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Advancements in Battery Technology for Electric Vehicles: A Comprehensive Analysis of Recent Developments

November 2023

Authors:

Saiful Islam

Lamar University

Md Shameem Ahsan

Lamar University

Md Khaledur Rahman

Lamar University

Numerous recent innovations have been attained with the objective of bettering electric vehicles and their components, especially in the domains of energy management, battery design and optimization, and autonomous driving. As a result, the eco-system becomes more efficient and long-lasting, and the technology for electric cars of the future is advanced. Insights into cutting-edge e-mobility research and developments, including electric cars (EVs) and other novel, inventive, and promising technologies, are provided by this study. These developments may be feasible by 2030. Digital twins that are linked to the Internet in Things (Iota) are one example of an appropriate modelling and design strategy covered in this research. Thanks to the concept of the Internet of Things, autonomous vehicles could improve road safety, fuel efficiency, and supply drivers more time for other tasks. Additionally discussed in this article is the technology that allows a vehicle to leave a parking spot, drive along a lengthy roadway, and finally park at its destination. The information gathered for use on real roads is crucial to the advancement of autonomous vehicles. Proposals for intelligent, autonomous vehicles and research needs are also present. The description includes numerous societal problems, one of which is the reason of an accident involving an autonomous car. We quickly go over a smart gadget that can detect unusual driving habits and stop accidents in their tracks. Additionally, every area of study pertaining to electric vehicles is addressed, along with the anticipated difficulties and gaps in understanding in each. This includes areas such as environmental evaluation, market research, power electronics, powertrain engineering, and power battery material sciences.

Charging Duration Level Systems [102]

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Challenges and Future Direction of Modern Intelligent Vehicle Technologies.

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Issue: 02

ISSN ONLINE: 2834-2739

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Texas, USA

Advancements in Battery Technology for Electric Vehicles:

A Comprehensive Analysis of Recent Developments

Md Saiful Islam1, Md Shameem Ahsan2,

Md Khaledur Rahman3, Faysal AminTanvir4

1 Department of Industrial & System Engineering, Lamar University, Beaumont, USA

2 Department of Industrial & System Engineering, Lamar University, Beaumont, USA

3 Department of Electrical Engineering, Lamar University, Beaumont, USA

4 Department of Electrical Engineering, Lamar University, Beaumont, USA

1E-mail: shamimstark@gmail.com

2E-mail: shameem6123@gmail.com (Corresponding Author)

3E-mail: krshoaib2@gmail.com

4E-mail: engr.faysalamin@gmail.com

Received: October 18, 2023

Accepted for publication: November 20, 2023

Published: November 28, 2023

Abstract

Numerous recent innovations have been attained with the objective of bettering electric vehicles and their components,

especially in the domains of energy management, battery design and optimization, and autonomous driving. As a result, the

eco-system becomes more efficient and long-lasting, and the technology for electric cars of the future is advanced. Insights into

cutting-edge e-mobility research and developments, including electric cars (EVs) and other novel, inventive, and promising

technologies, are provided by this study. These developments may be feasible by 2030. Digital twins that are linked to the

Internet in Things (Iota) are one example of an appropriate modelling and design strategy covered in this research. Thanks to

the concept of the Internet of Things, autonomous vehicles could improve road safety, fuel efficiency, and supply drivers more

time for other tasks. Additionally discussed in this article is the technology that allows a vehicle to leave a parking spot, drive

along a lengthy roadway, and finally park at its destination. The information gathered for use on real roads is crucial to the

advancement of autonomous vehicles. Proposals for intelligent, autonomous vehicles and research needs are also present. The

description includes numerous societal problems, one of which is the reason of an accident involving an autonomous car. We

quickly go over a smart gadget that can detect unusual driving habits and stop accidents in their tracks. Additionally, every area

of study pertaining to electric vehicles is addressed, along with the anticipated difficulties and gaps in understanding in each.

This includes areas such as environmental evaluation, market research, power electronics, powertrain engineering, and power

battery material sciences.

Keywords: Electrical Vehicles; AVs that drive themselves; EV parts; e-mobility

1. Introduction

Although the charging infrastructures are still seen as a big

issue. As shown in [10], there has been a tremendous

explosion of renewable energy sources in the power grid, and

the electric cars are undeniably eco-friendly [8, 9]. In addition

to highlighting the most recent state-of-the-art developments

in this area of e-mobility, this review paper will endeavour to

delve further into these concerns [11–13]. By 2020, the

specific energy of the battery might have jumped from about

110 Wh/kg to 275 Wh/kg. As a result, this development

suggests that it might achieve 450 Wh/kg by 2030. Moreover,

the electricity density likewise rose from 300 Wh/L - 560

Wh/L from 2009 to 2021 (in only 10 years). It follows that it

might go up to 1,100 Wh/L in 2030. There is a decrease in the

cost when it comes to batteries. The price of batteries, which

was formerly 1200 EUR/kWh, has dropped to 120 EUR/kWh

and is expected to drop to 50 EUR/kWh soon. Therefore, this

review article also includes the present status of battery

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technology [11–13]. If we go further into the topic of traction

inverter the amount of power, we find that it has climbed to 35

kW/L and is projected to reach 60 kW/L in the next decade

[11]. Incorporating broad band gap methods, researchers have

increased efficiency to 98% while increasing range of driving

by 8%. The extent to which electric vehicles contribute to

environmental sustainability is most affected by the power

generation technology. According to the European Energy

Mix, CO₂ emissions of 2010 were 300 g/kWh. By

implementing renewable energy sources and possibly

decommissioning nuclear power facilities, CO₂ emissions

might be brought down to 200 g/kWh by 2030, if not lower.

Considering the ingesting and outputs of the electric cars used

for electrical power, the CO₂ emissions per automobile will

drop form 66 g/km in 2010 to less than 30 g/km in 2030 [11–

14]. People expect to start using AVs (autonomous vehicles)

by the year 2030. Electric and shared, they are likely to be. In

2010, the SAE brought attention to the fact that several pre-

owned commercial vehicles have been outfitted with level 1

automation. Some of the most recent state-of-the-art vehicle

technologies even have level 4 automation, and the others

have already reached level 3. You can use them for novel

services for mobility right now because they have AI features

built into modern communication networks. Additionally, this

article contains such development. In the modern day, the

Internet of devices allows for the sensing, processing, and

actuation of thousands of devices. In addition to facilitating

quick data sharing, this will also allow for smooth cooperation

[14–16]. Automobile mobility, automation, and smart city

applications all make use of such platforms. Not only can

these platforms detect dangers, but they can also eliminate

them [14–20]. One may observe that drivers are required to

execute a number of tasks while on the road, including

adjusting the accelerator and brake pedals, paying attention to

road signs, and changing lanes and indicators as needed [21].

To function, an autonomous vehicle needs to take its

environment into account [22]. This is accomplished through

the five fundamental processes: perception, planning,

localization, the steering wheel controls system, and system

administration. The localization module is in charge of

making location estimates, while the impression module

constructs a model of what is happening while driving using

data from many sensors. So far, as far as the planning module

is concerned, it is primarily responsible for making decisions

on the EV's manoeuvrability based on safer mapping and

localization. All of this is feasible only based on the perceptual

data. In addition, the vehicle's control system regulates the

accelerator, steering, and braking systems. Consequently, the

process becomes quite involved due to the need to consider all

road elements, including pedestrians, cyclists, other cars, etc.

A crucial component of autonomous electric vehicles,

communication methods module enables the vehicle to handle

such tasks while being driven on roadways. A common term

for this kind of interaction is "vehicle-to-everything" (V2X)

communication,

which includes "vehicle-to-vehicle," "vehicle-to-

infrastructure," "vehicle-to-pedestrian," and "vehicle-to-

network" (V2N) communication, among others [24, 25]. So

far, research has shown that two vehicles may talk with each

other, a phenomenon called vehicle-to-vehicle

communication [25, 26]. By making other vehicles on the road

aware of each other, this allows for a decrease in collisions

and the ability to leave the road at a normal speed and

acceleration [27]. Alternatively, vehicle-to-infrastructure

(V2I) communication enables the vehicle to establish a

connection with roadside infrastructure, thereby

disseminating data extensively [28]. All the necessary data on

safe distances from other vehicles, speed limits, safety,

obstacles and accidental warnings may be found among the

sophisticated services; it also aids with lane tracking [29]. The

concept of "V2P"—vehicle-to-pedestrian information

interchanges using sensors and cognitive technology—is

central to the goal of accident reduction [30–32]. V2N links

the equipment used by drivers to a server that provides

centralized control and data about roads, traffic, and services

[33]. The use of V2X communications along with pre-existing

vehicle-sensing capabilities forms the backbone of intricate

applications that aim to improve traffic flow, passenger

entertainment, factory services, and road safety [34, 35].

Ultimately, data collected from actual touch is what these

algorithms need to succeed when applied in a real-life context

[36]. To monitor the rear vehicles, for example, machine

vision employs image processing [37] and trajectories of the

drivers in a given jurisdiction [38]. Additionally, by analysing

past data, the optimal control parameters can be identified,

which in turn maximize fuel efficiency and save fuel [39].

Utilizing data obtained from in-car sensors to analyses driver

behaviour, regardless of being the transport is not completely

autonomous, reduces the likelihood of intoxicated or drowsy

driving [40]. Research on vehicle-to-extensive X-

communication has focused on network security and

connectivity [41–45]. Different parts of the autonomous car

have also been the subject of reviews. Beginning with the

challenges, uses, and needs for vehicle data, Siegel as well as

others [40] laid out the current state of the skill for linked

vehicles. Half of the issues can be resolved through

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cooperative traffic management and communication amongst

transportation infrastructures, as stated in [46]. While they do

cover methods for signalized intersections, their study is

primarily concerned with non-signalized junctions. In [47],

the comprehensive analysis of autonomous overtaking was

published. Two essential elements of high-speed overtaking,

as shown by the authors, are an accurate knowledge of the

surrounding environment and any adjacent obstacles, and the

dynamics of the vehicles involved. In their evaluation of

localization approaches, Bresson et al. [48] considered

autonomous vehicles that have sensor-based systems

integrated into their bodies and a communication network

(V2V, V2I, or both).

1.1 Cloud-Based Transportation Network Research:

In addition, cloud computing has been the subject of other

scientific contributions [49–58]. Published in peer-reviewed

academic journals, these studies examine cloud-based

vehicular computing and its potential extensions to mobile

cloud computing and transport systems. Cloud apps, their

development, and the communication system architecture are

only a few examples of the other areas of privacy and security-

related subjects that might be found here. There was

discussion of the challenges of vehicle network of clouds in

[50, 51]. In a more fluid context, one can find comparable

conversations about vehicular cloud alternatives, such as

traffic scenarios, services, and apps [52, 53]. This review is

one of several that have focused on a given subject. Network

connectivity was the main emphasis of the paper [40], which

covered a broader subject. There weren't many specifics in the

application. However, as an example if driver monitoring, the

scientist in [40] solely mentioned programmers that might use

data gathered to check drivers, thus reducing the danger to

sluggish drinking. Although we have searched the literature

extensively, we have not been able to locate any research on

the most recent developments in autonomous vehicle

technology. The purpose of including this in our investigation

article is to provide an opinion on whether or not the

aforementioned tasks will be carried out by current

autonomous vehicle technology. Improving reduced

emissions and usage of energy without compromising the

vehicle's performance is achieved through the use of fuzzy

logic, advanced predictive control, and other techniques. To

enhance the vehicle's output characteristics, the EMS is used,

and some of these detail its immediate fashion development

process and calibration settings. Among these factors are

state-of-charge, speed, power-split, and others. Investigating

a thorough method for developing the control infrastructure of

an EMS employing numerous control techniques is the main

objective of this sort of research. Suitably emphasized are the

limitations and challenges associated with EMS advances, as

well as a short proposal and discussion on how future EMS

research might be improved. Finally, an interpretative study

[39] uncovered the relevance and possible consequences of

real-time the EMSs with various control systems. These

solutions are proposed for future transportation that is

expected to be sustainable according to of energy the next,

consumption, and vehicle emissions. Embedded intelligent

systems are crucial to the electrification, autonomy, and

deployment of vehicles. There are a lot of roadblocks that are

preventing electric vehicles from being widely used in the

automobile industry, even though the technology for electric

cars is expected to dominate the engines design in the decades

that follow. There are four main types of these challenges:

customer behaviour, charging infrastructure, vehicle

performance, and government support. Therefore, making

sure these challenges are fully understood is important.

Studying each obstacle and deducing their relative order of

removal is the focus of this essay, which ranks them according

to importance [40].

To kick things off, this article explains the digital-twin

based vehicle propellant system (DTVPS) and how it uses the

latest trend in semiconductor technology—wide band gap

(WBG)—in power converters—to achieve revolutionary

benefits. Those interested in learning more about fast charger

technology as it relates to V2G and V2D communication

systems can do so in the future [59,60]. Further, these cutting-

edge tactics enhance the capabilities of contemporary

autonomous vehicles, and one can see the results of their

general inquiry. Additionally, this manuscript includes a

suggestion for fixing the problems. Consequently, this

research study aims to paint a broad picture of the subject by

reviewing the relevant literature, which encompasses related

fields but refrains from discussing algorithms just yet.

2. Future Electric Vehicle Propulsion Systems

A power converter, battery, electric motor, and fixed

gearbox are the four main components of an electric vehicle's

propulsion system, which makes the concept straightforward

to understand. In addition, a gearbox is unnecessary, and

neither clutch nor oil filters are not required. In addition to

enhancing driving comfort, this also reduces costs [61–64]. In

this part, you may discover data related to the future of electric

vehicle propulsion market trends and also identify new

avenues for research.

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2.1 EV Digital Twin Benefits and Development:

Analytical и simulation models of both full EVs and their

component elements have been developed by automotive

engineers and scientists for a long time. The accuracy and

sophistication of these models have grown throughout the

years. The rise of sensor technologies and robust Iota-like

features has transformed all offline models into digital ones,

granting users lifetime access to features like fault endurance

and recognition, predictive maintenance, and the freedom to

reschedule maintenance. This leads to a decrease in the

expenses associated with the manufacturing process's

intermediate processes, like validating and verifying the

system design. For these reasons, digital twins have been

launched using cutting-edge tactics including cloud

computing, artificial intelligence, and the internet of things

[65, 66]. Figure 1 depicts a complete electric car with all the

necessary parts, including a power conversion device, battery,

and an appropriate number of sensors connected to a motor.

This is to explain the whole concept of a digital twin. In this

digital setting, you may find the simulation platform's

representational model. A very realistic model has been

developed using a multi-physics framework, which facilitates

the exchange of data and information across the digital and

physical domains. The vehicle's designer has the ability to

develop an electronic technique that operates in conjunction

with the physical process, providing a valuable tool for

assessing the model from both static and dynamic

perspectives.

Figure 1. The building of a virtual counterpart is an operational concept.

2.2 Electric vehicle digital twin architecture and tool

features include:

Achieving substantial improvements in the usability,

energy efficiency, and functionality of future EVs: Using

these standards, we may evaluate the vehicle's practicality and

features, such as its price, mileage, predicted range, total travel

time, suitability for long-distance travel, comfort in all

weather conditions, and handling of traffic.

2.3 Analysing stress with multi-physics modelling:

Predicting faults in advance, this analysis helps avert

failures and reduces downtime. Analysis of dependability

using mission profiles for predictive maintenance: The

components critical to the reliability of a battery-powered

electric vehicle's drivetrain can be detected using mission-

profile-oriented expedited lifetime testing. Consequently,

engineers working on the product will have a better chance of

reliably and rapidly inventing new ideas by trying out different

permutations of automotive components and other variables.

In addition, by analysing the data received from the cars'

digital twins, maintenance protocols and routines can be

developed. This helps to reduce inventory stocks by making

sure parts are accessible before they are expected to fail in the

electric vehicle. Future developments will likely centre on the

digital twin's application to control design, powertrain design,

and the reliability of novel, cutting-edge powertrains. Its

design, digital-twin-based control architecture, and reliability

are three important areas that are shared. Improvements in

reliability and efficiency in the next generation of vehicles will

need work in all of these areas.

2.4 Interfaces for Power Electronics

An essential part of every electric vehicle's powertrain is the

power electronic converter [11]. The use of semiconductor-based

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materials as switches in these power converters has been the subject

of extensive research. At the moment, these switches are being

considered using materials derived from silicon (Si) or the silicon

carbide (SiC). As mentioned in [67-70], some of them employ

gallium nitride (GaN). The switching frequency is the sole

restriction or limitation of such switches. The switching frequency

is limited in designs of silicon based IGBT traction inverters, which

is a problem when trying to meet user requirements [71]. In order

to conduct electricity, these materials with a broad band gap require

energy levels of one or two electron volts to move an electron

towards the valence region [67-73]. Figure 2 shows these

characteristics. The rate at which the switch for on-board chargers

(OBCs) based on MOSFETs must be below 100 kHz for OBCs

based on Si [72]. In contrast to conventional Si semiconductors,

WBGs exhibit remarkable properties and cutting-edge material

features such improved thermal conductivity, higher switching

frequencies, lower leakage current, and the ability to operate at

higher voltages. Consequently, the WBG semiconductor, which is

based on high frequencies, offers superior power density and good

efficiency for low voltage applications. The electric powertrain

becomes more efficient and the converter's overall weight decreases

as a result. In addition, operating on high temperature readings is

also possible with these high frequencies, which range from 40 kHz

to 100 kHz for proactive front-end inverters and up to 200 to 500

kHz for OBC systems. It has been noted that the thermal regulation

of GaN-based semiconductor circuits receives significantly less

attention. In order to make predictions based on both parametric

and non-parametric representations, accurate models of GaN-

based electronic power converters are necessary. Furthermore,

semiconductor modules are the faultiest components of power

electronic converters. Their great thermal stress characteristic is

the reason behind this. Dielectric breakdowns, which are

influenced by time, are a common cause of these failures [74].

Because of their low production costs and high activation, WBG-

based power conversion devices are now the best option. Up until

now, no prior research has addressed these reliability activities.

Reliability analyses of converters based on silicon or silicon carbide

have been published in a few research articles; however, there is

scant

literature on power converters based on gallium nitride. Even

though these GaN devices that are in the electric vehicle power

sector allow for greater range efficiency, a major limitation in this

field to overcome is the voltage range. No comprehensive stress test

is accessible with respect to dependability and predictive

maintenance. Integration with electric motors and battery systems

is thus possible in line with these WBG technologies.

Figure 2. Evaluation of gallium nitride (GaN), silicon carbides (SiC), and silicon (Si) [73].

3. Solid Batteries: What Lies Ahead

Literature reviews have shown that lithium batteries are

now the market leader in electric vehicle battery technology

due to their dominating features [75–78]. Electric vehicles'

energy, lifespan of a cycle power output, safety, and, above all

else, driving range is all dictated by this battery. The

performance and total cost of electric vehicles have been

enhanced by numerous distinct scientific breakthroughs in the

composition, manufacture, and chemistry of batteries [79].

3.1 Prior Advancements in Lithium Battery Technology

One common way to classify Li-ion batteries is by the material

used for the cathode [80, 81]. In contrast, LFP (lithium iron

phosphorous) batteries are made using the common steel and

phosphate. These batteries have an extremely long lifespan and

may provide a great deal of power because of the material's strong

olivine structure. The inherent low potential of this technology in

comparison to Li+ and its specific capacitance render it unsuitable

for high-energy uses, which is a major disappointment. While

electric cars commonly use energy-dense technologies like lithium

nickel manganese is allowed cobalt oxide (NMC) and lithium

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nickel cobalt aluminium oxide (NCA), LFP is still a good choice for

power applications like hybrid cars and power equipment, as well

as situations asking a lot of cycles. As a general tendency, both

technologies are shifting away from cobalt and towards nickel.

Reducing reliance on costly cobalt and ensuring a superior energy

density are both achieved by this. Many varieties of NMC are now

accessible for commercial use, and this is made possible by studying

various stoichiometric proportions. When it comes to NMC111, a

NMC532, and NMC622, there is an equal amount of each

component. Figure 3 shows that NCA, NMC-532, and the Nagpur

Municipal Corporation-622 are state-of-the-art cathode materials,

but NMC111 is preferable for higher power workloads because to

its manganese content and reduced nickel content. When it comes

to their practicality for commercial applications, negative

electrodes have numerous limitations. Since 1991, the specific

capacitance of silicon and composite the anode has surpassed them

as a result of their tiny potential relative to Li+. As of 2016, graphite

constituted about 90% of commercial batteries, with amorphous

carbon accounting for just 7% and lithium titan ate oxide for just

2%. The raw material appears to be expensive, and these materials

have a low energy density, yet they can charge batteries rapidly [82].

Thanks to recent advancements in Li-ion battery research and

development, the electrode material on the market today offers a

variety of benefits, as shown in Figure 3. The role of selenium in

this is going to play a pivotal role very soon. Literature reviews have

shown that silicon offers an alternative to graphite as a next-

generation anode material, which results in lower pricing (around

8 to 10% lower). More so, even with reduced levels of graphite

amalgamated into silicon-based batteries, their life cycles are still

Figure 3. Representation of comparing energy density with specific energy.

somewhat limited. Certain instances of this combination have

previously taken place, such as the 5% inclusion in Panasonic

cells that were subsequently used in Tesla X. It is widely

anticipated that current technology will undergo significant

advancements in the next years. One consequence of these

advancements is the expected increase in nickel-based

cathodes, which might reduce the silicon content and directly

contribute to a rise in energy density. Along with the

anticipated technologies built around lithium-sulfur-oxygen

and solid-state batteries, this is something that is anticipated

until 2025. Current market forecasts indicate that the lithium-

ion battery will form the basis of the next wave of modern

technology in the next eight to ten years. The results

demonstrated in [84, 85] lead to the conclusion that cobalt or

nickel inclusion can enhance energy densities both at the cell

and pack levels. Further investigation into these solid-state

electrolytes is necessary to confirm this hypothesis. These

electrolytes have a higher energy density and are thicker.

Unlike other liquid electrolytes, they do not cause combustion

and have little effect on concentration polarization voltage

losses. These have excellent resistance to dendritic

development, which makes them ideal for use as Li-based

anodes [86].

A solid electrolyte's capacity for rapid charging is crucial

to its suitability for usage in electric vehicle applications.

Along these lines, it is important to remember that the Li-

dendrite penetration phenomena cause the battery to short

circuit at a certain current density. Critical current density

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levels as measured by recent parametric methods are even

lower than 0.12 mA/cm2, even though they should be less than

5 mA/cm2 [87, 88]. In addition, charging and discharging

require current densities that can be adjusted. It was just

recently found out that critical the current density is greater

when charging compared to discharging [86].

An important part of getting a long-life cycle and high

specific energy is studying the interfaces, like the electrode

with electrolyte in solid-state batteries. Additionally, electro-

chemical interfacial instability is a common cause of cell

failure. As an example, unlike Li+/Li batteries, solid-state

electrolytes currently have a viability window of up to 6 V.

When the contact between the solid electrolyte and the solid

electrode breaks down, the cell impedance could go up. Some

methods, such liquid-solid hybrid electrolytes, aim to clarify

the instability at the interface [87]. Solid-state batteries are the

primary focus of polymers and composite electrolytes due to

their inherent orientation towards the energy storage sector.

Compared to liquid electrolytes, they offer fewer fire hazards

while simultaneously improving mechanical flexibility,

processing capacity, and scalability. Poly (ethylene oxy)

(PEO) and its metabolites provide intriguing solid-state

battery potential due to their large ionic conductivity ranges.

While organic liquid electrolytes have come a long way, ion

conduction remains a formidable obstacle [89–91]. Like how

anode, cathode, and liquid strips are typically assembled in

normal Li-ion batteries, solid-state battery production follows

a similar pattern. But there are differences in the fabrication of

battery pieces and the procedures used to assemble them.

Electrodes are inserted after electrolytes such as has been

generated, unlike normal Li-ion batteries. To make solid

electrolytes, you need to create very toxic hydrogen supplied

(H2S) (for sulphides like Li6PS5Cl) and slightly high

temperatures (for Li7La3Zr2O12, around 1000 C) [92].

3.2 Solid-State Battery Issues and Possible Fix

In the case of electric vehicles, price per cell or budget per

pack is extremely high, and the same is true for power per cell

or pack. This objective is deeply held, even though the

anticipated degree of security is diminished. Some of the

problems that researchers are currently trying to find solutions

to are as follows:

• The formation of an interfacial resistance is a result of

the weak wetting between the solid electrolyte and

lithium. Inadequate wetting of Li causes solid

electrolytes, particularly ceramic ones, to display

extremely high interfacial resistance. That rules out Li

as a potential component of solid-state batteries. It was

found that solid electrolytes composed of polymers

have lower ionic conductivity and better Li wetting

than their ceramic equivalents. Because of this, the Li

wetting problem can be tackled by using

polymer/ceramic composites as electrolytes [86].

• There are significant issues with dendrite growth and

spread when using Li metal in high-power

applications. The target value of 5 mA/cm2 is far away

from the critical current density for solid-state

batteries [87, 88]. Also, the crucial current density

needs to go down because plating (charging) while

stripping (discharging) are different processes.

Although the precise cause and therapeutic therapies

are yet unknown, much effort has been made to

construct the water molecules as tightly as possible

due to

the substantial constraints on dendrite propagation in

dense microstructures [86]

• Producing, storing, and working with solid

electrolytes that exhibit high ionic conductivity

presents a number of challenges. They require

specialized procedures and oxygen-free settings,

which adds to their high cost. Minimizing production

costs and making solid electrolyte handling easier are

ongoing challenges in this area.

As shown in Figure 4, numerous heats pressing processes are

employed throughout the development of a ceramic-based cell.

Also, the electrode and electrolyte must have a suitable and smooth

relation or contact, so this step is conducted. It has been noted that

bulk type solid-state batteries can create enough retention capacity

[93], and design engineering can readily accomplish this technique

nowadays. Another major limitation of bulk type batteries is their

scalability. As a result, polymer composites can be a good choice for

these products when mass-produced. Not to mention that when

working with high temperatures, Li metal creeps. As a result,

current practices advocate including Li metal into a procedure that

mitigates creep [94, 95].

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Figure 4. Battery production for process parameter optimization [94]

3.3 Power Sources with Built-In Sensors

The performance of batteries undergoes significant

variations as time passes. Dendritic short circuits, which can

cause capacity diminishing and impedance development, are

one of many potentially dangerous material adverse responses

that could be at play here. When batteries are in use, it is

critical to handle and monitor them correctly. A BMS, or

battery management system, is usually employed for this

purpose. The BMS ensures that the current, temperature, and

voltage of each cell remain within their optimal safety ranges.

Battery health shows how well an old battery can retain a

charge compared to a brand new one, and a battery's state of

state (So C) describes the amount of energy stored information

that is specific to a single battery [96]. While direct

examination of these characteristics is impossible,

measurements of electrical current, voltage, and temper allow

for their analysis. At the moment, smart algorithms are used

to measure them precisely and optimize them [97]. The

sensors used in these all-measurement methods keep tabs on

all the relevant metrics that contribute to the overall picture of

battery life. Consequently, there is a growing interest in

implantable sensors that incorporate battery cells. Using this

method, we may gain a better understanding of the chemical

processes occurring inside the cells of parasites, measure

quantities that have never been measured before, and learn

more concerning the physical attributes. The reliability and

security of batteries are both enhanced as a result. When the

suggested electrolyte's composition, as well as its pressure,

strain, enlargement, temperature, and next-generation state

estimation methods are computed, a plethora of alternatives

arise [98]. Researchers have also recently focused on self-

healing batteries. Battery failure occurs due to unwanted

chemical changes within the cell. The basic principle of self-

healing in batteries is to undo these changes so the battery can

function as it did when it was new. A self-healing battery's

primary goals will be to restore the conductivity of damaged

electrodes, regulate the flow of ions within the cell, and

mitigate parasitic side effects. Although self-healing

mechanisms are gaining popularity, the battery technology

industry has been slow to embrace them because of the

challenging chemical environment in which they must

function. Some substrates made of polymers have the ability

to mend themselves; they are called self-healing polymer

substrates or SHPS for short. Their principal objective is to

restore conductivity by fixing any damage to the electrodes

[99]. To prevent the loss of electrical contact between

fragmented active material particles, self-healing polymer

binders are employed, for example, in silicon anodes [100].

Another possible concept is functionalized membranes, which

have the ability to trap unwanted molecules and prevent them

from interacting with other cellular components. But self-

healing electrolytes can remove unwanted depositions since

they include healing agents [90]. A potential future notion is

the contained self-healing molecules. Microcapsules

containing medicinal compounds make them up. When the

right stimulus is applied, the healing chemicals might be

released at the right time. It is important to highlight the

interconnected nature of sensing of self-healing processes.

The first step in using a smart battery is for a BMS to collect

signals from the built-in sensors and process them. When a

problem is detected, the BMS will send a signal to the

actuator, which will then initiate the correct self-healing

process.

This innovative approach will optimize future battery safety,

longevity, user confidence, and reliability. Consider

embedding the sensor with the battery to the same way as an

integrated conductivity alongside temperature (CT) micro-

sensor was used in [91] to measure the conductivity of electric

battery coolant with high precision; this could be an option for

those considering sensor integration with the battery. A sensor

that detects temperature cell is a thin-film titanium resistor,

and the inter-digital microelectrode is used for resistance

detection. The integrated CT sensor's 0.1 S/cm resolution is

quite good for a limit of detection. In addition, sensors feature

an ideal full-scale measurement error and are equipped with a

high-precision signal-collection and processing circuit. The

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data collected by this sensor, once installed, may be simply

transferred to a static IP address through the Iota, where it can

run a number of artificial intelligence algorithms to aid in

predictive maintenance and additional monitoring.

4. Intelligent Two-Way V2G Networks

The efficient and rapid charging of EV batteries is crucial to

their broad adoption. Contemporary electric vehicles typically have

a range of 300–400 km before requiring a recharge. The

widespread availability of charging stations is one of several

challenges.

Table 1: Charging Duration Level Systems [102]

System Level

Charging Duration

Output Nature

Location

Ultra-Fast Charging

It Takes approximately 2 minutes.

Three-phase-Vac: dual conversion of

210–600 AC circuit to DC circuit for

EVs. Typically, output falls between

800 V and 400 kW.

Off Board 3

Phase

DC Fast Chargers

For 100–130 km of range per hour,

charging takes 30 minutes to an hour.

Three-phase Vac: dual conversion from

AC circuit 210-600 to DC circuit with

an output range of 500

Off-Board 3

Phase

Level 2 Chargers

These are domestic chargers that can be

used at home to charge a 16 to 32 km/h

vehicle in 4 to 8 hours.

Vac: 240 (according to US standards;

400 according to EU norms). The power

is between 3.1 and 19.2 kW, and the

output spans from 15 to 80 A.

On-Board

single/3

Phase

Level 1 Chargers

This mechanism relies greatly on the

type of EV model and takes about 7–10

hours to charge for 3–8 kilometres.

Vac: 240 (according to US standards;

400 according to EU norms). The output

is 12–16 A, and the dower is 1.44–1.92

kW.

On-Board

Single

Phase

Another is the need for rapid charging; and a third is the need

to increase power density as well as specific power [101].

Present day usage is characterized by four main types of

charging. According to Table 1 [102], there are several kinds

of chargers.

Level 3 converters often use off-board systems with

sufficient capacity for high-power charging, in contrast to

level 1 and two chargers that always charge batteries on-board

level 1 and two chargers that always charge batteries on-

board. Level 1 and 2 are also known for their slow charging

times, which is why you can find them in public places,

residences, and private settings rather often. Level 3 charging

frameworks, that use DC power based and charge the system

extremely quickly, are common in most retail centres

[101,102]. Level 2 charging methods take nearly 2 hours and

generate about 20 kW of AC power; with this method, an

electric vehicle can go up to 200 km. Not only that, but the

150-kW electricity system may cut down on time by 15

minutes compared to the normal one, allowing one to go 200

kilometres. It takes 7 minutes for the 350-kW charging system

as well [103,104]. Rectifiers using diodes, matrix rectifiers,

and Vienna rectifiers are all examples of three-phase

topologies that are different from front-end inverters [105]. A

diode rectifier is the most fundamental and most efficient tool

for converting power. The output a predetermined voltage is,

nevertheless, impacted by the

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Figure 5. Architecture of multiphase-bidirectional on-board charger system [107].

three-phase input voltage. When considering total harmonic

distortion (THD), it's not a good thing. To address the transient

harmonic distortion (THD) issue, a three phases active front-

end (AFE) capacitor can provide changeable DC output

voltages in addition to improved factor of return and

efficiency in the form of three-phase sine-shaped input current

waveforms. Despite its relative obscurity, the city's rectifier is

quickly gaining popularity. For off-board fast-charging

systems, the AFE boost rectifier is the best three-phase

conversion method developed thus far [103,106]. The use of

power electronic converters that link to the grid has increased

in tandem with the proliferation of battery electric vehicles.

Bidirectional power electronic circuits (PECs) allow electric

vehicles to serve as both peak power generators and short-term

energy storage devices (V2G, or vehicle-to-grid; G2V). In

current PEC topologies, active switches are used to manage

the bidirectional flow of power instead of diodes. Figure 5

shows the schematic of the multiphase bidirectional on-board

charger system.

4.1. Wireless vehicle and vehicle-to-vehicle charging:

Considerations such as high efficiency, low system size,

and weight, high reliability, devoid of distortion operation,

reduced grid interference, and a few other important metrics

should be considered before settling on an off-board charger

for a power electronic converter. The appropriate switching

frequency, dictated by low gate charge and output capacitance,

is essential in this field, and wide band gap technologies are

making significant contributions towards these goals while

remaining lightweight, portable, and effective. The power

transistors based on GaN made this possible. With the

development of WBG technology, passive devices, including

inductors, caps, and transformers, were also reduced in size

and weight [107,108]. Noted as high electron mobility

electronics (HEMT), GaN-based transistors are abbreviated as

GaN-HEMT. These transistors can withstand voltages up to

660 V and currents ranging from 20-50 A [104,107]. These

parts are commonly found in charging stations off-board

(OBCs) with a power output of 3.0 kW to 20 kW. Two single-

phase bidirectional portable chargers with a totem pole PFC

AC-to-DC structure and a unique DC-to-DC stage structure

are shown in Figure 6. The galvanic nature-based isolator and

reverse power transformation make it possible for the dual

active bridge to work, detecting zero voltage and switching on

both the primary and secondary sides. As mentioned in [109],

this has components that are compact and operate at a set

frequency.

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Figure 6. Topologies for bidirectional OBC systems based on GaN switches [107].

Because of the large load-power variation, reaching ZVS full range

is challenging. Due to zero-voltage switching in the main bridge

and zero-current switching on the secondary side, the resonant

continuous CLLC architecture (if C is inductance and L is

capacitance) seen in Figure 7b is exceedingly efficient. An issue

with the CLLC design is that it can't modify the charging output

voltage by adjusting the series resonant frequency. Reference [70]

suggests replacing modulation of frequencies in the DC-DC phase

with DC bus voltage regulation in the PFC level to address this

issue. That way, the resonance of the CLLC stage can perform to

its full potential [109,110]. Modular converter technology can

replace the creation of ultra-fast batteries.

As a 600-go DC ultra-fast charger, the concept calls for four

parallel AFE converters to be linked [111]. You can see this in

Figure 7. With each 150-kW module, we compared semiconductors

based on silicon and silicon carbide. The non-linear electro-

thermal modelling framework was utilized to investigate the

efficacy of Si (SKM400GB12T4) or SiC (CAS300M12BM2)

circuits across various power levels. All relevant datasheet

information is present in the calculations for both cases. The vastly

superior charging performance of SiC devices compared to silicon-

based ones is seen in Figure 8. Due to Si's higher loss than SiC, a

wide gap in the band devices can conserve energy in this manner.

Figure 7. (a, b) Modular 600 kW DC ultra-fast charger [111].

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Figure 8. Si- and SiC-based high-power off-board charging system efficiency map.

5. Energy and Transportation Transition to Climate

Neutral

In order to establish energy-sustainable communities, it is

imperative to incorporate renewable energy sources such as

hydro, wind, and solar power. When considering the life cycle

viewpoint, it is evident that renewable energy sources exert far

less impact on climate change compared to conventional

energy sources such as coal, oil, or natural gas. This holds true

even when accounting for the intermittent nature of

renewables within a fully dynamic energy system [112]. The

proliferation of DPGs linked to the electricity grid has

exacerbated reliability, operational safety, and islanding

prevention issues. To meet grid connectivity regulations,

better control of distributed generating systems is required

[113]. In Belgium, for example, switching to electric vehicles

would only lead to a 20% spike in electricity use [114]. The

utilization of renewable energy sources is on the rise. When

neither the wind nor the sun is present, what then? Either we

need to put more money into energy storage or depend more

on alternative energy sources. The dimensions of the battery

exert a substantial influence on the operational efficiency of

electric vehicles. Vehicle batteries can store surplus power

from renewable energy sources like solar or wind. For this, the

term "smart charge management" is used. Releasing stored

energy into the grid is an option during peak power demand.

This is known as vehicle-to-grid (V2G) in the technical world.

According to an exhaustive cycle test, the effects of ageing

on the battery were unaffected by using the V2H will power a

dwelling. Due to the vastly different discharge currents

required to power a house and accelerate an automobile, the

V2G characteristics do not impact battery ageing [115]. A

battery's many useful applications in a local energy

community (LEC) make it possible to store energy when it's

cheap and release it when it's expensive on the wholesale

market. One service that can be provided is capacity credit,

which can put off or lessen the necessity of improvements in

infrastructure in the manufacturing, transmission, and

distribution sectors. In microgrids, batteries placed behind the

metro can boost PV self-consumption, which in turn reduces

energy costs and helps with backup power. The electricity

system is anticipated to change as energy industries and

decentralized production grow more prevalent. Electric fleet

omnidirectional charging

systems are an essential component of energy management for

these systems. They provide adaptable services, help with self-

consumption, and keep grid congestion at bay. The techno-

economic analysis of a vehicle-to-grid case study is available

in reference [116].

On the other hand, bidirectional power transmission is essential

for chargers and automobiles to work together. This brings up the

previously unsolved matter of contacting the local grid operator.

The first insight is that smart grid integration of EVs can take

advantage of value streams related to grid balancing [117].

Building a real laboratory to conduct this study is, thus, of the

utmost importance. Several prerequisites and principles are laid out

in reference to incorporating the V2G into a local energy system

[118]. Using the electricity mix in Europe, we find that electric

vehicles produce twice as little atmospheric carbon dioxide (CO2)

across their lifetimes as petrol or diesel engines. Using Belgium's

electrical mix as an example, this might be four times lower. More

than a tenfold reduction in oxygen and carbon dioxide pollutants

might be possible if vehicles were powered by renewable energy

sources [8, 102, 119]. The results for each vehicle's potential to

contribute to warming temperatures or global warming are shown

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in Figure 9. The BEV that uses Belgium's power mix gets the worst

overall score regarding climate change. Besides human toxicity, the

BEV excels in most other mid-range categories compared to

traditional petrol and diesel vehicles. The production of auxiliary

components like batteries, motors, electronics, etc. significantly

influences human toxicity. On the other hand, the BEV

outperforms all other vehicles in the examined impact categories

when analysing the well-to-wheel (WTW) timing, which is suitable

for the Belgian restrictions (and metropolitan environment).

Figure 9. The findings of the lifespan evaluation (LCA) related to global warming [8].

Accordingly, a range based LCA approach that accounts for

the market diversity of each technology is proposed in

reference [120]. Figure 10 shows that when a comprehensive

single score level is used to evaluate the BEV, the results are

the best.

Figure 10. Results of single-score LCA [8]

6. Autonomous Electric Vehicles (AEVs)

As the energy and transportation industries become more

electrified, these industries are also moving towards increased

automation. The automobile industry and other technical

sectors are allocating increasing resources to study and build

highly automated electric vehicles. In order to bring about

greater advantages in terms of reducing expenses, safety, level

of service, and, most importantly, environmental benefits, it is

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essential that an electric car be autonomous [121, 122].

Moving from EVs to AEVs allows AVs and EV synergies to

be utilized. A new era of data-driven algorithms, AI, strong

sensor technology, and intelligent communication is required

to bring about this change. Resolving energy demand and fleet

management issues might further optimize the transportation

infrastructure and its incorporation into the electricity grid

while reducing its environmental effects [123]. A smooth

integration can only be provided with reliable and fast

communication protocols.

6.1 AEVs' Use of Wireless Technology

Autonomous vehicles rely on two types of communication: vehicle-

to-vehicle (V2V) and vehicle-to-infrastructure (V2I). V2V refers to

the communication between individual cars, while V2I refers to the

communication between vehicles and any infrastructure. In

addition, it includes vehicle-to-home (V2H) communication and

vehicle-to-people (V2P) communication. That stands for "vehicle-

to-network," or V2N. Figure 1 depicts each of these methods. 11.

Figure 11. Protocol illustrations for vehicle-to-everything (V2E) systems.

There are many options for making this connection, and they

all have advantages and disadvantages. Popular wireless

communication standards include Bluetooth, 5G, and Wi-Fi.

Remember that there may be times when these

electromagnetic technologies do not provide sufficient speed

for V2V channels and V2I communication, even if it is

possible on occasion. A few examples are indoor and

underground locations like tunnels and parking lots and rural

and urban places with spotty coverage or high levels of

electromagnetic interference. Light fidelity (lithium-ion) is an

alternative to radio wave transmission, which transfers data

through visible and infrared light. Professor Herald Haas first

used the term "Li-Fi" in 2011 [123]. He showed how data

could have been sent to a photo receiver using the brightness

from an inexpensive LED desk lamp. It is possible to

accomplish this by altering the light output of currently in use

lighting systems, such as streetlights, automobile headlights,

and so forth. Adequate photo receivers allow for establishing

bidirectional or unidirectional communication links with

bandwidths that can provide data rates up to one hundred times

higher than Wi-Fi [123]. A technological execution of Li-Fi is

shown in Figure 12. A transmitter of a solid-state light source,

like a laser diode or an LED, can adjust its output brightness

with an electrical driver by regulating the current flow. The

widespread usage of solid-

state lighting in infrastructure (such as road lights, traffic

signals, vehicle headlights, and taillights) makes Li-Fi an easy

technology to implement. A new infrastructure must be built

compared to comparable systems that use conventional radio

frequency (RF) transmission (DSRC, for example). The

simplicity of a Li-Fi transmitter makes it possible to upgrade

existing lighting systems to use them. In the future, it can

facilitate communication between vehicles and other

commuters by providing a central location to retrieve relevant

data. A high number

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Figure 12. Application of technology for a Li-Fi downlink channel.

of easily accessible access points and a low installation cost

are the outcomes of this. The existing road lighting

infrastructure, which is currently "dumb," can potentially

become "smart" with very little work. Yet, as said before,

putting it into practice is still challenging. On the other hand,

the implementation costs are lower than those of alternatives.

6.2 Shared Electric Autonomous Transportation

(SEAVs)

Shared autonomous vehicles (SAVs) are getting a lot of

buzz because they could be better, cheaper, and more

convenient than the current options for car- and ridesharing

[124]. Furthermore, SEAVs, in their electric form, could be

environmentally preferable to conventional gas-powered

vehicles while still being competitive in terms of cost. People

see them as a potential smart mobility component because of

this [124]. There are several challenges associated with SEAV

use. In order to create viable business models, it is crucial to

estimate passenger demand and ascertain passengers' desire to

utilize and pay for the procedure [124]. From a mobility

standpoint, transport demand must match vehicle availability.

SEAVSs can improve mobility, especially for those older or

with limited movement [125-127]. This is where the digital

gap comes into play, which is worrisome since it means that

individuals who are less adept at technology and unable to

embrace new technology are socially ostracized. Because

AEVs are electric, fleet managers must consider charging

needs, driving range, and passenger service. It is crucial to

consider the charging stations' expected number, placement,

and power levels for setting SEAV fleets [123]. Since most

studies on SEAVs that think charging aspects [128,129] have

focused on spatial distribution or just rule-based introductions,

we still need to learn how to fully assess a location's suitability

or how grid constraints and impacts play a role.

The rapid use of electric vehicles heightens concerns over

the reliability and power availability of the electrical grid.

Electric cars can improve bidirectional charging (vehicle-to-

grid) and provide some ancillary services, which could help to

balance the electrical grid. On the other hand, research has

shown that EVs only modestly increase electricity demand

[130]. Furthermore, as mentioned earlier in the chapter, EVs

can balance out the intermittent nature of RES, which speeds

up their deployment. The highly controllable and coordinated

SEAV fleets show promise in this area [131]. Studies currently

emphasize the possibilities of SEAV fleets (natural, economic,

and service-related) because of their electric and autonomous

characteristics, which allow optimized fleet behavior.

Nevertheless, it presents a difficult problem for fleet

management that calls for additional study and the

development of important enabling technologies, such as

energy demand and mobility.

7. Autonomous Electric Cars and Driving

This section extensively reviews the supporting

technologies that will allow autonomous vehicles to operate,

including ADAS and the concept of self-driving electric

vehicles (EVs). After that, it finds gaps in the current literature

and suggests solutions to those problems.

7.1. ADASs or sophisticated driver assist systems.

ADAS (advanced driver assistance system) technology

overview is provided before completely autonomous driving

is covered. Accessible driver assistance systems (ADASs) can

aid in monitoring, braking, and various alerting tasks,

contributing to better road safety. It is possible to use an

ADAS to help with parking or keep an eye on things. Along

with ADAS, other linked technologies, such as streetlights and

traffic data, can make roadways safer for everyone. As

ADASs work to improve and gain additional benefits in the

coming years, governments may require vehicle installation.

The advanced driver assistance systems (ADASs) discussed

here are not driverless cars but technologies that help drivers.

Driver assistance systems nowadays are progressively getting

more advanced in terms of technology. Adaptive cruise

control, parking assistance, frontal collision warnings, lane

departure warnings, and driver fatigue recognition are the

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main features that most systems aim to offer [132]. A wide

variety of ADAS, or advanced driver assistance systems, is

currently on the market and might greatly enhance the

convenience and security of driving. Assuming they are

bought and used, such in-vehicle technologies could greatly

benefit older drivers due to age-specific performance

limitations.

On the other hand, ADASs are more well-known than they

are used, according to a number of market research polls. The

disparity in knowledge of and interest in ADAS was

investigated in a survey of 32 seniors using semi-structured

interviews. Knowledge, experiences, and barriers to utilizing

ADASs among the elderly have been the subject of several

studies, such as [132]. Parking assistance systems are

designed to make backward parking a safe and pleasant

experience.

The driver can prevent a collision when reversing because

of a reference showing them the direction in which the

automobile travels. The purpose of forward collision-

avoidance technologies is to alert drivers visually and aurally

when they approach the vehicle ahead of them too closely

[133]. To determine if a collision is imminent, these systems

frequently measure the gap between the two cars and monitor

their and the preceding vehicle's speeds [134]. Various sensors

can be employed, including Liar, GPS, radar systems, and

vision-based ones [135,136]. Typical reasons for driving

unconventionally include being drunk, being irresponsible, or

being extremely tired [136-144]. These things can alter a

driver's demeanor or physical movement. When drivers are

tired, they may blink quickly and continuously, nod or swivel

their heads, and yawn often [145]. Contrarily, a drunk driver

is likely to develop the behavior of consistently responding

slowly and with abrupt acceleration or deceleration. Driving

carelessly is comparable to driving under the influence to

some extent. The driver could be fully aware of the road

conditions, but their emotions could be causing them to speed

or brake unexpectedly, potentially exceeding the posted limit

[145]. Therefore, it is possible to install driver surveillance

equipment by keeping an eye on the driver in some way,

shape, or form. Direct passenger monitoring systems use

several sensors to record the driver's vital signs and movement

patterns. As part of passive driver monitoring, we examine the

driver's pedal and steering moves and how they react to certain

occurrences [146,147]. A warning system will be set into

motion upon detecting such unusual behavior.

7.2 Limitations of Driverless Vehicles

There has been a lot of study on autonomous vehicles, but

some ground still needs to be explored. This is the first time

anyone has discussed an unexpected obstacle in the literature

while discussing the autonomous vehicle's parking trajectory.

The parking lot is dangerous since a toddler, or an adult could

run into it while grabbing something. The driver is aided by

installing a rear camera and a device that can identify obstacles

in the rear. On the other hand, the driver might not look that

way, or the sensors might not go off. An autonomous vehicle

should stop if this unexpected obstacle occurs while

completing the parking trajectory. For example, the

autonomous vehicle shouldn't stop if it sees a balloon as a

barrier; it should keep parked. Recent studies have addressed

systems that aim to avoid obstacles [148,149]. To account for

unanticipated obstacles like a deer running the road, Funk and

associates [149] proposed an additional component. The

question of how autonomous vehicles should react when an

object falls off a car has yet to be discussed in any of the

investigations. If a big truck carrying construction iron rods

were to have any of the rods come off and smash through the

truck window, it might cause catastrophic injuries or perhaps

death. The literature does not specifically state that drivers

must yield to emergency vehicles, but it is important to note

that such cars have some priority at intersections [150].

Electric vehicles that can drive themselves in the future must

use a combination of their sensors and those of other cars.

Implementing V2V is to raise environmental consciousness by

exchanging measurement data. The integration of ADASs and

sophisticated lighting infrastructures can be achieved through

the use of inexpensive GNSS receivers [131,142], devices that

monitor traffic using radar cameras [144], micro-scale traffic

data [123-146], and other networks.

While a driverless car certainly has numerous advantages,

it also has the potential to bring about several societal

problems. The question of who should pay in the event of an

accident is serious; manufacturers or insurance companies

should shoulder this burden [107,108]. Some have argued that

if we treat self-driving and human drivers similarly, we can

ensure that they will only be held accountable for actions

linked to carelessness (as stated in [148]). It's simpler to say

than to do: a car should have the same rights as a person.

Vehicles ought to be subject to tort law in the same way that

canines are [119]. This is similar to the dog law. There is a lot

of ground to cover before autonomous vehicles can be entered

into practice, as the writers should have mentioned how the

regulation could be applied to them. Because implementing

such a system shouldn't jeopardize road safety, it was stated in

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[139] that producers should have control over their designs

and that items should undergo thorough testing before

distribution. Before the public and manufacturers can embrace

autonomous vehicles, it is evident that the laws governing

them need to be improved [120-123].

8. Challenges and Opportunities

Here, we look at the most current research roadblocks for

connected, autonomous, and intelligent electric car technologies.

The following information is given: [134-143]. Because they do

away with impairments like alcohol, attention, fatigue, and slow

decision-making, autonomous vehicles improve drivers' decision-

making abilities. The ability of these technologies to surpass human

decision-

making abilities while driving is largely due to these factors [149].

Consequently, autonomous vehicles with AI present significant

challenges in real-time responses and mistake prevention. The

importance of safety and performance metrics for autonomous cars

has been the subject of lots of studies. These metrics should account

for hardware failures, programming mistakes, unexpected

occurrences and entities, cyber-attack probabilities, and threats.

Creating and analysing these indicators in real-time will be of

utmost importance going forward. Appeared in Table 2 is the

comparative assessment of autonomous driving systems.

Table 2. Challenges and Future Direction of Modern Intelligent Vehicle Technologies.

Key Findings

Challenges and Features

Year

This paper discussed the importance of deep

learning in autonomous driving. Here, a

number of problems with autonomous

vehicles are looked at, and deep learning and

artificial intelligence are used to propose

answers.

This work can broaden the understanding of deep

learning's role and how it integrates with other

autonomous driving assistance systems. It

incorporates components of contemporary

infrastructure, like cloud, blockchain, and Internet of

Things technologies [134].

2020

A taxonomy for self-driving cars was created

as a result of this study's investigation and

classification of automated driving as it stands

today. This work also produced an idea for a

hybrid architecture that combines computer

and human intelligence. The car's design

served as an overview of autonomous driving.

A taxonomy of autonomous driving systems,

akin to self-driving cars, was developed as a

result of this study. Information integrity and

machine-human interaction were given more

importance than driver replacement alone.

To this endeavour, discourse and safety criteria can be

added. Blockchain technology may be utilized to

solve data security and privacy problems, and the

suggested hybrid architecture includes a safety

monitoring system that can be expanded with other

cutting-edge tools like drones and cloud computing

sets. State-of-the-art networks like SG networks can

be used to study further performance issues.

2020

The use of drones in autonomous systems is

the main topic of this article. Furthermore,

discussed are the anti-collision strategies for

drone mobility and traffic surveillance. The

number of drones and on-road cars is changed

in order to analyse the data.

Applications of this technique include real-time

autonomous system deployment and monitoring. But

still. A detailed investigation into the interaction

between drones and autonomous vehicles is necessary

[136].

2021

This paper presents a blockchain-based

architecture supporting network and

autonomous vehicles' safety and security.

The main technological components and their

connections to driving, systems, and autonomous cars

are briefly reviewed in this article. By delving further

into technical matters, this work can be further

expanded [137].

2020

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Control systems, driving components of the system, interactions across vehicle-to-everything groups, and risk

assessment and survey programmers are only some of the many domains that cyber-attacks can infiltrate. The

primary forms of threats that require investigation and examination are assaults on sensors, assaults on mobile

applications and vehicle information systems, attacks on Iota infrastructure, brutal assaults, and side-channel

attacks. In addition, cyber security uses AI to detect attacks. Autonomous architecture is another interesting aspect.

Important architectural subsystems to study include autonomous systems that incorporate sensors and actuators,

control mechanisms, the monitored vehicle environment, external control variables, visibility, speed, and object

recognition. The proliferation of driverless cars will drive up the cost of communication. Implicitly lowering

performance or increasing communication fault causes packet loss or delay. Autonomous vehicles and their

widespread use are critical to human survival. One shortcoming of earlier attempts was the need for

comprehensive studies examining emerging developments, like the application of deep learning and the Internet

of Things.

Additionally, it is essential to address intelligent software and tools, which still need to be discussed in the existing

literature. Effective simulation also needs improvement. Improving object recognition, navigation, sensors, etc.,

and cloud computing are all necessary to build self-driving automobiles. Autonomous vehicles can use predictive

models to determine their routes and how to control their mobility. There has to be a more sophisticated AI-based

approach for AVs. Care for every part of the real-time architecture is essential. Scene recognition, for example,

requires object tracking and object detection [139]. There must be a comprehensive representation of current AV

designs [140]. The design of the AVs should handle system failures and scalability management. Autonomous

vehicles (AVs) need real-time architecture to perceive their surroundings and communicate with other cars in real-

time. Automated systems can achieve this. For AVs to be accurate, their infrastructure and devices—the principal

agents—must work together [141]. The SAE uses a scale from 0 (no automation) to 5 (full performance) to classify

automation levels.

Companies and academics are putting in much time and effort to attain level 5 [81]. The following classes of

components are required for design following SAEJ 3016:

• Vehicle control is the primary focus in the operational class.

• In addition to route planning, object detection, and tracking are covered in the tactical class, which is the second

level.

• At number three, the strategic class is where one might think about trip planning, which is undoubtedly crucial.

A lot of work has gone into designing, developing, validating, and monitoring AVs in real-time with the help

of AI. Perception, route planning, and driving decision-making are all areas where AI shines. AVs use AI for the

following purposes:

• The paths that autonomous vehicles take are determined by a prediction algorithm.

• Many sensors provide AVs with real-time data, which they intelligently employ.

• Autonomous cars look to their historical data when deciding on a speed and course.

The future of autonomous vehicles is uncertain due to the possibility of a deliberate assault on the machine

learning system that disrupts its functioning. One example of such an attack is the practice of covering stop signs

with stickers to make them harder to see. These changes could trigger AI to mistakenly identify things, leading

the autonomous car to act in a way that endangers people. Therefore, it is necessary to investigate RFID or Iota-

based AI solutions to these problems. Many believe that autonomous vehicles would significantly alter the way

we live. It is the responsibility of lawmakers to design laws that improve the social and economic fabric of the

nation. The possibility that an AV could become a "killer app" with far-reaching effects has been the subject of

research. Though they are still in the early stages of research, AVs will already have far-reaching consequences.

As a result, research into the safety measures should precede their implementation in actual settings.

Autonomous vehicles can navigate their environments with the help of deep neural networks (DNNs). Because of

their shared reliance on trial-and-error learning, human brains and DNNs are quite similar. Regarding autonomous

driving, the exact number of DNNs needed is not determined by any concrete criterion. So, a thorough

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investigation is required down the road. An independent driving system takes millions of connections between

cars, humans, and other devices to work in a real-world road setting. A high-end, potentially expensive

infrastructure is required to manage such a vast infrastructure. Therefore, research into how AI can make the most

of the infrastructure to facilitate seamless autonomous experiences is necessary. Improved route selection and

object identification capabilities for autonomous cars should be realized by developing smarter tools and software

in the future. Since real-time choices are made, data exchange should be faster [142-144]. Autonomous systems

rely on machine learning algorithms to monitor machine behaviour and foresee potential issues. The solution

improves operating efficiency, prolongs asset life, and decreases unscheduled downtime expenses. It is essential

to find the most effective algorithmic learning algorithms and methods for keeping tabs on a machine or its

operations. In the future, we can investigate this task more. Retinopathies, which include glaucoma, hypertension,

diabetes, and others, may be preventable with early vascular detection using fundus imaging [125-127]. This

research aims to find a better way to leverage both traditional template-matching methods and cutting-edge deep

learning techniques for optimal performance. Train your convolutional neural network to detect vessels and

backgrounds in photos using a U-shaped, fully connected network (Unit). Exploring additional cutting-edge

technologies like quantum and blockchain for AV mobile computer networks is possible [122-125]. Autonomous

cars communicate data via a wireless sensor network [146–148].

9. Conclusions

This document presents all of the most recent findings in electric vehicle innovation and technology. This is

on top of the little research on the various batteries and their levels. Also covered are safety concerns and how the

present market complements shared autonomous electric vehicles. Because of this, we have concluded that current

advanced driving systems for assistance require quick improvement; this review study also addresses this element.

The most recent advances in battery technology and theories on the evolution of solid-state batteries and their

interactions with other systems have been discussed. Integrating embedded sensors into the cell and developing

self-healing batteries are two examples of how this state-of-the-art technology improves battery safety and

dependability. Electric vehicles (EVs) can benefit from digital twins (DTs) in some ways, including cost-

effectiveness and more reliable powertrain design. Powertrains that are novel, functional, and affordably priced

are thus provided with new directions and trends.

Regarding fully autonomous vehicles, drivers won't have to worry about that complicated task. This means

less traffic, less gas consumption, and no accidents while parking. The complete implementation of autonomous

vehicles is a precondition for the ideas presented in the literature, which may take some time to materialize. How

an autonomous automobile should respond to a negligent motorist is still in the air. Examples of careless driving

include Following too closely, going too fast for conditions, Not using turn signals, continuing through stop signs

without halting, Not yielding the right of way. Researchers have also focused on interactions between four-wheel

drives and have ignored interactions between motorcycles and autonomous vehicles. When it comes to

transportation, it's not easy to figure out how an auto should handle a situation when motorcyclists are at high risk

of death. The capacity to analyse driving behaviour is now a feature of modern technologies, which can aid in

preventing unusual driving patterns. When an electric vehicle exhibits abnormal behaviour, the devices can control

its lateral movement. With the successful demonstration of neural-network-based autonomous driving, NVIDIA

has set a new standard for independent driving software. Autonomous transverse control is the biggest challenge

for autonomous vehicles. An end-to-end model is quite promising in providing a complete software stack for

automated driving. Despite not being ready for market availability, this technology is a major step towards

developing self-driving cars. Implementing an end-to-end framework is the main subject of the work presented in

the article. The complexities of creating a successful end-to-end model are emphasized in an effort to shed light

on deep-taking classes and the software required for training neural networks. In a multilane track, such as the

one used for training in the current research, the model demonstrated an autonomy of 96.62%. With an accuracy

rate of 89.02%, the model guided the vehicle safely along unexplored, single-lane tracks. Autonomous driving in

unknown and unfamiliar surroundings is now within reach, thanks to the combination of AI with end-to-end

learning and behavioural cloning.

 
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One of the many appealing aspects of electric vehicles is the way their carbon footprints and power systems that 
use renewable  energy  sources work  together.  The possibility of reducing  CO₂  emissions  from  electric  vehicle 
charging is being studied in the context of coordinated charging. This could involve charging solely if the grid's 
carbon intensity (gCO2/kWh) is little and absorption of excess wind generation when it would not otherwise be 
curtailed. A method for scheduling charge events that aims for the lowest intensity of carbon of charging while 
respecting Av and network limits is presented as a time-coupled linearized DNA optimal power flow formulation. 
This  method  is  based  on  plugging-in  periods  produced  from  a  large  travel  dataset.  The  effectiveness  of 
autonomous vehicles  has also been greatly enhanced with the development of artificial intelligence, which is 
another argument. Therefore, this manuscript also includes an outline of independent automobiles. Sensors are 
essential for an autonomous car to collect and transmit data. With this data, we can build an improved structure 
for lane preserving, lane changing, and obstacle detection. 
     Nevertheless, different sensors do have their limitations. Despite the potential cost savings, image processing 
techniques are vulnerable to environmental and climatic variables. Consequently, further research is needed to 
improve the  accuracy of  cheap sensors  or reduce  the  price of  high-reliability sensors  so they  can be  mass-
produced. 
 
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Integrating Automation and Big Data in Lithium-Ion Battery Manufacturing: A Case Study of the Ultium Cells Joint Venture

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Oct 2024

Joonghyun Song

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Jul 2024

Freeman Munisi Mateko

Digital technology has proven helpful in enhancing economic growth and development in several countries globally. This article examines the root causes of low levels of digital technology usage in Zimbabwe and explores the opportunities it could tap into using digital technologies. Using qualitative research techniques, published literature from online repositories was reviewed. It emerges that the leading causes of Zimbabwe’s low digital technological advancement are macroeconomic factors, poor infrastructure, irregular power supply in urban and rural areas, political upheaval, which affects the country’s ability to attract investment, a lack of ICT-skilled experts, and poor institutional frameworks, regulatory challenges, high internet costs, information asymmetry, and complex tax regime structures. Leveraging digital technologies in the primary, secondary, and tertiary sectors will boost Zimbabwe’s economic growth. To benefit, it must develop and amend ICT policies, mobilize financial and non-financial resources, and form strategic alliances with technologically developed economies.

Procedural Insights into Cross-Cultural Management Research: A Model for Cultural Ecology

Article

Full-text available

May 2024

This research provides procedural insights into Cross-Cultural Management Research through the lens of a model inspired by Cultural Ecology. The model incorporates a comprehensive approach to understanding the intricacies of cross-cultural dynamics within organizational settings. By amalgamating principles from Cultural Ecology, the research outlines a systematic framework for conducting cross-cultural studies. The model encompasses key procedural steps, including literature review, study design, data collection, analysis, and interpretation. Through this holistic approach, the research aims to enhance the methodological rigor of cross-cultural management research and contribute valuable insights into the interplay between organizational dynamics and cultural diversity.

Critical analysis on the implementation barriers and consumer perception toward future electric mobility

Article

Full-text available

Mar 2022

Future mobility is expected to be sustainable in terms of energy production, consumption, and vehicle emissions. Embedded intelligent systems are playing a crucial role in the electrification of vehicles, their autonomy and implementation. Though electric vehicle technology is expected to lead the automotive powertrain architecture in the coming decades, various barriers currently hinder their acceptance into the automotive market. These barriers are generally categorized into battery technology, vehicle performance, charging infrastructure, consumer behavior, and government support. Hence, a detailed analysis of these barriers, especially for developing countries with minimal electric vehicle penetration is an area of concern. This article investigates the barriers and infers the comparative order of resolution for each barrier based on its priority to be identified and overcome. As consumers are the major influencers of electric vehicle demand and acceptance, barrier analysis is carried out based on their opinions. Using a Consumer Perception Survey, this article determines the influence of each barrier on potential users of electric vehicles. Fuzzy Stepwise Weight Assessment Ratio analysis and TOPSIS are implemented to allocate evaluation factors to each sub-barrier to obtain the hierarchy of priority. Furthermore, this article highlights the policies and schemes implemented in developed countries and correlates them with their electric vehicle population. The article sheds light on different measures to be taken in developing countries such as India to mitigate barriers and bridge gaps. The outcome of the literature review and consumer perception survey shows that the major factors affecting electric vehicle implementation in developing countries are the lack of charging infrastructure and high overall cost. Therefore, it is realized that such developing countries must introduce more schemes and incentives on infrastructural and operational costs to promote EV growth.

Accurate Mapping and Planning for Autonomous Racing

Conference Paper

Full-text available

Oct 2020

Optimal Design Strategy and Electro-Thermal Modelling of a High-Power Off-Board Charger for Electric Vehicle Applications

Conference Paper

Full-text available

Nov 2020

This paper proposes an optimal design procedure of high-power off-board charger (Off-BC) systems for electric vehicle EV applications. The main purpose is to design a high-power DC charging system with low current ripples for achieving maximum efficiency. This paper presents linear and non-linear modelling of a 175kW three-phase AC/DC converter for EV applications. This paper also presents manufacturer's datasheet driven accurate electro-thermal modelling. The design methodology is categorized into analytical modelling of three-phase active rectifier, optimal design strategy of passive filters, and control design of DC bus voltage. Finally, closed-loop dynamic electro-thermal behavior of Off-BC is simulated using MATLAB Simulink®. The proposed Off-BC operates with a current Total Harmonic Distortion (THD) below 3% and Power Factor (PF) above 99%, while the highest efficiency achieved is approximately 99% at rated power condition.

A Rapid Non-Linear Computation Model of Power Loss and Electro Thermal Behaviour of Three-Phase Inverters in EV Drivetrains

Conference Paper

Full-text available

Jun 2020

This paper proposes an analytical and dynamic electro-thermal model of a three-phase inverter based on wide bandgap semiconductors (SiC) for application in electric vehicles (EV). The optimal design approach of the passive filters is presented for LCL filters at DC side and LC filters at AC side of the inverter. The analytical modelling is performed, which includes mathematical representation of the inverter's power losses based on the specifications of the datasheet of the power modules. The dynamic electro-thermal simulation model of the inverter with integrated power loss model is developed. The designed state-space system thermal model is incorporated and can be used as a virtual sensor, for the conversion of power losses to temperature. With this inverter model, it's possible to simulate designs for achieving high efficiency in a specific operating range of temperature, with high specific power and reduced cost and size. The proposed design is considered for a power load of 20kW.

2020 roadmap on solid-state batteries

Article

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Aug 2020

Li-ion batteries have revolutionized the portable electronics industry and empowered the electric vehicle (EV) revolution. Unfortunately, traditional Li-ion chemistry is approaching its physicochemical limit. The demand for higher density (longer range), high power (fast charging), and safer EVs has recently created a resurgence of interest in solid state batteries (SSB). Historically, research has focused on improving the ionic conductivity of solid electrolytes, yet ceramic solids now deliver sufficient ionic conductivity. The barriers lie within the interfaces between the electrolyte and the two electrodes, in the mechanical properties throughout the device, and in processing scalability. In 2017 the Faraday Institution, the UK's independent institute for electrochemical energy storage research, launched the SOLBAT (solid-state lithium metal anode battery) project, aimed at understanding the fundamental science underpinning the problems of SSBs, and recognising that the paucity of such understanding is the major barrier to progress. The purpose of this Roadmap is to present an overview of the fundamental challenges impeding the development of SSBs, the advances in science and technology necessary to understand the underlying science, and the multidisciplinary approach being taken by SOLBAT researchers in facing these challenges. It is our hope that this Roadmap will guide academia, industry, and funding agencies towards the further development of these batteries in the future.

Battery digital twins: Perspectives on the fusion of models, data and artificial intelligence for smart battery management systems

Article

Full-text available

Jul 2020

Effective management of lithium-ion batteries is a key enabler for a low carbon future, with applications including electric vehicles and grid scale energy storage. The lifetime of these devices depends greatly on the materials used, the system design and the operating conditions. This complexity has therefore made real-world control of battery systems challenging. However, with the recent advances in understanding battery degradation, modelling tools and diagnostics, there is an opportunity to fuse this knowledge with emerging machine learning techniques towards creating a battery digital twin. In this cyber-physical system, there is a close interaction between a physical and digital embodiment of a battery, which enables smarter control and longer lifetime. This perspectives paper thus presents the state-of-the-art in battery modelling, in-vehicle diagnostic tools, data driven modelling approaches, and how these elements can be combined in a framework for creating a battery digital twin. The challenges, emerging techniques and perspective comments provided here, will enable scientists and engineers from industry and academia with a framework towards more intelligent and interconnected battery management in the future.

Integrated interdigital electrode and thermal resistance micro-sensors for electric vehicle battery coolant conductivity high-precision measurement

Article

Feb 2023

Advancements in Battery Technology for Electric Vehicles: A Comprehensive Analysis of Recent Developments

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