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MicroLED display is an innovative display technology with high brightness, wide color gamut, high aperture ratio, and excellent reliability. MicroLED represents a new era in display technology, and it is rapidly entering the mass production phase. It can be used in traditional display applications and can also be applied to various innovative display technologies, such as AR glasses, transparent displays, electric vehicles, or other new use cases, expanding the application of displays into more fields. Therefore, the most critical challenge is how to rapidly reduce costs to enable the widespread adoption of MicroLED. [ABSTRACT FROM AUTHOR]

This article discusses the role of power electronic converters in smart grids and electric vehicle applications. The authors highlight the technical connection between renewable energy, smart grids, energy storage, and electric vehicles. The special issue includes twelve accepted papers that cover topics such as high gain DC-DC converters, power converters for electric vehicle and motor drive applications, and power system applications. The papers present various novel topologies and control techniques, indicating the rapid growth and future potential of power converters in these fields. The authors express their appreciation to the contributing authors and anonymous reviewers for their valuable contributions to this special issue. [Extracted from the article]

This paper proposes a smart system for managing the operations of grid-connected charging stations for electric vehicles (EV) that use photovoltaic (PV) sources. This system consists of a mobile application for EV drivers to make charging reservations, an algorithm to optimize the charging schedule, and a remote execution module of charging operations based on the open charge point protocol (OCPP). The optimal charging schedule was obtained by solving a binary integer programming problem. The merits of our solution are illustrated by simulating different charging demand scenarios. [ABSTRACT FROM AUTHOR]

With the rise of the 3Ds--decarbonization, decentralization, and digitalization--the number of electric vehicles is projected to increase, necessitating the implementation of modern technologies to avoid unnecessary energy wastage. Numerous studies have been developed proposing electric vehicle (EV) charging frameworks in networks empowered by renewable energy resources. In addition, more focus has recently been directed on incorporating blockchain technology to assure security and transparency in trading systems. However, fewer studies have delved into developing a practical implementation of their solution due to the complexity of the topic. Therefore, this paper thoroughly investigates integrating blockchain technology in electric vehicle charging systems, analyzing the existing practical implementation and their characteristics. It comprises 48 relevant studies between 2017 and 2023, covering the following main research areas: (i) renewable energy-based electric charging systems, (ii) blockchain frameworks used in energy trading, and (iii) performance metrics of simulated and implemented solutions. Results show that blockchain applications in EVs and energy trading systems are highly current, and researchers are actively exploring ways to improve their efficiency and effectiveness. [ABSTRACT FROM AUTHOR]

Modelica is a very powerful language to simulate a very large set of systems, including electrical, thermal, mechanical, fluidic, control, and has already been used very extensively for several purposes, as the several Modelica conferences testify. Despite of this large literature, no paper seems to be available regarding the use of Modelica for real-time applications or hardware-in-the loop (HIL). This is a field where applications may be very fruitful. In this paper, the possibility of creating mixed software–hardware experiences (i.e., HIL), through combination of a Modelica program, the related simulation tool, a LabVIEW program, and the corresponding hardware is demonstrated. This demonstration is made using as an example a partial simulator of an electric vehicle running in a stand-alone PC, which communicates via User Datagram Protocol (UDP) packets with another PC running the LabVIEW program, which in turn is physically connected with the hardware-under-test. The obtained results are satisfying, given the inherent delay times due to the UDP communication. [ABSTRACT FROM AUTHOR]

Sustainable urban logistics is imperative in view of the increasing demands related to sustainability and the satisfaction of requirements related to sustainability, and it supports the implementation and use of solutions based on the application of electric vehicles when talking about transport logistics, ensuring all the resources necessary for the development of the basic urban process, and similar. In order to analyze the current situation related to the progress of researchers, in this paper, a bibliometric analysis of existing papers and research in the field of sustainable urban logistics was carried out. According to the findings, there is a significant lack of research dealing with urban logistics from the perspective of the supporting process, and a large number of authors summarize urban logistics solely from the perspective of transportation and storage. [ABSTRACT FROM AUTHOR]

This paper presents a bibliometric analysis of battery state estimation in electric vehicles. In this paper, a quick study is performed on the top global research contributors, funding agencies, and affiliate universities or institutes performing research on this topic while also finding the top keyword searches and top authors based on the most citations in the field of electric vehicles. Trend analysis is done by using the SCOPUS and Web of Science (WOS) databases (DB) from the period of 2000 to 2021. Battery state estimation plays a major role in the battery present state based on past experience. Battery available charge and health knowledge is a must for range estimation and helps us acknowledge if a battery is in useful condition or needs maintenance or replacement. A total of 136 documents in SCOPUS and 1311 documents in Web of Science were analyzed. Through this bibliometric analysis, we learn the top authors, country, publication journal, citation, funding agency, leading documents, research gap, and future trends in this research direction. The author Xiong Rui has the most publications, and he is working at the Beijing Institute of Technology, China. The most common institution is the Beijing Institute of Technology, and China is the most highly contributing country in this research. Most of the publications are conference types in SCOPUS DB and article types in WOS DB. The National Natural Science Foundation of China provides the most funding. The journal Energies has the most publications related to this field. The most cited works are by the authors M.A. Hannan and L.G. Lu in SCOPUS and WOS DB, respectively. A statistical analysis of the top ten countries' productivity results is also discussed. [ABSTRACT FROM AUTHOR]

With the introduction of automated driving, new requirements for the design and control of a vehicle powernet and powertrain arise, since the increasing level of driving automation shifts the responsibility for passengers’ safety from a driver towards automation system. Therefore, the functionality of the subsystems required for the automated transition to a standstill in case of a failure must be guaranteed. The work presented in this paper proposes a novel and generic system architecture for the energy management of a fail-operational powernet and powertrain in fault-free and failure case operation. The underlying control strategy is based on a concept enabling safety based range extension with the main goal to complete a driving mission at the safest possible location for the passengers. The proposed Energy Management System (EMS) approximates a driving trajectory to the destination based on route preview, which is then used for the dynamic optimization of the torque split in a powertrain with multiple motors. In this way, the total energy required for propulsion, which is also used as an input for the energy distribution, can be accurately predicted. The optimal distribution of the energy for the supply of safety-critical subsystems and powernet auxiliaries is then estimated by solving a mixed-integer optimization problem. If the desired driving mission cannot be completed, a three-level-degradation concept adapting the driving mission is applied. The individual modules of the EMS are presented in this paper and a system architecture enabling model predictive and adaptive energy distribution in a vehicle powertrain and powernet with automated fault reactions is proposed and exemplified with simulation results. [ABSTRACT FROM AUTHOR]

As a new generation of transportation, electric vehicles play an important role in carbon-peak targets. The development of electric vehicles needs the support of a charging network, and improper planning of charging stations will result in a waste of resources. In order to expand the charging network of electric vehicles and give full play to the low-carbon and efficient characteristics of electric vehicles, this paper proposed a charging station planning method that considers the characteristics of carbon emission trends. This paper combined the long short-term memory (LSTM) network with the stochastic impacts by regression on population, affluence, and technology (STIRPAT) model to predict the carbon emission trend and quantified the correlation between the construction speed of a charging station and the evolution characteristics of carbon emission by Pearson's correlation coefficient. A multi-stage charging station planning model was established, which captures the dynamic characteristics of the charging demand of the transportation network and determines the station deployment scheme with economic and low-carbon benefits on the spatiotemporal scale. The Pareto frontier was solved by using the elitist non-dominated sorting genetic algorithm. The model and solution algorithm were verified by the actual road network in a certain area of Shanghai. The results showed that the proposed scheme can meet the charging demand of regional electric vehicles in the future, improve the utilization rate of charging facilities, and reduce the carbon emission of transportation networks. [ABSTRACT FROM AUTHOR]

In the context of the global drive towards sustainability and rapid integration of renewables, electric vehicles, and charging infrastructure, the need arises for advanced operational strategies that support the grid while managing the intermittent nature of these resources. Microgrids emerge as a solution, operating independently or alongside the main grid to facilitate power flow management among interconnected sources and different loads locally. This review paper aims to offer a comprehensive overview of the different control strategies proposed in the literature to control microgrids with electric vehicle charging stations. The surveyed research is primarily categorized according to the employed control algorithms, although distinctions are also made based on defined microgrid architecture, utilization of specific power sources, and charging stations configurations. Additionally, this paper identifies research gaps in the current research. These gaps encompass the use of oversimplified models for charging stations and/or renewable sources operation, limited simulation time periods, or lack of experimental testing of proposed approaches. In the light of these identified shortcomings, this manuscript presents recommendations for guiding future research. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The topic of electric vehicles is currently generating significant interest worldwide. They are beneficial for the environment as they save up fuel, reduce pollution and carbon footprint too, but their market consumption is at the nascent level. The demand for these electric vehicles in developing countries is currently very slow and not that steady. The importance of Electric Vehicles (EVs) in the upcoming future is high and so it is crucial to figure out why these EVs are not being sold as much as we expect them in the market today. This paper tries to find out the multiple reasons why these vehicles are not in high demand, and why their counterparts, gasoline-fuelled vehicles are more in demand in the market. This paper discusses, after reviewing multiple other papers, why the consumption of EVs is low. Why their market demand is not where it should be and also multiple other facets. Finally, the paper finds that reasons like higher costs and low government incentives among multiple others are some factors leading to a low market demand for EVs. Also, the paper discusses the future scope of these EVs and how we can increase their consumption in the market. [ABSTRACT FROM AUTHOR]

In order to solve the problem of inconsistent energy in the charging and discharging cycles of lithium-ion battery packs, a new multilayer equilibrium topology is designed in this paper. The structure adopts a hierarchical structure design, which includes intra-group equilibrium, primary inter-group equilibrium and secondary intergroup equilibrium. This structure greatly increases the number of equilibrium paths for lithium-ion batteries, thus shortening the time required for equilibrium, and improving the overall efficiency. In terms of control strategy, fuzzy logic control (FLC) is chosen to control the size of the equilibrium current during the equilibrium process. We performed rigorous modeling and simulation of the proposed system by MATLAB and Simulink software. Experiments show that the multilayer equilibrium circuit structure greatly exceeds the traditional single-layer equilibrium circuit in terms of efficacy, specifically, the Li-ion battery equilibrium speed is improved by 12.71% in static equilibrium, 14.48% in charge equilibrium, and 11.19% in discharge equilibrium. In addition, compared with the maximum value algorithm, the use of the FLC algorithm reduces the equalization time by about 3.27% and improves the energy transfer efficiency by about 66.49% under the stationary condition, which verifies the feasibility of the equalization scheme. [ABSTRACT FROM AUTHOR]

For a segmented dynamic wireless power transfer (DWPT) system, when electric vehicles (EVs) move over two adjacent transmitting coils, the received power fluctuates greatly and has some loss. Arming at this problem, this paper proposes a dual receiver segmented DWPT topology to reduce the received power fluctuation of the receiving coil passing through the rail junction during the moving process of the EV. Through magnetic field analysis in this paper, tuning the current phase difference between two adjacent transmitting coils to 180° can enhance the magnetic field. In this paper, a dual receiver series topology (DRST) is designed to pick up power, which consists of four fully controlled components and four diodes. According to EVs' real-time position, the segment DWPT system has three working modes and six working states under the control of DRST. Finally, experiments are carried out. Compared to the single-receiving-coil system, DRST is effective in improving the average output power from 2.007 to 5.657 W and significantly reducing the power fluctuation. [ABSTRACT FROM AUTHOR]

With the rapid development of global energy transformation and new power system, ensuring the stability of distributed energy grid connections is the key to maintaining the reliable operation of the whole power system. This paper constructs detailed impedance models of grid-following (GFL) and grid-forming (GFM) inverters using a harmonic linearization method and thoroughly investigates the mechanisms of resonance when inverters are connected to the grid, as well as the impact of model parameters on the stability of the grid system. This paper also briefly analyzes the scenario where distributed energy and electric vehicles are integrated into the grid simultaneously, demonstrating that grid system stability can be ensured in complex grid situations through reasonable parameter design. Lastly, the accuracy of the proposed impedance models and analysis is verified through MATLAB/Simulink simulations. [ABSTRACT FROM AUTHOR]

Electric vehicles can reduce the dependence on limited resources such as oil, which is conducive to the development of clean energy. An accurate battery state of health (SOH) is beneficial for the safety of electric vehicles. A multi-feature and Convolutional Neural Network–Bidirectional Long Short-Term Memory–Multi-head Attention (CNN-BiLSTM-MHA)-based lithium-ion battery SOH estimation method is proposed in this paper. First, the voltage, energy, and temperature data of the battery in the constant current charging phase are measured. Then, based on the voltage and energy data, the incremental energy analysis (IEA) is performed to calculate the incremental energy (IE) curve. The IE curve features including IE, peak value, average value, and standard deviation are extracted and combined with the thermal features of the battery to form a complete multi-feature sequence. A CNN-BiLSTM-MHA model is set up to map the features to the battery SOH. Experiments were conducted using batteries with different charging currents, and the results showed that even if the nonlinearity of battery SOH degradation is significant, this method can still achieve a fast and accurate estimation of the battery SOH. The Mean Absolute Error (MAE) is 0.1982%, 0.1873%, 0.1652%, and 0.1968%, and the Root-Mean-Square Error (RMSE) is 0.2921%, 0.2997%, 0.2130%, and 0.2625%, respectively. The average Coefficient of Determination (R2) is above 96%. Compared to the BiLSTM model, the training time is reduced by an average of about 36%. [ABSTRACT FROM AUTHOR]

As they are the core components of the drive motor in electric vehicles, the accurate fault diagnosis of rolling bearings is the key to ensuring the safe operation of electric vehicles. At present, intelligent diagnostic methods based on current signals (CSs) are widely used owing to the advantages of the easy collection, low cost, and non-invasiveness of CSs. However, in practical applications, the fault characteristics of the CS are weak, resulting in diagnostic performance that fails to meet the expected standards. In this paper, a diagnosis method is proposed to address this problem and enhance the diagnosis accuracy. Firstly, CSs from two phases are processed by periodic resampling to enhance data features, which are then fused through splicing operations. Subsequently, a feature enhancement module is constructed using multi-scale feature fusion for decomposing the input. Finally, a diagnosis model is constructed by using an improved channel attention module (CAM) for enhancing the diagnosis performance. The results from experiments containing two different types of bearing datasets show that the proposed method can extract high-quality fault features and improve the diagnosis accuracy, presenting great potential in intelligent fault diagnosis and the maintenance of electric vehicles. [ABSTRACT FROM AUTHOR]

Electrification of vehicles intensifies their cooling demands due to the requirements of maintaining electronics/electrical systems below their maximum temperature threshold. In this paper, passive cooling approaches based on heat pipes have been considered for the thermal management of electric vehicle (EV) traction systems including battery, inverter, and motor. For the battery, a heat pipe base plate is used to provide high heat removal (180 W per module) and better thermal uniformity (<5°C) for the battery modules in a pack while downsizing the liquid cold plate system. In the case of Inverter, two phase cooling system based on heat pipes was designed to handle hot spots arising from high heat flux (~100 W/cm)–for liquid cooling and provide location independence and a dedicated cooling approach-for air cooling. For EV motors, heat pipe-based systems are explored for stator and rotor cooling. The paper also provides a glimpse of development on high-performance microchannel-based cold plate technologies based on parallel fins and multi-layer 3D stacked structures. Specifically, this work extends the concept of hybridization of two-phase technology based on heat pipes with single-phase technology, predominately based on liquid cooling, to extend performance, functionalities, and operational regime of cooling solutions for components of EV drive trains. In summary, heat pipes will help to improve and extend the overall reliability, performance, and safety of air and liquid cooling systems in electric vehicles. Graphic Abstract [ABSTRACT FROM AUTHOR]

Featured Application: The LM-IEKF algorithm proposed in this paper can effectively estimate the state of charge of a lithium-ion battery, and it is suitable for the estimation of an electric vehicle. The error covariance matrix in the IKEF process is modified by the LM algorithm, and it can still maintain a good convergence speed and estimation accuracy in the face of severe current changes. With the application of lithium batteries more and more widely, in order to accurately estimate the state of charge (SoC) of the battery, this paper uses the iterated extended Kalman filter (IEKF) algorithm to estimate the SoC. The Levenberg–Marquardt (LM) method is used to optimize the error covariance matrix of IKEF. Based on the hybrid pulse power characteristics experiment, a second-order Thevenin model with variable parameters is established on the MATLAB platform. The experimental results show that the proposed model is effective under the constant current discharge condition, the Federal Urban Driving Schedule (FUDS) condition, and the Beijing dynamic stress test (BJDST) condition. The results show that the simulation error of the improved LM-IEKF algorithm is less than 2% under different working conditions, which is lower than that of the IKEF algorithm. The improved algorithm has a fast convergence speed to the true value, and it has a good estimation accuracy in the case of large changes in external input current. Additionally, the fluctuation of error is relatively stable, which proves the reliability of the algorithm. [ABSTRACT FROM AUTHOR]

The number of individuals wearing cardiac pacemakers is gradually increasing as the population ages and cardiovascular disease becomes highly prevalent. The safety of pacemaker wearers is of significant concern because they must ensure that the device properly functions in various life scenarios. Electric vehicles have become one of the most frequently used travel tools due to the gradual promotion of low-carbon travel policies in various countries. The electromagnetic environment inside the vehicle is highly complex during driving due to the integration of numerous high-power electrical devices inside the vehicle. In order to ensure the safety of this group, the paper takes passengers wearing cardiac pacemakers as the object and the electric vehicle drive motors as the exposure source. Calculation models, with the vehicle body, human body, heart, and cardiac pacemaker, are built. The induced electric field, specific absorption rate, and temperature changes in the passenger's body and heart are calculated by using the finite element method. Results show that the maximum value of the induced electric field of the passenger occurs at the ankle of the body, which is 60.3 mV/m. The value of the induced electric field of the heart is greater than that of the human trunk, and the maximum value (283 mV/m) is around the pacemaker electrode. The maximum specific absorption rate of the human body is 1.08 × 10−6 W/kg, and that of heart positioned near the electrode is 2.76 × 10−5 W/kg. In addition, the maximum temperature increases of the human torso, heart, and pacemaker are 0.16 × 10−5 °C, 0.4 × 10−6 °C, and 0.44 × 10−6 °C within 30 min, respectively. Accordingly, the induced electric field, specific absorption rate, and temperature rise in the human body and heart are less than the safety limits specified in the ICNIRP. The electric field intensity at the pacemaker electrode and the temperature rise of the pacemaker meet the requirements of the medical device standards of ICNIRP and ISO 14708-2. Consequently, the electromagnetic radiation from the motor operation in the electric vehicle does not pose a safety risk to the health of passengers wearing cardiac pacemakers in this paper. This study also contributes to advancing research on the electromagnetic environment of electric vehicles and provides guidance for ensuring the safe travel of individuals wearing cardiac pacemakers. [ABSTRACT FROM AUTHOR]

Smart Transformers (STs) are being envisioned as a key element for the controllability of distribution networks in a future context of Renewable Energy Source (RES), Energy Storage System (ESS) and Electric Vehicle (EV) massification. Additionally, STs enable the deployment of hybrid AC/DC networks, which offer important advantages in this context. In addition to offering further degrees of controllability, hybrid AC/DC networks are more suited to integrate DC resources such as DC loads, PV generation, ESS and EV chargers. The purpose of the work developed in this paper is to address the feasibility of exploiting STs to actively coordinate a fleet of resources existing in a hybrid AC/DC network supplied by the ST aiming to provide active power-frequency regulation services to the upstream AC grid. The feasibility of the ST to coordinate the resources available in the hybrid distribution AC/DC network in order to provide active power-frequency regulation services is demonstrated in this paper through computational simulation. It is demonstrated that the aforementioned goal can be achieved using droop-based controllers that can modulate controlled variables in the ST. [ABSTRACT FROM AUTHOR]

An electric vehicle is a type of vehicle that is propelled by an electric motor fuelled by a battery. Electric vehicles are gaining popularity worldwide because of increasing environmental awareness and various benefits like less dependency on fossil fuel and they are considered an efficient and sustainable mode of transportation. This research presents a thorough overview and a bibliometric analysis of studies published related to consumer's adoption/ purchase intentions of Electric vehicles (EVs) from 1994 to 2023. The Scopus database was utilized to extract the papers as it is considered the largest database of peer-reviewed academic publications. The VOS Viewer software was used for the bibliometric analysis of networks between authors, institutions, countries, publications, journals, and keyword occurrence. The study was performed on 1 April 2023, which yielded a total of 140 documents after exclusion using the selected keywords. The findings indicate a considerable increase in EV adoption intention-related publications during the past six years. China is the world leader in this field of research, providing the maximum number of papers and involving the most prominent authors and research organizations. Whereas, Wang Z. has been the most productive author with a maximum number of publications in the area. Sustainability (Switzerland) journal stands out as the most prolific journal with the most publications. This analysis will help academicians better understand historical trends, current challenges, and prospective future research topics in the area of electric vehicle adoption/purchasing intentions. [ABSTRACT FROM AUTHOR]

Decision-making processes require the selection of appropriate and choice of the optimal solution for implementation. This means that different criteria and their sub-criteria evaluate various alternatives of possible solutions to determine the optimal solution. The research focuses on an Analytic Hierarchy Process (AHP) as one of the multi-criteria decision-making (MCDM) methods and its implementation to evaluate road transport vehicles. The AHP is one of the most used methods for evaluating projects in transport and traffic area. This paper presents a comprehensive review of studies on road transport vehicles evaluated by the AHP method. To gather research articles for the study, several databases such as Web of Science and Scopus were searched. The focus of the research is on road transport vehicles but the performance of the AHP method in the road sector, in general, is briefly reviewed. The results show that most of the studies use AHP for the evaluation of electric and autonomous vehicles. Finally, research results are discussed and recommendations for future research are proposed. [ABSTRACT FROM AUTHOR]

Batteries have been integral components in modern vehicles, initially powering starter motors and ensuring stable electrical conditions in various vehicle systems and later in energy sources of drive electric motors. Over time, their significance has grown exponentially with the advent of features such as "Start & Stop" systems, micro hybridization, and kinetic energy regeneration. This trend culminated in the emergence of hybrid and electric vehicles, where batteries are the energy source of the electric traction motors. The evolution of storage for vehicles has been driven by the need for larger autonomy, a higher number of cycles, lower self-discharge rates, enhanced performance in extreme temperatures, and greater electrical power extraction capacity. As these technologies have advanced, so have they the methods for their disposal, recovery, and recycling. However, one critical aspect often overlooked is the potential for battery reuse once they reach the end of their useful life. For each battery technology, specific regeneration methods have been developed, aiming to restore the battery to its initial performance state or something very close to it. This focus on regeneration holds significant economic implications, particularly for vehicles where batteries represent a substantial share of the overall cost, such as hybrid and electric vehicles. This paper conducts a comprehensive review of battery technologies employed in vehicles from their inception to the present day. Special attention is given to identifying common failures within these technologies. Additionally, the scientific literature and existing patents addressing regeneration methods are explored, shedding light on the promising avenues for extending the life and performance of automotive batteries. [ABSTRACT FROM AUTHOR]

Globally, transport is responsible for 23% of energyrelated carbon dioxide emissions and 80% of these emissions are attributable to road transport. Significant transformations, including extensive electrification of the sector, are necessary to achieve climate change goals. To understand new energy vehicle (NEV) policy research, we explore the status, knowledge base and research frontiers of NEV policy research by studying 355 papers collected from the Web of Science™ (WoS) Core Collection database. We map NEV policy research trends and knowledge structure development using knowledge domain technology and bibliometric techniques. The knowledge base analysis shows that: (a) NEV policy formation and evaluation; (b) policy incentives and consumer adoption; and (c) consumer preferences towards NEV adoption are all essential knowledge foundations in NEV policy research and development (R&D). The efficiency of NEV policy, cost-effectiveness of alternative fuel vehicles (AFVs), consumer preferences for NEV adoption, hydrogen energy and fuel cell vehicles, climate policy and CO2 emissions are five main lines of research in NEV policy studies. With the highest number of publications from Tsinghua University, China is the most active country in NEV policy research. Energy Policy, Sustainability and Journal of Cleaner Production are the core journals and Energy and Fuels and Environmental Sciences are the core disciplines of NEV policy research. The findings of this analysis help policymakers and researchers to navigate the literature on NEV, provide a clear map of existing works, identify the gaps and recommend promising avenues for future studies. [ABSTRACT FROM AUTHOR]

Recycling lithium-ion batteries (LIBs) has gained prominence in the last decade due to increasing supply chain constraints for critical materials (such as lithium and cobalt) and policy shift toward increased circularity of materials to mitigate environmental concerns. Conventional recycling methods (e.g., pyrometallurgical techniques) are suboptimal because of high-temperature (>1400 °C) processing with recovery yields ranging from 50% to 85%. On the other hand, optimal preprocessing/pretreatment of end-of-life (EoL) LIBs results in a) high (>90%) recovery yield, b) lower temperature processing (lower environmental footprint), c) high potential for commercial returns of materials, and d) lower safety risks. This paper reviews major preprocessing methods, including sorting, stabilization, dismantling and comminution, and separation for spent LIBs. The capabilities of major recycling firms and preferences for preprocessing in recycling methods are also reviewed, highlighting research and development (R&D) initiatives to allow more efficient and cleaner solutions for recycling spent LIBs. The industry-wide state-of-the-art recycling process is also detailed based on global practices, focusing on the highest yields and lowest environmental footprint. Finally, this paper provides policy recommendations to enable sustainable recycling of LIBs on a global scale, consequently reducing the environmental Impact of waste material and addressing the growing need for LIBs as a result of the increased demand for electric vehicles and stationary storage. [Display omitted] • This paper addresses waste management of Li-ion batteries (LIBs). • Outlines the pros and cons of the preprocessing approach in LIB recycling. • Reviews the commercial preprocessing techniques used by LIB recyclers. • Proposes an optimized recycling method to increase element recovery. • Highlights (and proposes) new regulations to make preprocessing viable. [ABSTRACT FROM AUTHOR]

In the COVID-19 era, automobiles with internal combustion engines are being replaced by eco-friendly vehicles. The demand for battery electric vehicles (BEVs) has increased explosively. Treatment of spent batteries has received much attention. Battery life can be extended via both efficient charging and driving. Consideration of the vehicles ahead when driving a BEV effectively prolongs battery life. Several studies have presented eco-friendly driving profiles for BEVs, the cited authors did not develop a BEV driving profile that considered battery life using reinforcement learning. Here, this paper presents a method of driving profile optimization that increases BEV battery life. This paper does not address how to regenerate spent batteries in an eco-friendly manner. The BEV driving profile is optimized employing a deep Q-network (a reinforcement learning method). This paper uses simulations to evaluate the effect of the driving profile on BEV battery life; these verified the applicability of our model. Finally, the speed profile optimization method was limited to improve energy efficiency or battery life in rapid speed change sections. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have already been acknowledged to be the most viable solution to the climate change that the entire globe has long been combating. Along the same line, it is a salient subject to expand the availability of EV charging infrastructure, which quintessentially necessitates the optimization of the charger's locations. This paper proposes to formulate the optimal EV charger location problem into a facility location problem (FLP). As an effort to find an efficient method to solve the well-known nonpolynomial deterministic (NP) hard problem, we present a comparative quantification among several representative solving techniques. This paper features two comprehensive case studies representing regions with an average and a high density of EVs. As such, this paper shows that the proposed framework can lead to successful location optimization with adequate refinement of solving techniques. [ABSTRACT FROM AUTHOR]

The deployment of intelligent transportation is still in its early stages and there are many challenges that need to be addressed before it can be widely adopted. Autonomous vehicles are a class of intelligent transportation that is rapidly developing, and they are being deployed in selected cities. A combination of advanced sensors, machine learning algorithms, and artificial intelligence are being used in these vehicles to perceive their environment, navigate, and make the right decisions. These vehicles leverage extensive data sourced from various sensors and computers integrated into the vehicle. Hence, massive computational power is required to process the information from various built-in sensors in milliseconds to make the right decision. The power required by the sensors and the use of additional computational power increases the energy consumption, and, hence, could reduce the range of the autonomous electric vehicle relative to a standard electric car and lead to additional emissions. A number of review papers have highlighted the environmental benefits of autonomous vehicles, focusing on aspects like optimized driving, improved route selection, fewer stops, and platooning. However, these reviews often overlook the significant energy demands of the hardware systems—such as sensors, computers, and cameras—necessary for full autonomy, which can decrease the driving range of electric autonomous vehicles. Additionally, previous studies have not thoroughly examined the data processing requirements in these vehicles. This paper provides a more detailed review of the volume of data and energy usage by various sensors and computers integral to autonomous features in electric vehicles. It also discusses the effects of these factors on vehicle range and emissions. Furthermore, the paper explores advanced technologies currently being developed by various industries to enhance processing speeds and reduce energy consumption in autonomous vehicles. [ABSTRACT FROM AUTHOR]

Though lithium-ion batteries are extensively applied in electric vehicles as a power source due to their excellent advantages in recent years, the security risk has inarguably always existed. The state of health (SOH) of lithium-ion batteries is one of the most important indicators related to security, the prediction of SOH is paid close attention spontaneously. To improve the prediction accuracy of SOH, this paper constructs an SOH prediction model based on a multi-kernel relevance vector machine and error compensation (EC-MKRVM). The provided model comprises a pre-estimation model and an error compensation model, both of which use the multi-kernel relevance vector machine (MKRVM) algorithm. The pre-estimation model takes the feature factors extracted in the charging segment as the input variable and the SOH pre-estimation value as the output. The error compensation model takes the pre-estimation error sequence as the input variable and the SOH prediction error as the output. Finally, the SOH prediction error is used to compensate for the SOH pre-estimation value of the pre-estimation model, and the final SOH prediction value is obtained. To verify the effectiveness and advancement of the model, the CACLE dataset is used for comparative experimental analysis. The results show that the proposed prediction model in this paper has higher prediction accuracy. [ABSTRACT FROM AUTHOR]

Aiming at the problem of source-load uncertainty caused by the increasing penetration of renewable energy and the large-scale integration of electric vehicles (EVs) into modern power system, a robust optimal operation scheduling algorithm for regional integrated energy systems (RIESs) with such uncertain situations is urgently needed. Based on this background, aiming at the problem of the irregular charging demand of EV, this paper first proposes an EV charging demand model based on the trip chain theory. Secondly, a multi-RIES optimization operation model including a shared energy storage station (SESS) and integrated demand response (IDR) is established. Aiming at the uncertainty problem of renewable energy, this paper transforms this kind of problem into a dynamic robust optimization with time-varying parameters and proposes an improved robust optimization over time (ROOT) algorithm based on the scenario method and establishes an optimal scheduling mode with the minimum daily operation cost of a multi-regional integrated energy system. Finally, the proposed uncertainty analysis method is verified by an example of multi-RIES. The simulation results show that in the case of the improved ROOT proposed in this paper to solve the robust solution of renewable energy, compared with the traditional charging load demand that regards the EVs as a whole, the EV charging load demand based on the trip chain can reduce the cost of EV charging by 3.5% and the operating cost of the multi-RIES by 11.7%. With the increasing number of EVs, the choice of the starting point of the future EV trip chain is more variable, and the choice of charging methods is more abundant. Therefore, modeling the charging demand of EVs under more complex trip chains is the work that needs to be studied in the future. [ABSTRACT FROM AUTHOR]

This paper deals with the analysis of challenges and perspectives of the transition to electric vehicles as a sustainable solution for the transport sector in the context of global energy challenges and the need to reduce negative environmental impacts. With an emphasis on the energy situation in Bosnia and Herzegovina, the paper explores the possibilities of switching to electric vehicles (EVs) and analyses the effects of energy sources on CO2 emissions. The paper highlights the motivation to switch to EVs, driven by the need to reduce greenhouse gas emissions and rely on renewable energy sources. After analysing relevant studies, it is concluded that smaller and lighter electric vehicles have lower CO2 emissions and that the participation of renewable sources in electricity production reduces these emissions. The conducted analysis of the vehicle fleet specifies that the CO2 emissions of electric vehicles are not zero and that they depend on the source of electricity. Furthermore, other factors, such as the production of batteries, also play an important role in the overall environmental impact. Although the motivation to switch to electric vehicles is emphasized to reduce greenhouse gas emissions and use renewable energy sources, it has been shown that the CO2 emissions of electric vehicles (EVs) are not zero and significantly depend on the energy sources. Calculations performed on the vehicle fleet of the Federation of Bosnia and Herzegovina for the year 2021, using Copert as the tool, showed that vehicles driven by fossil fuels emit about 1.6 million tonnes of CO2. In comparison, if all vehicles were replaced with electrical ones, the CO2 emissions would be about 1.15 million tonnes. As for the required electricity to power EVs, it is calculated that the required amount would be about 1,539 GWh per year. This paper acknowledges the presence of emissions associated with battery production, storage, and disposal, as well as vehicles themselves. However, it does not delve into this issue in detail. Future research will aim to address this matter more thoroughly. [ABSTRACT FROM AUTHOR]