
challenges which excludes the need for future driving information. (Çağatay Bayindir
etal. 2011).
Global optimization strategies are classified into different methods such as linear pro-
gramming (LP), dynamic programming (DP), stochastic DP, genetic algorithm (GA), and
particle swarm optimization (PSO) (Çağatay Bayindir etal. 2011).
Fig. 4 Classification of EMSs employed in HEV
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 13 of 19
Boudmenetal. Energy Informatics (2024) 7:80
On the other hand, real-time optimization strategies address global optimization
issues by solving a sequence of local optimization challenges, which removes the need to
obtain future driving information (Enang and Bannister 2017).
As a result, real-time optimization strategies can be employed for online applications.
ese strategies can be classified into several types, including model predictive control
(MPC), intelligent control, robust control, decoupling control strategies (DCS), and
equivalent fuel consumption (EFC) minimization, (Advantages Disadvantages of Solar
Power 2023; Sukamongkol etal. 2002). Each type of optimization-based strategy has its
own advantages and challenges and requires appropriate implementation and tuning to
achieve optimal energy management in EVs.
Electric vehicles andmachine Learning
Machine learning inrange optimization
Range estimation (RE) is a critical step in achieving EV range optimization and is one
of the key topics of research and investigation in EV technology these days. Precise RE
can greatly alleviate range concerns experienced by EV drivers due to restricted driv-
ing distance It empowers EV drivers to make informed decisions about driving, park-
ing, and charging, as well as participate more actively in vehicle-to-grid (V2G) charging.
Yet, conventional RE techniques are occasionally not effective because of their failure
to account for dynamically changing outside and environmental circumstances (Huang
etal. 2017; Zhang etal. 2012; Pan etal. 2017).
For instance, the range predictors in Tesla’s Model S predict the upcoming available
range by analyzing the energy consumption from the preceding miles, without account-
ing for variations in driving conditions, driving habits and environmental factors (Dazi-
ano 2013). Compared to traditional RE methods, artificial intelligence (AI) has the
potential to provide more precise RE by modeling the complicated relationship between
RE and the factors that affect it. AI algorithms, such as ML, can reliably predict upcom-
ing environmental and driving conditions using past and present data, resulting in a
more precise range estimate.
AI algorithms have been employed for RE by explicitly leveraging environmental as
well as historical driving attitude data(Sun etal. 2019; Yavasoglu etal. 2019), predicting
EV battery energy and power consumption (Pan etal. 2017; Zheng etal. 2016) and rec-
ognizing driving conditions and behaviors (Pan etal. 2017; Lee and Wu 2015). Real-time
historical EV discharge data is selected for applicable batteries, vehicle, and external
parameters (Table2) as well as removing missing and erroneous data for ML training.
Additionally, EV historical data has the potential to be combined with historical weather
data and road conditions data to incorporate external parameters, thereby enhancing the
RE estimation accuracy using ML training (Sun etal. 2019; Zheng etal. 2016).
ML models are capable of learning to directly predict EV range and future EV energy
or power consumption. By taking into account the evolving EV internal and external
conditions in a computationally efficient manner, and without the use of complex explicit
models, ML enables more accurate RE (Pan etal. 2017; Zheng etal. 2016). Parameters
more influence on RE, such as battery state of charge (SOC) and external temperature,
can be approximated using correlations, which reduces the complexity and training time
of the ML model (Sun etal. 2019).
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 14 of 19
Boudmenetal. Energy Informatics (2024) 7:80
Machine learning inenergy consumption
To predict the remaining driving range of an EV, it is important to accurately predict
its energy consumption. is prediction relies on calculating the energy required to
drive the vehicle, energy lost through the drivetrain, and energy used to power auxiliary
devices (Smuts etal. 2017). However, accurately estimating the range of an EV remains
a challenge due to factors such as limited driving range, long charging time, high battery
replacement cost, and inadequate charging infrastructure. ese issues can be addressed
by ameliorating battery performance and raising the number of charging stations, but
both solutions are highly expensive and may not fully address drivers’ concerns about
the remaining driving range estimates. erefore, adequate and precise range estima-
tion is needed to enhance driver confidence and encourage widespread adoption of EVs.
Recent studies suggest using advanced methods to accurately predict the energy con-
sumption of EVs, resulting in increased driving range and reduced range anxiety. is
promotes driver’s confidence and encourages EV usage over longer distances. In recent
times, ML techniques have been employed for predicting the energy consumption of
EVs as summarized in Table3.
Discussion
Various ML algorithms are used to optimize and resolve EV’s issues. According to
Table 4, Multiple Linear Regression (MLR) stands out as a popular and adaptable
approach for EV optimization. e importance of MLR results from its capacity to detect
Table 2 Research of ML in electric vehicle RE
MAE mean absolute error, MSE mean-squared error
Algorithm Parameters RE accuracy
Historical data Significant parameter
identification using cor-
relation analysis and
multiple linear regression
(MLR)
Vehicle speed
Acceleration
Past power consumption
Past distance
Past trip run time
Temperature
Weight of loads
Tire pressure
Frontal area
External road elevation
Recent energy consump-
tion
External road elevation
1.63 km (MAE) (Nowaková
and Pokorný 2020)
Classification and regres-
sion tree (CART) 1.27 km (MAE) (Sun et al.
2019)
Artificial neural networks
(ANN) 2.2% (MSE) accuracy for
a 50.4 km real-life EV trip
(Rhode et al. 2020)
Gradient boosting deci-
sion tree (GBDT) 0.82 km (MAE) (Sun et al.
2019)
Prediction of future
energy and power con-
sumption
Data clustering using self-
organizing maps (SOM)
pursued by regression tree
Principal component
regression (PCR)
Multiple linear regression
(MLR)
Support vector regression
(SVR)
Linear regression (LR)
Battery SOC
SOH
Auxiliary load
Weight
External road type
Traffic
Temperature
Driving behavior
Voltage (min, max)
Current (min, max)
Temperature (min, max)
Vehicle speed (avg)
External temperature
Visibility
Precipitation
0.70 km (MAE) (Yokoi et al.
2004)
2.07 km (MAE) (Yokoi et al.
2004)
1.95 km (MAE) (Yokoi et al.
2004)
1.95 km (MAE) (Lee and Wu
2015)
2.18 km (MAE) (Lee and Wu
2015)
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 15 of 19
Boudmenetal. Energy Informatics (2024) 7:80
correlations between various input factors and the intended result. MLR helps in the
analysis of many elements impacting an electric vehicle’s range, allowing for informed
choices on improving driving conditions and overall performance.
In addition to MLR, neural networks (NN) have become more significant in the opti-
mization of electric vehicles. Deep neural networks, in particular, have outstanding
Table 3 Summary of energy consumption predicted by different ML algorithms
Researcher Description ML Algorithm Remarks Refs.
Alvarez et al Anticipate the energy
consumption and
driving patterns of
electric vehicles using
three input parameters,
namely car speed,
acceleration, and jerk
ANN The dataset was only
limited to 10 drivers,
which may not be
sufficient to represent
sample characteristics
Alvarez et al. (2014)
Bi et al Calculates the residual
range of EVs by
utilizing five internal
vehicle-specific fac-tors
Neural network The proposed model
achieved good estima-
tion accuracy, but the
internal influencing
factors were disre-
garded
Bi et al. (2018)
Li et al Predict the energy con-
sumption of electric
buses in Shenzhen,
China
KNN and RF models These models are rela-
tively traditional and
less advanced than
recently developed
ML algorithms such
as LightGBM (Ke et al.
2017) and XGBOOST
(Chen and Guestrin
2016), which have
shown better predic-
tion performance in
different research fields
(Gu et al. 2020; Qu et al.
2019)
Li et al. (2021)
Abdelaty et al Predict electric bus
energy consumption MLR
SVR
Radial basis function
interpolation model
(RBF)
Decision tree model
(DT)
GBDT
Abdelaty et al. (2021)
Chen et al Predict electric vehicle
energy consumption XGBOOST LightGBM outperforms
XGBOOST in terms of
robustness
Chen et al. (2015)
Wang et al detect
Transportation modes
from the GPS trajectory
data automatically
LightGBM Wang et al. (2018)
Table 4 ML algorithms used in EVs
Used ML algorithms
Range optimization MLR
CART
ANN-based models
GBDT
PCR
MLR
SVR
LR
Energy consumption ANN-based models
MLR
SVR
DT
XGBOOST
LightGBM
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 16 of 19
Boudmenetal. Energy Informatics (2024) 7:80
capacity for learning complicated patterns from input. NNs can anticipate battery per-
formance and behavior based on many inputs when used in battery management. NNs
assist optimize battery management tactics by revealing deep correlations, thereby
enhancing battery life and overall efficiency.
Neural networks additionally contribute to optimizing energy consumption. By ana-
lyzing historical energy consumption data, NNs can predict and optimize energy con-
sumption patterns in EVs. By capturing complex relationships between different factors,
NNs enable accurate prediction, facilitating energy efficiency and cost reduction in EVs.
In summary, both multiple linear regression (MLR) and neural networks (NN) are
essential machine learning algorithms for electric vehicle optimization. MLR’s versatility
allows it to identify relationships and optimize range, while NNs excel at learning com-
plex patterns and optimizing battery management and energy consumption. By leverag-
ing the strengths of MLR and NNs, researchers and engineers can harness the power
of data-driven decision-making to improve the performance, efficiency and overall user
experience of electric vehicles, contributing to the advancement and widespread adop-
tion of electric transportation systems.
Conclusion andperspectives
In conclusion, electric vehicles have become an increasingly popular alternative to tra-
ditional gas-powered cars. In this comprehensive review, we examined various types of
EVs, charging methods, and the associated issues and challenges for some types. We also
explored various energy generation systems and energy management strategies that are
used to power and optimize electric vehicles. Additionally, we discussed the application
of machine learning techniques in electric vehicle battery management, range optimiza-
tion, and energy consumption prediction. Overall, the use of machine learning in elec-
tric vehicles has shown promising results in improving their efficiency, performance,
and sustainability. However, there are still several challenges that need to be addressed,
such as battery degradation, data privacy, and ethical considerations in the development
and deployment of machine learning algorithms for electric vehicles. Further study and
invention is needed to overcome these challenges and accelerate the adoption of EVs as
a clean and sustainable transportation solution for the future. We finish by outlining our
perspective on the field that requires further research and development to ensure that
these ML algorithms can provide accurate and reliable results to EVs, and to make an
influence on the optimization and management of EVs.
Author contributions
K.B. and A.E. wrote the main manuscript text and reviewed the paper, Z.E. and Z.A. prepared Figs. 1, 2, 3, 4 and Tables 1, 2,
3, 4 and reviewed the paper, M.R. reviewed and validated the manuscript.
Funding
There is no funding.
Availability of data and materials
No datasets were generated or analysed during the current study.
Declarations
Ethics approval and consent to participate
Not applicable.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 17 of 19
Boudmenetal. Energy Informatics (2024) 7:80
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Received: 24 May 2024 Accepted: 12 August 2024
References
Abdelaty H, Al-Obaidi A, Mohamed M, Farag HE (2021) Machine learning prediction models for battery-electric bus
energy consumption in transit. Transp Res Part Transp Environ 96:102868
Advantages & disadvantages of solar power. https:// clean techn ica. com/ 2013/ 10/ 08/ advan tages- disad vanta ges- solar-
power/. Accessed 17 Apr 2023
Ahmad A, Khan ZA, Saad Alam M, Khateeb S (2018) A review of the electric vehicle charging techniques, standards,
progression and evolution of EV technologies in Germany. Smart Sci 6:36–53. https:// doi. org/ 10. 1080/ 23080 477.
2017. 14201 32
Alvarez AD, Garcia FS, Naranjo JE, Anaya JJ, Jimenez F (2014) Modeling the driving behavior of electric vehicles using
smartphones and neural networks. IEEE Intell Transp Syst Mag 6:44–53
Andrea Calvo E, Mañana Canteli M, Ortiz Fernández A, Renedo Estébanez CJ, Eguíluz Morán LI, Pérez Remesal SF, Delgado
San Román F (2006) A simplified electrical model of small PEM fuel cell. European association for the development
of renewable energies, environment and power quality (EA4EPQ)
Anwar A, Siddique M, Dogan E, Sharif A (2021) The moderating role of renewable and non-renewable energy in
environment-income nexus for ASEAN countries: evidence from Method of Moments Quantile Regression. Renew
Energy 164:956–967
Arias J. Hydrogen and fuel cells in Japan; 2019
Arif SM, Lie TT, Seet BC, Ahsan SM, Khan HA (2020) Plug-in electric bus depot charging with PV and ESS and their impact
on LV feeder. Energies 13:2139
Arif SM, Lie TT, Seet BC, Ayyadi S, Jensen K (2021a) Review of electric vehicle technologies, charging methods, standards
and optimization techniques. Electronics 10:1910
Arif SM, Lie TT, Seet BC, Ayyadi S (2021b) A novel and cost-efficient energy management system for plug-in electric bus
charging depot owners. Electr Power Syst Res 199:107413
Atabani AE, Badruddin IA, Mekhilef S, Silitonga AS (2011) A review on global fuel economy standards, labels and tech-
nologies in the transportation sector. Renew Sustain Energy Rev 15:4586–4610. https:// doi. org/ 10. 1016/j. rser. 2011.
07. 092
Bi J, Wang Y, Shao S, Cheng Y (2018) Residual range estimation for battery electric vehicle based on radial basis function
neural network. Measurement 128:197–203
Birnie DP (2009) Solar-to-vehicle (S2V) systems for powering commuters of the future. J Power Sources 186:539–542.
https:// doi. org/ 10. 1016/j. jpows our. 2008. 09. 118
Borup RL, Mukundan R, More KL, Neyerlin KC, Weber A, Myers DJ, Ahluwalia R (2018) PEM fuel cell catalyst layer (MEA)
architectures. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Borup RL, Kusoglu A, Neyerlin KC, Mukundan R, Ahluwalia RK, Cullen DA, More KL, Weber AZ, Myers DJ (2020) Recent
developments in catalyst-related PEM fuel cell durability. Curr Opin Electrochem 21:192–200. https:// doi. org/ 10.
1016/j. coelec. 2020. 02. 007
Çağatay Bayindir K, Gözüküçük MA, Teke A (2011) A comprehensive overview of hybrid electric vehicle: Powertrain
configurations, powertrain control techniques and electronic control units. Energy Convers Manag 52:1305–1313.
https:// doi. org/ 10. 1016/j. encon man. 2010. 09. 028
Carrilero I, González M, Anseán D, Viera JC, Chacón J, Pereirinha PG (2018) Redesigning European public transport: impact
of new battery technologies in the design of electric bus fleets. Transp Res Procedia 33:195–202
Chen T, Guestrin C (2016) Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD interna-
tional conference on knowledge discovery and data mining. pp 785–794
Chen J-L, Li G-S, Xiao B-B, Wen Z-F, Lv M-Q, Chen C-D, Jiang Y, Wang X-X, Wu S-J (2015) Assessing the transferability of
support vector machine model for estimation of global solar radiation from air temperature. Energy Convers Manag
89:318–329
Chen Y, Wu G, Sun R, Dubey A, Laszka A, Pugliese P (2020) A review and outlook of energy consumption estimation
models for electric vehicles. ArXiv Prepr ArXiv 2003. 12873
Chowdhury S, Tarek MTB, Haque ME, Sozer Y (2023) A three-phase overlapping winding based wireless charging system
for transportation applications. IEEE Trans Power Electron. https:// doi. org/ 10. 1109/ TPEL. 2023. 33094 96
Clegg SJ (1996) A Review of Regenerative Braking Systems. http:// www. its. leeds. ac. uk/. Accessed 17 Apr 2023
Daziano RA (2013) Conditional-logit Bayes estimators for consumer valuation of electric vehicle driving range. Resour
Energy Econ 35:429–450. https:// doi. org/ 10. 1016/j. resen eeco. 2013. 05. 001
Deshmukh MK, Deshmukh SS (2008) Modeling of hybrid renewable energy systems. Renew Sustain Energy Rev
12:235–249. https:// doi. org/ 10. 1016/j. rser. 2006. 07. 011
Dharmakeerthi CH, Mithulananthan N, Saha TK (2014) Impact of electric vehicle fast charging on power system voltage
stability. Int J Electr Power Energy Syst 57:241–249
Enang W, Bannister C (2017) Modelling and control of hybrid electric vehicles (a comprehensive review). Renew Sustain
Energy Rev 74:1210–1239. https:// doi. org/ 10. 1016/j. rser. 2017. 01. 075
Erdinç O, Taşcıkaraoǧlu A, Paterakis NG, Dursun I, Sinim MC, Catalao JP (2017) Comprehensive optimization model for
sizing and siting of DG units, EV charging stations, and energy storage systems. IEEE Trans Smart Grid 9:3871–3882
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 18 of 19
Boudmenetal. Energy Informatics (2024) 7:80
(2024) Ford escape® plug-in hybrid SUV|model details & specs|Ford.com. In: Ford Mot. Co. https:// www. ford. com/ suvs-
cross overs/ escape/ 2024/ models/ escape- plug- in- hybrid/. Accessed 11 Nov 2023
Gschwendtner C, Sinsel SR, Stephan A (2021) Vehicle-to-X (V2X) implementation: an overview of predominate trial con-
figurations and technical, social and regulatory challenges. Renew Sustain Energy Rev 145:110977
Gu X, Han Y, Yu J (2020) A novel lane-changing decision model for autonomous vehicles based on deep autoencoder
network and XGBoost. IEEE Access 8:9846–9863
Habib S, Khan MM, Abbas F, Sang L, Shahid MU, Tang H (2018) A comprehensive study of implemented international
standards, technical challenges, impacts and prospects for electric vehicles. IEEE Access 6:13866–13890
Hannan MA, Azidin FA, Mohamed A (2014) Hybrid electric vehicles and their challenges: a review. Renew Sustain Energy
Rev 29:135–150. https:// doi. org/ 10. 1016/j. rser. 2013. 08. 097
Hannan MA, Hoque MM, Mohamed A, Ayob A (2017) Review of energy storage systems for electric vehicle applications:
issues and challenges. Renew Sustain Energy Rev 69:771–789. https:// doi. org/ 10. 1016/j. rser. 2016. 11. 171
Hertzke P, Müller N, Schenk S, Wu T (2018) The global electric-vehicle market is amped up and on the rise. McKinsey Cent
Futur Mobil 1:1–8
Huang Y, Wang H, Khajepour A, He H, Ji J (2017) Model predictive control power management strategies for HEVs: A
review. J Power Sources 341:91–106. https:// doi. org/ 10. 1016/j. jpows our. 2016. 11. 106
Hui S, Lifu Y, Junqing J, Yanling L (2011) Control strategy of hydraulic/electric synergy system in heavy hybrid vehicles.
Energy Convers Manag 52:668–674. https:// doi. org/ 10. 1016/j. encon man. 2010. 07. 045
Jiang J, Ning X, Xu Y, Wang Q, Liu W, Pan Z, Zhao F (2013) Research on hydraulic regenerative braking system for pure
electric vehicle based on AMESim. In: Proceedings of the FISITA 2012 world automotive congress. Springer, Berlin,
Heidelberg, pp 1161–1170
Kalantar M, Mousavi GSM (2010) Dynamic behavior of a stand-alone hybrid power generation system of wind turbine,
microturbine, solar array and battery storage. Appl Energy 87:3051–3064. https:// doi. org/ 10. 1016/j. apene rgy. 2010.
02. 019
Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu T-Y (2017) LightGBM: a highly efficient gradient boosting decision
tree. Advances in neural information processing systems. Curran Associates, Inc
Koubaa R, Yoldas Y, Goren S, Krichen L, Onen A (2021) Implementation of cost benefit analysis of vehicle to grid coupled
real Micro-Grid by considering battery energy wear: Practical study case. Energy Environ 32:1292–1314
Lazarou S, Vita V, Christodoulou C, Ekonomou L (2018) Calculating operational patterns for electric vehicle charging on a
real distribution network based on renewables’ production. Energies 11:2400
Lee C-H, Wu C-H (2015) A novel big data modeling method for improving driving range estimation of EVs. IEEE Access
3:1980–1993. https:// doi. org/ 10. 1109/ ACCESS. 2015. 24929 23
Li T, Zhang J, Zhang Y, Jiang L, Li B, Yan D, Ma C (2018) An optimal design and analysis of a hybrid power charging sta-
tion for electric vehicles considering uncertainties. In: IECON 2018–44th annual conference of the IEEE industrial
electronics society. IEEE, pp 5147–5152
Li Z, Khajepour A, Song J (2019) A comprehensive review of the key technologies for pure electric vehicles. Energy
182:824–839. https:// doi. org/ 10. 1016/j. energy. 2019. 06. 077
Li P, Zhang Y, Zhang Y, Zhang K (2021) Prediction of electric bus energy consumption with stochastic speed profile gen-
eration modelling and data driven method based on real-world big data. Appl Energy 298:117204
Lo Piano S, Mayumi K (2017) Toward an integrated assessment of the performance of photovoltaic power stations for
electricity generation. Appl Energy 186:167–174. https:// doi. org/ 10. 1016/j. apene rgy. 2016. 05. 102
Macharia VM, Garg VK, Kumar D (2023) A review of electric vehicle technology: architectures, battery technology and its
management system, relevant standards, application of artificial intelligence, cyber security, and interoperability
challenges. IET Electr Syst Transp 13:e12083. https:// doi. org/ 10. 1049/ els2. 12083
Meishner F, Satvat B, Sauer DU (2017) Battery electric buses in european cities: economic comparison of different tech-
nological concepts based on actual demonstrations. In: 2017 IEEE vehicle power and propulsion conference (VPPC).
IEEE, pp 1–6
Mekhilef S, Saidur R, Safari A (2012) Comparative study of different fuel cell technologies. Renew Sustain Energy Rev
16:981–989. https:// doi. org/ 10. 1016/j. rser. 2011. 09. 020
METI Ministry of Economy, Trade and Industry. https:// www. meti. go. jp/ engli sh/. Accessed 17 Apr 2023
National Renewable Energy Laboratory (NREL) (2012) Life cycle greenhouse gas emissions from solar photovoltaics (Fact
Sheet). U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy. https:// doi. org/ 10. 2172/ 10567
45
Negarestani S, Fotuhi-Firuzabad M, Rastegar M, Rajabi-Ghahnavieh A (2016) Optimal sizing of storage system in a fast
charging station for plug-in hybrid electric vehicles. IEEE Trans Transp Electrification 2:443–453
Nissan reveals LEAF e-Plus: 62 kWh battery, 226-Mile Range. In: InsideEVs. https:// insid eevs. com/ news/ 341958/ nissan-
revea ls- leaf-e- plus- 62- kwh- batte ry- 226- mile- range/. Accessed 11 Nov 2023
Nowaková J, Pokorný M (2020) Intelligent controller design by the artificial intelligence methods. Sensors 20:4454.
https:// doi. org/ 10. 3390/ s2016 4454
Ong HC, Mahlia TMI, Masjuki HH (2012) A review on energy pattern and policy for transportation sector in Malaysia.
Renew Sustain Energy Rev 16:532–542. https:// doi. org/ 10. 1016/j. rser. 2011. 08. 019
Pan C, Dai W, Chen L, Chen L, Wang L (2017) Driving range estimation for electric vehicles based on driving condition
identification and forecast. AIP Adv. https:// doi. org/ 10. 1063/1. 49939 45
Patil D, McDonough MK, Miller JM, Fahimi B, Balsara PT (2017) Wireless power transfer for vehicular applications: overview
and challenges. IEEE Trans Transp Electrification 4:3–37
Photovoltaics Report—Fraunhofer ISE. In: Fraunhofer Inst. Sol. Energy Syst. ISE. https:// www. ise. fraun hofer. de/ en/ publi
catio ns/ studi es/ photo volta ics- report. html. Accessed 17 Apr 2023
Qu Y, Lin Z, Li H, Zhang X (2019) Feature recognition of urban road traffic accidents based on GA-XGBoost in the context
of big data. IEEE Access 7:170106–170115
Ramachandran S, Stimming U (2015) Well to wheel analysis of low carbon alternatives for road traffic. Energy Environ Sci
8:3313–3324. https:// doi. org/ 10. 1039/ C5EE0 1512J
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 19 of 19
Boudmenetal. Energy Informatics (2024) 7:80
Rhode S, Van Vaerenbergh S, Pfriem M (2020) Power prediction for electric vehicles using online machine learning. Eng
Appl Artif Intell 87:103278. https:// doi. org/ 10. 1016/j. engap pai. 2019. 103278
Richardson DB (2013) Electric vehicles and the electric grid: a review of modeling approaches, impacts, and renewable
energy integration. Renew Sustain Energy Rev 19:247–254. https:// doi. org/ 10. 1016/j. rser. 2012. 11. 042
Sabri MMF, Danapalasingam KA, Rahmat MF (2016) A review on hybrid electric vehicles architecture and energy manage-
ment strategies. Renew Sustain Energy Rev 53:1433–1442. https:// doi. org/ 10. 1016/j. rser. 2015. 09. 036
Sadeghian O, Oshnoei A, Mohammadi-ivatloo B, Vahidinasab V, Anvari-Moghaddam A (2022) A comprehensive review
on electric vehicles smart charging: solutions, strategies, technologies, and challenges. J Energy Storage 54:105241.
https:// doi. org/ 10. 1016/j. est. 2022. 105241
Salmasi FR (2007) Control strategies for hybrid electric vehicles: evolution, classification, comparison, and future trends.
IEEE Trans Veh Technol 56:2393–2404. https:// doi. org/ 10. 1109/ TVT. 2007. 899933
Salmi T, Bouzguenda M, Gastli A, Masmoudi A (2012) MATLAB/simulink based modeling of photovoltaic cell. Int J Renew
Energy Res 2:213–218
Sanguesa JA, Torres-Sanz V, Garrido P, Martinez FJ, Marquez-Barja JM (2021) A review on electric vehicles: technologies
and challenges. Smart Cities 4:372–404
Sharif A, Baris-Tuzemen O, Uzuner G, Ozturk I, Sinha A (2020) Revisiting the role of renewable and non-renewable energy
consumption on Turkey’s ecological footprint: evidence from Quantile ARDL approach. Sustain Cities Soc 57:102138
Smuts M, Scholtz B, Wesson J (2017) A critical review of factors influencing the remaining driving range of electric
vehicles. In: 2017 1st international conference on next generation computing applications (NextComp). IEEE, pp
196–201
Sukamongkol Y, Chungpaibulpatana S, Ongsakul W (2002) A simulation model for predicting the performance of a solar
photovoltaic system with alternating current loads. Renew Energy 27:237–258. https:// doi. org/ 10. 1016/ S0960-
1481(02) 00002-2
Sun S, Zhang J, Bi J, Wang Y (2019) A machine learning method for predicting driving range of battery electric vehicles. J
Adv Transp 2019:1–14. https:// doi. org/ 10. 1155/ 2019/ 41091 48
Tie SF, Tan CW (2013) A review of energy sources and energy management system in electric vehicles. Renew Sustain
Energy Rev 20:82–102. https:// doi. org/ 10. 1016/j. rser. 2012. 11. 077
(2023) Toyota Mirai|Toyota.com. https:// www. toyota. com/ mirai/. Accessed 11 Nov 2023
Toyota Prius PHV. (2013) 2014 Toyota Prius PHV: to plug in or not to plug in? In: Car guide. https:// www. guide autow eb.
com/ en/ artic les/ 21152/. Accessed 11 Nov 2023
Valente S, Ferreira H (2008) Braking energy regeneration using hydraulic systems. Polytechnic Institute of Porto (IPP),
Portugal
Villalva MG, Gazoli JR, Filho ER (2009) Modeling and circuit-based simulation of photovoltaic arrays. In: 2009 Brazilian
power electronics conference. pp 1244–1254
Vita V, Koumides P (2019) Electric vehicles and distribution networks: analysis on vehicle to grid and renewable energy
sources integration. In: 2019 11th electrical engineering faculty conference (BulEF). IEEE, pp 1–4
Wang B, Wang Y, Qin K, Xia Q (2018) Detecting transportation modes based on LightGBM classifier from GPS trajectory
data. In: 2018 26th international conference on geoinformatics. IEEE, pp 1–7
Wang Y, Ruiz Diaz DF, Chen KS, Wang Z, Adroher XC (2020) Materials, technological status, and fundamentals of PEM fuel
cells—a review. Mater Today 32:178–203. https:// doi. org/ 10. 1016/j. mattod. 2019. 06. 005
Williamson S, Lukic M, Emadi A (2006) Comprehensive drive train efficiency analysis of hybrid electric and fuel cell vehi-
cles based on motor-controller efficiency modeling. IEEE Trans Power Electron 21:730–740. https:// doi. org/ 10. 1109/
TPEL. 2006. 872388
Xu G, Wang S, Li J, Zhao D (2020) Moving towards sustainable purchase behavior: examining the determinants of
consumers’ intentions to adopt electric vehicles. Environ Sci Pollut Res Int 27:22535–22546. https:// doi. org/ 10. 1007/
s11356- 020- 08835-9
Yang Y, Zhang W, Niu L, Jiang J (2015) Coordinated charging strategy for electric taxis in temporal and spatial scale. Ener-
gies 8:1256–1272
Yavasoglu HA, Tetik YE, Gokce K (2019) Implementation of machine learning based real time range estimation method
without destination knowledge for BEVs. Energy 172:1179–1186. https:// doi. org/ 10. 1016/j. energy. 2019. 02. 032
Yokoi Y, Ichikawa S, Doki S, Okuma S, Naitou T, Shiimado T, Miki N (2004) Driving pattern prediction for an energy man-
agement system of hybrid electric vehicles in a specific driving course. In: 30th annual conference of IEEE industrial
electronics society, Vol. 2, 2004. IECON 2004. pp 1727–1732
Yoldaş Y, Önen A, Muyeen SM, Vasilakos AV, Alan I (2017) Enhancing smart grid with microgrids: challenges and opportu-
nities. Renew Sustain Energy Rev 72:205–214
Yu D, Adhikari MP, Guiral A, Fung AS, Mohammadi F, Raahemifar K (2019) The impact of charging battery electric vehicles
on the load profile in the presence of renewable energy. In: 2019 IEEE Canadian conference of electrical and com-
puter engineering (CCECE). IEEE, pp 1–4
Zeiaee M (2016) An intelligent cell-level battery/ultracapacitor hybrid. Thesis
Zhang Y, Wang W, Kobayashi Y, Shirai K (2012) Remaining driving range estimation of electric vehicle. In: 2012 IEEE inter-
national electric vehicle conference. pp 1–7
Zhang P, Yan F, Du C (2015) A comprehensive analysis of energy management strategies for hybrid electric vehicles
based on bibliometrics. Renew Sustain Energy Rev 48:88–104. https:// doi. org/ 10. 1016/j. rser. 2015. 03. 093
Zheng B, He P, Zhao L, Li H (2016) A hybrid machine learning model for range estimation of electric vehicles. pp 1–6
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

1.
2.
3.
4.
5.
6.
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers and authorised users (“Users”), for small-
scale personal, non-commercial use provided that all copyright, trade and service marks and other proprietary notices are maintained. By
accessing, sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of use (“Terms”). For these
purposes, Springer Nature considers academic use (by researchers and students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and conditions, a relevant site licence or a personal
subscription. These Terms will prevail over any conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription
(to the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of the Creative Commons license used will
apply.
We collect and use personal data to provide access to the Springer Nature journal content. We may also use these personal data internally within
ResearchGate and Springer Nature and as agreed share it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not
otherwise disclose your personal data outside the ResearchGate or the Springer Nature group of companies unless we have your permission as
detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial use, it is important to note that Users may
not:
use such content for the purpose of providing other users with access on a regular or large scale basis or as a means to circumvent access
control;
use such content where to do so would be considered a criminal or statutory offence in any jurisdiction, or gives rise to civil liability, or is
otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association unless explicitly agreed to by Springer Nature in
writing;
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a systematic database of Springer Nature journal
content.
In line with the restriction against commercial use, Springer Nature does not permit the creation of a product or service that creates revenue,
royalties, rent or income from our content or its inclusion as part of a paid for service or for other commercial gain. Springer Nature journal
content cannot be used for inter-library loans and librarians may not upload Springer Nature journal content on a large scale into their, or any
other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not obligated to publish any information or
content on this website and may remove it or features or functionality at our sole discretion, at any time with or without notice. Springer Nature
may revoke this licence to you at any time and remove access to any copies of the Springer Nature journal content which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or guarantees to Users, either express or implied
with respect to the Springer nature journal content and all parties disclaim and waive any implied warranties or warranties imposed by law,
including merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published by Springer Nature that may be licensed
from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a regular basis or in any other manner not
expressly permitted by these Terms, please contact Springer Nature at
onlineservice@springernature.com