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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]

Introduction: With the installation of advanced metering infrastructures, the operation data of EVs in the distribution networks can be obtained with time intervals of seconds and minutes. Based on these operation data, the impacts of integrating EVs into the distribution networks can be calculated and discussed. Methods: In this paper, an improved clustering algorithm with a new distance index for the daily curves of different types of EVs was proposed. The different types of EVs can be classified into several typical groups and the required number of operation scenarios can be reduced. After reducing the large-scale database to typical clusters, research can be conducted on the characteristics of EVs specific to certain scenarios. Results and discussion: In this way, the capability of integrating different types of EVs into the distribution network, such as fast EV charging stations, slow EV charging stations, and EV bus charging stations, is assessed from the perspective of load capacity size. The proposed clustering algorithm was verified with practical operation data. [ABSTRACT FROM AUTHOR]

With the needs of environmental protection and the adjustment of energy structure, new energy vehicles are playing an increasingly important role in the field of transportation today. The permanent-magnet brushless direct-current motor has the characteristics of high efficiency, and can be used in the drive system of new energy vehicles or other auxiliary equipment. In the control process of the permanent-magnet brushless direct-current motor, based on a three-Hall position sensor, due to various factors, there are some errors in the Hall position signal, which must be corrected by appropriate measures. In this paper, the relationship between the position deviation in the commutation interval and the non-commutation-phase current is analyzed, and the current expressions in three different states are given. A new closed-loop compensation strategy for correcting the inaccurate commutation caused by the Hall signal error is proposed. Taking the position of a 30° electrical angle before and after the phase-change point as the H point, realizing the current symmetry within the 30° interval around the H point as the target and the sum of the slopes of the tangent lines at the two points symmetrical within the β (0 < β < 30) electrical angle around the H point as the deviation, a proportional-integral regulator is designed to correct the phase error of the phase-change signal. Finally, it is verified by experiments that the closed-loop compensation strategy proposed in this paper can effectively compensate the phase deviation of the commutation signal at a speed of about 2000 r/min, which improves the working efficiency of the motor to a certain extent. [ABSTRACT FROM AUTHOR]

Peer-to-peer (P2P) energy trading has attracted a lot of attention and the number of electric vehicles (EVs) has increased in the past couple of years. Toward sustainable mobility, EVs meet the standard development goals (SDGs) for attaining a sustainable future in the transport sector. This development and increasing number of EVs creates an opportunity for prosumers to trade electricity. Considering this opportunity, this review article aims to provide an in-depth analysis of P2P energy trading of EVs using blockchain in centralized and decentralized networks, which enables prosumers to exchange energy directly with one another. The paper is aimed to provide the reader with a state-of-the-art review on the P2P energy trading for EVs, considering different blockchain algorithms that are practically implemented or still in the research phase. Moreover, the paper presents blockchain applications, current trends, and future challenges of EVs' energy trading. P2P energy trading for EVs using blockchain algorithms can be successfully implemented considering real-time scenarios and economically benefits smart sustainable societies. [ABSTRACT FROM AUTHOR]

Charging infrastructure deployment has taken off in many cities with the rise of the number of electric vehicles on the road. Expansion of infrastructure is a matter of prioritisation of resources to optimise the infrastructure. This paper explores how to measure charging station performance, to address the challenges that policy makers face. These performance indicators are used in a regression model, based upon current utilisation of the network, to predict which charging stations perform best. The results show that a model based on available geographical data and performance metrics of the current network are best combined to predict infrastructure performance. The variability between public charging stations is however big, as frequent user characteristics do determine the performance to a large extent. [ABSTRACT FROM AUTHOR]

With the increasing prominence of environmental and energy issues, electric vehicles (EVs) as representatives of clean energy vehicles have experienced rapid development in recent years, and the charging load has also exhibited statistical characteristics. Accurate prediction of EV charging load is crucial to improve grid load dispatch and intelligent level. However, current research on EV charging load prediction still faces challenges such as data reliability, complexity and variability of charging behavior, uncertainty, and lack of standardization methods. Therefore, this paper proposes an electric vehicle charging load prediction method based on spectral clustering and deep learning network (SC-CNNLSTM). Firstly, to address the insufficient amount of EV charging load data, this paper proposes to use Monte Carlo simulation to sample and simulate historical load data. Then, in order to identify the internal structure and patterns of charging load, the sampled and simulated dataset is clustered using spectral clustering, dividing the data into different clusters, where each cluster represents samples with similar charging load characteristics. Finally, based on the different sample features of each cluster, corresponding CNN-LSTM models are constructed and trained and predict using the respective data. By modifying the model parameters, the prediction accuracy of the model is improved. Through comparative experiments, the proposed method in this paper has significantly improved prediction accuracy compared to traditional prediction methods without clustering, thus validating the effectiveness and practicality of the method. [ABSTRACT FROM AUTHOR]

The rapid development of electric vehicles (EVs) has created more possibilities for their flexible participation in electric power dispatching. Considering the clustering and fast mobility of EVs coinciding with real-time market requirements for responsive demands, a bidding strategy is proposed in this paper to assist EV aggregators with submitting reasonable quotations for the real-time market. Based on Bayesian updating, the strategy in this paper considers relevant factors such as aggregator cost and expected return. First, a quotation strategy in electricity spot market is proposed for EV aggregators. Second, the applicable scope of the strategy is given under different scenarios. Then, the objective function of learning is proposed, and the specific process of Bayesian updating is demonstrated. Finally, a case study of the aggregator quotation strategy based on Bayesian updating is simulated to verify the effectiveness and feasibility of the proposed strategy, which helps improve the bidding success rate of EVs and peak-to-valley balance of the grid. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are increasingly being used, as they are more environmentally Friendly than conventional vehicles with internal combustion engines (ICE). Battery electric vehicles (BEVs) can be said to have zero exhaust emissions only if the electricity used to drive these vehicles is obtained in an environmentally friendly way. It is common knowledge that BEVs have a significantly higher overall mass than conventional vehicles. The significantly higher total vehicle weight of BEVs can have various adverse effects on energy consumption during movement and the vehicle's dynamics. In contrast to the negative aspects of BEVs, there are also positive aspects that are primarily related to the comfort of drivers and passengers, considering the main fact that they do not require the presence of a floor tunnel. In this paper, trends related to BEVs in the previous five years were statistically analysed. Changes in average sizes related to BEVs are shown, primarily internal dimensions that can be of crucial importance when deciding between BEVs and conventional vehicles. In the paper itself, other important trends are presented, both for the electric motor itself and for the batteries used in BEVs. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various papers within the issue on topics including an approach to decision-making in smart electricity markets, battery usage optimisation in electric vehicles and Beam search algorithms for multilabel learning.

The aim of this paper is to analyze in depth the coupling between leakage electromagnetic fields (EMFs) generated by an electric vehicle wireless power transfer (EV-WPT) device under misaligned operating conditions and metallic coronary stents, which could be potentially hazardous to human electromagnetic safety. In this paper, we established that a standing human with a coronary stent implant and a sitting human with a coronary stent implant are exposed to the leaked EMFs of an EV-WPT device with a transmission power of 11 kW and 22 kW and a transmission frequency of 85 kHz, and we quantified the induced E-field strength of the human body by considering the x- and z-axis misalignment ranges of the WPT device as [−75 mm, 75 mm]. The results showed that the enhancement of the induced E-field in the human tissues near the sharp edges of the implant is significant. Meanwhile, the larger the WPT device misalignment generated, the higher the transmission power, and the closer the human body is to the WPT device, the larger the induced E-fieldmax value of the human body is, which has the possibility of exceeding the ICNIRP safety limit, and there is a potential human body electromagnetic safety problem. [ABSTRACT FROM AUTHOR]

The integration of the Internet of Things (IoT) with renewable energy technologies is revolutionizing modern power systems by enhancing efficiency, reliability, and sustainability. This paper examines the role of the IoT in optimizing the integration and management of renewable energy sources, such as solar and wind power, into the electrical grid. The IoT enables real-time monitoring, data analysis, and automation, facilitating advanced load management, demand response, and energy storage solutions. Key advancements in IoT technologies, including smart grids and energy management systems, are discussed, highlighting their impact on improving grid stability and promoting the use of renewable energy. The paper also finds some challenges such as data security, privacy, and the need for standardized communication protocols. Furthermore, it finds how the IoT optimizes electric vehicle performance through advanced battery management, real-time energy consumption monitoring, and improved interaction with the electrical grid. Future research directions emphasize the potential of the IoT to further enhance renewable energy integration through artificial intelligence and machine learning, driving the transition towards a more sustainable and resilient energy future. [ABSTRACT FROM AUTHOR]

Efficient traffic sign recognition is crucial to facilitating the intelligent driving of new energy vehicles. However, current approaches like the Vision Transformer (ViT) model often impose high storage and computational demands, escalating hardware costs. This paper presents a similarity filter pruning method based on locally linear embedding. Using the alternating direction multiplier method and the loss of the locally linear embedding method for the model training function, the proposed pruning method prunes the operation model mainly by evaluating the similarity of each layer in the network layer filters. According to the pre-set pruning threshold value, similar filters to be pruned are obtained, and the filter with a large cross-entropy value is retained. The results from the Belgium Traffic Sign (BelgiumTS) and German Traffic Sign Recognition Benchmark (GTSRB) datasets indicate that the proposed similarity filter pruning based on local linear embedding (SJ-LLE) pruning algorithm can reduce the number of parameters of the multi-head self-attention module and Multi-layer Perceptron (MLP) module of the ViT model by more than 60%, and the loss of model accuracy is acceptable. The scale of the ViT model is greatly reduced, which is conducive to applying this model in embedded traffic sign recognition equipment. Also, this paper proves the hypothesis through experiments that "using the LLE algorithm as the loss function for model training before pruning plays a positive role in reducing the loss of model performance in the pruning process". [ABSTRACT FROM AUTHOR]

As a core component of electric vehicles, the state of health (SOH) of lithium-ion battery has a direct impact on vehicle performance and safety. Existing data-driven models primarily focus on feature extraction, often overlooking the processing of multi-level redundant information and the utilization of multi-stage battery features. To address the issues, this paper proposes a novel data-driven method, named multi-stage mixed attention U-Net (MMAU-Net), for SOH estimation, which is based on both the phased learning and an encoder–decoder structure. First, the geometric knee-point division method is proposed to divide the battery life cycle into multiple stages, which allows the model to learn the distinctive features of battery degradation at each stage. Second, to adeptly capture degraded features and reduce redundant information, we propose a mixed attention U-Net model for the SOH prediction task, which is constructed upon the fundamental U-Net backbone and is enhanced with time step attention and feature attention modules. The experimental results validate the proposed method's feasibility and efficacy, demonstrating an acceptable performance across a spectrum of evaluative metrics. Consequently, this study offers a research within the domain of battery health management. [ABSTRACT FROM AUTHOR]

The research of electric vehicle power battery fault diagnosis technology is turning to machine learning methods. However, during operation, the time of occurrence of faults is much smaller than the normal driving time, resulting in too small a proportion of fault data as well as a single fault characteristic in the collected data. This has hindered the research progress in this field. To address this problem, this paper proposes a data enhancement method using Least Squares Generative Adversarial Networks (LSGAN). The method consists of training the original power battery fault dataset using LSGAN models to generate diverse sample data representing various fault states. The augmented dataset is then used to develop a fault diagnosis framework called LSGAN-RF-GWO, which combines a random forest (RF) model with a Gray Wolf Optimization (GWO) model for effective fault diagnosis. The performance of the framework is evaluated on the original and enhanced datasets and compared with other commonly used models such as Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and Naïve Bayes (NB). The results show that the proposed fault diagnosis scheme improves the evaluation metrics and accuracy level, proving that the LSGAN-RF-GWO framework can utilize limited data resources to effectively diagnose power battery faults. [ABSTRACT FROM AUTHOR]

This systematic review paper examines the current integration of artificial intelligence into energy management systems for electric vehicles. Using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology, 46 highly relevant articles were systematically identified from extensive literature research. Recent advancements in artificial intelligence, including machine learning, deep learning, and genetic algorithms, have been analyzed for their impact on improving electric vehicle performance, energy efficiency, and range. This study highlights significant advancements in energy management optimization, route planning, energy demand forecasting, and real-time adaptation to driving conditions through advanced control algorithms. Additionally, this paper explores artificial intelligence's role in diagnosing faults, predictive maintenance of electric propulsion systems and batteries, and personalized driving experiences based on driver preferences and environmental factors. Furthermore, the integration of artificial intelligence into addressing security and cybersecurity threats in electric vehicles' energy management systems is discussed. The findings underscore artificial intelligence's potential to foster innovation and efficiency in sustainable mobility, emphasizing the need for further research to overcome current challenges and optimize practical applications. [ABSTRACT FROM AUTHOR]

Supercapacitors (SCs) are an emerging energy storage technology with the ability to deliver sudden bursts of energy, leading to their growing adoption in various fields. This paper conducts a comprehensive review of SCs, focusing on their classification, energy storage mechanism, and distinctions from traditional capacitors to assess their suitability for different applications. To investigate the voltage response of SCs, the existing electrical equivalent circuits are further studied. The analysis is carried forward with the parameter of impedance, which has not so far been addressed. Impedance analysis is essential for a better understanding of SCs as capacitors work on alternating source of supply. The paper also highlights the applications of SCs in electric automobiles and charging stations, showcasing their advantages such as fast charging and higher power density compared to traditional capacitors. Additionally, other applications in areas like the military, medicine, and industry are discussed, demonstrating the versatility of SC technology. [ABSTRACT FROM AUTHOR]

With the rapid development of digital technology and the increasing focus on the global supply chain network, it has become a new challenge for international companies to select digital technology innovation projects in an efficient way, so as to improve their supply chain position and competitiveness. Prior works have identified the effects of digital technology adoption on companies' supply chain positions; however, there has been limited research on the impact of digital technology innovation heterogeneity on companies' supply chain position and the pathways through which this effect plays out. Hence, based on the global supply chain panel data from Chinese new energy vehicle companies, this study used a two-way fixed-effects model and causal stepwise regression analysis to study the impact of digital technological innovation on companies' supply chain position and the dynamic mechanisms between them. The empirical results show that all three types of digital technology innovations, in the design and development process, the production and manufacturing process, and the sales and after-sales process, significantly enhance the company's supply chain position. Further mechanism analysis shows that digital technology innovations enhance the company's managerial efficiency and profitability mainly by reducing costs and increasing revenues, which ultimately improves the company's supply chain position. This paper can provide a reference for policy makers to promote the application and development of a company's digital technology and enhancing the supply chain position. [ABSTRACT FROM AUTHOR]

To further improve the braking energy recovery efficiency of battery electric vehicles and increase the range of the cars, this paper proposes a multi-mode switching braking energy recovery control strategy based on fuzzy control. The control strategy is divided into three modes: single-pedal energy recovery, coasting energy recovery, and conventional braking energy recovery. It takes the accelerator pedal and brake pedal opening as the switching conditions. It calculates the front and rear wheel braking ratio allocation coefficients and the motor braking ratio through fuzzy control to recover braking energy. The genetic algorithm (GA) is used to update the optimized affiliation function to optimize the motor braking allocation ratio through fuzzy control, and joint simulation is carried out based on the NEDC (New European Driving Cycle) and CLTC-P (China Light-duty Vehicle Test Cycle for Passenger vehicles) cycle conditions. The results show that the multi-mode braking energy recovery control strategy proposed in this paper improves the energy recovery rate and range contribution rate by 4% and 9.6%, respectively, and increases the range by 22.5 km under NEDC cycle conditions. It also improves the energy recovery rate and range contribution rate by 8.7% and 5.5%, respectively, and increases the range by 13 km under CLTC-P cycle conditions, which can effectively improve the energy recovery efficiency of the vehicle and increase the range of battery electric vehicles. [ABSTRACT FROM AUTHOR]

Over the past decade, Chinese government has invested large amounts of government subsidies to support the development of new energy vehicle (NEV) industry. Whether the government subsidy has achieved the expected effect and how to further adjust the subsidy policy to better promote the development of the NEV industry have received wide attention. This paper considers the characteristics of technological innovation and financing constraints of enterprises at different position of the NEV industry chain, and empirically test the incentive effect of government subsidies on R&D investment from the perspective of the industry chain. The research findings show that government subsidies promote the R&D investment of all enterprises in the NEV industry chain, however, the promotion effect on upstream and midstream enterprises is more significant. With the reduction of consumption subsidies, the promotion effect of government subsidies on the R&D investment of listed companies in the NEV industry has been strengthened. Further research finds that government subsidies promote R&D investment by alleviating the financing constraints of upstream and midstream enterprises. There is an industry chain synergy effect in the technological innovation of new energy vehicle enterprises, and government subsidies can promote this synergistic effect from upstream to downstream. [ABSTRACT FROM AUTHOR]

In order to improve the transportation efficiency of cold chain logistics and achieve the low-carbon goal of energy saving and emission reduction, this paper discusses the multi-objective low- carbon cold chain vehicle routing problem in cold-chain logistics distribution (MOLCVRPCLD). Compared with the existing literature, this paper considers a more low-carbon mixed fleet of electric vehicles and fuel vehicles, and establishes an optimization model with the goal of optimal comprehensive cost and minimum carbon emissions. Comprehensive cost includes fixed cost of vehicles, time window cost, cargo damage cost, fuel consumption cost and power consumption cost. Carbon emissions includes fuel emissions and refrigeration emissions. According to the characteristics of the model, a non-dominated sorting genetic algorithm II (NSGA-II) is designed to solve the model. And genetic algorithm (GA) is used to optimize the two targets respectively to verify the effectiveness of the model and algorithm. [ABSTRACT FROM AUTHOR]

This paper summarizes the results of numerous studies aimed at improving the operating characteristics of electric motors used in light electric vehicles (LEVs). This review focuses on four types of electric motors that can be installed in the drive wheel rims of LEVs. Due to the availability of new magnetic materials and the use of advanced techniques for optimizing the design of electric motors, new motor topologies have emerged. The latest generation motors have been shown to be more efficient, have higher torque density, and generate less torque ripple. This paper indicates and discusses current trends in the topology of electric motors designed for LEV drives. In this context, the effectiveness of the proposed design modifications in terms of selected motor operational characteristics was assessed. The proposed new topologies were compared with commercial solutions, also in terms of the possibility of improving their operational parameters. [ABSTRACT FROM AUTHOR]

Climate change risks have triggered the international community to find efficient solutions to reduce greenhouse gas (GHG) emissions mainly produced by the energy, industrial, and transportation sectors. The problem can be significantly tackled by promoting electric vehicles (EVs) to be the dominant technology in the transportation sector. Accordingly, there is a pressing need to increase the scale of EV penetration, which requires simplifying the manufacturing process, increasing the training level of maintenance personnel, securing the necessary supply chains, and, importantly, developing the charging infrastructure. A new modular trend in EV manufacturing is being explored and tested by several large automotive companies, mainly in the USA, the European Union, and China. This modular manufacturing platform paves the way for standardised manufacturing and assembly of EVs when standard scalable units are used to build EVs at different power scales, ranging from small light-duty vehicles to large electric buses and trucks. In this context, modularising EV electric systems needs to be considered to prepare for the next EV generation. This paper reviews the main modular topologies presented in the literature in the context of EV systems. This paper summarises the most promising topologies in terms of modularised battery connections, propulsion systems focusing on inverters and rectifiers, modular cascaded EV machines, and modular charging systems. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach for rapidly balancing lithium-ion batteries using a single DC–DC converter, enabling direct energy transfer between high- and low-voltage cells. Utilizing relays for cell pair selection ensures cost-effectiveness in the switch network. The control system integrates a battery-monitoring IC and an MCU to oversee cell voltage and ensure battery protection. A prototype circuit with twelve lithium-ion batteries demonstrates the method's efficacy, achieving a remarkable balancing time of 48 min during charging with a maximum efficiency of 89.85%. Comparative analysis with other methods underscores the superior performance of the proposed balancing circuit in terms of balancing time and implementation cost. Furthermore, this paper delves into hardware aspects of battery management systems (BMSs) for electric vehicles and stationary applications. It offers an overview of prevailing concepts in state-of-the-art systems, aiding readers in assessing considerations essential for BMS design in various applications. The discussion includes examples of battery packs sourced from commercially available electric vehicles. Subsequently, the manuscript addresses implementation aspects concerning the measurement of critical physical variables such as voltage, current, and temperature, alongside balancing strategies. [ABSTRACT FROM AUTHOR]

When large-scale electric vehicles are connected to the grid for unordered charging, it will seriously affect the stability and security of the power system. To solve this problem, this paper proposes a regional power network optimization scheduling method considering vehicle network interaction. Initially, based on the user behavior characteristics and charging and discharging characteristics of electric vehicles, a charging and discharging behavior model of electric vehicles was established. Based on the Monte Carlo sampling algorithm, the scheduling upper and lower limits of each scheduling cycle of electric vehicles were described, and the scheduling potential of each scheduling cycle of electric vehicles was obtained. Then, the electricity price is then used as an incentive parameter to guide EV users to charge during periods of low electricity prices and participate in discharge during periods of peak electricity prices. Aiming at the highest economic efficiency, the best consumption effect of new energy and the smoothest demand-side power curve of regional power grid, a three-objective optimal dispatching model was established. In the later stage, uncertainty factors are taken into consideration by introducing the concept of interval numbers, and an interval multi-objective optimization dispatching model is established. The two dispatching models are solved by NSGA-II algorithm and improved NSGA-II algorithm, and the Pareto solution set is obtained. Finally, based on the analytic Hierarchy Process (AHP), the optimal scheduling scheme is determined. The Monte Carlo sampling method is used to simulate the user side charging demand, and the effectiveness of this method is verified. In addition, the results of the interval multi-objective optimization model and the deterministic multi-objective optimization model are compared, and it is proved that the solution results of the interval multi-objective model are more adaptive, practical and robust to the uncertain factors. [ABSTRACT FROM AUTHOR]

This paper presents an advanced Wireless Power Transfer (WPT) system for electric vehicles (EVs) featuring Active Load Impedance Matching (ALIM) at the rectification stage. Unlike traditional synchronous rectification, ALIM dynamically adjusts load impedance, optimizing energy transfer efficiency and reducing thermal stresses, system costs, and mass. The system incorporates two circuits optimized for distinct frequency bands: one operates below 10 kHz using standard copper wiring for cost-effectiveness, and the other at 85 kHz, which significantly reduces the mass of the onboard coil and magnetic circuit while ensuring interoperability according to SAE J2954 standard. Our approach enhances charging efficiency across various operating conditions, improves thermal management, and minimizes maintenance costs. Additionally, it enables partial compensation for vehicle misalignment and ground assembly impedance, further boosting efficiency and interoperability. Experimental results demonstrate a notable increase in efficiency and reduction in system mass, confirming the superiority of the ALIM-equipped WPT system over conventional solutions. This paper underscores the potential of ALIM to advance the scalability, efficiency, and economic viability of wireless EV charging technology, promoting broader adoption and sustainability in EV infrastructures. By providing a comprehensive solution that addresses key challenges in wireless charging, our work paves the way for more efficient and cost-effective EV charging systems. [ABSTRACT FROM AUTHOR]

Accurate forecasting of electric vehicle (EV) load is essential for grid stability and energy management. EV load forecasting is influenced by multiple factors. At present, the load forecasting model for EVs mainly uses collected sample data to build a data‐driven model. But these algorithms need to collect all the data together to train the model, ignoring the privacy of each data collection source. In a competitive market environment, each device service provider is not willing to share the sample data they store. Aiming at this problem, this paper proposes an EV load diagnosis algorithm considering data privacy. Firstly, a convolutional neural network with dual attention mechanism is constructed as the basic time series forecasting model. The association rule algorithm is used to select weather data with strong associations as the inputs of the model. Each service provider uses local data to perform deep learning network. All models are then trained using a federated learning framework. During the entire training process, historical data is stored locally, and only model parameter information is shared and interacted; thus data privacy is protected. Finally, the validity of the algorithm in this paper is verified by using real collected EV load data. [ABSTRACT FROM AUTHOR]

The advancement of new energy vehicles (NEVs) represents a strategic initiative to combatting climate change, mitigating the energy crisis, and fostering green growth. Using provincial panel data from China between 2017 and 2022, in this study, we applied machine learning techniques for sentiment analysis of textual reviews, used word frequency statistics to explore consumers' views on the attributes of new energy vehicles, and constructed a consumer sentiment index to study the impact of consumer sentiment on NEV sales. Considering the dependence of NEVs on a charging station, this paper explores the nonlinear impact of the popularity of charging stations on the relationship between consumer sentiment and sales of new energy vehicles. The findings indicate the potential for enhancement in the areas of space, interior design, and comfort of NEVs. Additionally, consumer sentiment was found to facilitate the diffusion of NEVs, with this effect being heterogeneous across different educational backgrounds, income levels, and ages. Furthermore, the availability of per capita public charging stations was shown to significantly reduce range anxiety and encourage consumer purchasing behavior. [ABSTRACT FROM AUTHOR]

This paper presents a review of the most common power converters and torque ripple minimisation approaches for switched reluctance motors (SRMs). Unlike conventional three-phase AC motors, namely squirrel cage induction motors and permanent magnet synchronous motors, which require a typical three-phase inverter for operation, the switched reluctance motor requires a different topology power converter for reliable and efficient operation. In addition, due to the non-linear, discrete nature of SRM torque production, torque ripple is severely pronounced, which is undesirable in servo applications like electric vehicles. Hence, deploying a proper torque control function for smooth and quiet motor operation is crucial. This paper sheds light over the most popular SRM power converters as well as torque ripple minimisation methods, and it suggests an optimal SRM drive topology for EV applications. [ABSTRACT FROM AUTHOR]

This paper presents a groundbreaking control strategy for a bidirectional battery charger that allows power to be injected into the smart grid while simultaneously compensating for the grid's reactive power using an electric vehicle battery. An artificial neural network (ANN) controller is utilized for precise design to ensure optimal performance with minimal error. The ANN technique is applied to generate sinusoidal pulse width modulation (SPWM) for a bidirectional AC–DC inverter, with the entire algorithm simulated in MATLAB Simulink.The core innovation of this study is the creation of the ANN algorithm, which supports grid compensation using electric vehicle batteries, an approach termed "vehicle-for-grid". Additionally, the paper details the PCB circuit design of the system controlled by the DSP F28379D board, which was tested on a three-phase motor. The total harmonic distortion (THD) of the proposed ANN algorithm is approximately 1.85 % , compared to the MPC algorithm's THD of about 2.85 % . This indicates that the proposed algorithm is more effective in terms of the quality of the power injected into the grid. Furthermore, it demonstrates effective grid compensation, with the reactive power effectively neutralized to 0 KVAR in the vehicle-for-grid mode. [ABSTRACT FROM AUTHOR]

The optimized layout of electric vehicle (EV) chargers is not only crucial for users' convenience but also a key element in urban sustainable development, energy transition, and the promotion of new energy vehicles. In order to provide a basis for the problem of localization and capacity determination of chargers and compare the merits of several mainstream algorithms, this paper first establishes an optimization model with the objective of minimizing the total investment cost of all the chargers and the constraint of meeting the charging demands of all electric vehicles. Optimizations were performed using genetic algorithm (GA), surrogate optimization algorithm (SOA), and mixed integer linear programming (MILP) algorithm, respectively. In the case of using MILP, the original nonlinear optimization problem was transformed into a linear problem. In the planning of city-level EV chargers, MILP took 14182.57 s to calculate the minimum cost of 34.62 million yuan. After retaining only 10% of the original data amount, SOA took 87651.34 s to calculate the minimum cost of 3.01 million yuan. The results indicate that GA is prone to falling into local optima and is not suitable for large-scale optimization problems. SOA, on the other hand, requires significant memory consumption, so the issue of memory usage needs to be carefully considered when using it directly. Although MILP is only applicable to linear programming problems, it has the advantages of lower memory usage and higher reliability if the problem can be transformed into a linear one. [ABSTRACT FROM AUTHOR]

Batteries play a crucial role in the domain of energy storage systems and electric vehicles by enabling energy resilience, promoting renewable integration, and driving the advancement of eco-friendly mobility. However, the degradation of batteries over time remains a significant challenge. This paper presents a comprehensive review aimed at investigating the intricate phenomenon of battery degradation within the realm of sustainable energy storage systems and electric vehicles (EVs). This review consolidates current knowledge on the diverse array of factors influencing battery degradation mechanisms, encompassing thermal stresses, cycling patterns, chemical reactions, and environmental conditions. The key degradation factors of lithium-ion batteries such as electrolyte breakdown, cycling, temperature, calendar aging, and depth of discharge are thoroughly discussed. Along with the key degradation factor, the impacts of these factors on lithium-ion batteries including capacity fade, reduction in energy density, increase in internal resistance, and reduction in overall efficiency have also been highlighted throughout the paper. Additionally, the data-driven approaches of battery degradation estimation have taken into consideration. Furthermore, this paper delves into the multifaceted impacts of battery degradation on the performance, longevity, and overall sustainability of energy storage systems and EVs. Finally, the main drawbacks, issues and challenges related to the lifespan of batteries are addressed. Recommendations, best practices, and future directions are also provided to overcome the battery degradation issues towards sustainable energy storage system. [ABSTRACT FROM AUTHOR]

The coevolution of technological capabilities and institutional arrangements is critical for the success of innovations. Yet, it remains unclear how actors' shifting relations during institutional change affect innovation. The paper examines the dynamics in how actors shape the institution by integrating the institutional logics perspective with policy mix literature. Insights are drawn from the Online Electric Vehicle and Fuel Cell Electric Vehicle cases in Korea, where involved stakeholders revealed contrasting identities and practices throughout the innovation stages. While both cases witnessed shifts in institutional logics, the repercussions on innovation differed depending on the characteristics of actors' identity mix. Findings contribute to institutional theory by expanding the traditional dyadic perspective on incumbent and emerging institutional logics. We also add to the policy mix literature by introducing a neo-institutional perspective. The paper concludes by underlining how the overarching characteristics of the institutional mix trigger different coordination failures. [ABSTRACT FROM AUTHOR]

With the ongoing promotion and adoption of electric vehicles, intelligent and connected technologies have been continuously advancing. Electrical control systems implemented in electric vehicles have emerged as a critical research direction. Various drive-by-wire chassis systems, including drive-by-wire driving and braking systems and steer-by-wire systems, are extensively employed in vehicles. Concurrently, unavoidable issues such as conflicting control system objectives and execution system interference emerge, positioning integrated chassis control as an effective solution to these challenges. This paper proposes a model predictive control-based longitudinal dynamics integrated chassis control system for pure electric commercial vehicles equipped with electro–mechanical brake (EMB) systems, centralized drive, and distributed braking. This system integrates acceleration slip regulation (ASR), a braking force distribution system, an anti-lock braking system (ABS), and a direct yaw moment control system (DYC). This paper first analyzes and models the key components of the vehicle. Then, based on model predictive control (MPC), it develops a controller model for integrated stability with double-layer torque distribution. The required driving and braking torque for each wheel are calculated according to the actual and desired motion states of the vehicle and applied to the corresponding actuators. Finally, the effectiveness of this strategy is verified through simulation results from Matlab/Simulink. The simulation shows that the braking deceleration of the braking condition is increased by 32% on average, and the braking distance is reduced by 15%. The driving condition can enter the smooth driving faster, and the time is reduced by 1.5 s~5 s. The lateral stability parameters are also very much improved compared with the uncontrolled vehicles. [ABSTRACT FROM AUTHOR]

The growing relevance of sustainability, as well as the necessity to replace traditional forms of transportation with sustainable ones, has made sustainable urban mobility an imperative. In order to respond to the ever-increasing need to develop sustainable modes of transport, the importance of electric, autonomous, and electric autonomous vehicles is increasingly emphasized. In addition, as trends of growth and development in electric autonomous vehicle technology are increasing, one of the questions that has appeared is whether autonomous electric vehicles represent one of the mechanisms that will be used to increase the sustainability of urban mobility. With this in mind, the results of a systematic analysis of existing research in the WOS and Scopus databases using the keywords "urban mobility", "electric vehicles", and "autonomous vehicles" was carried out to identify research trends in the use of autonomous electric vehicles in urban areas. The research showed that authors focus on the advantages and disadvantages of autonomous electric vehicles and their usage in the urban mobility system, but an insufficient number of authors consider and define the need to plan the transition towards incorporating autonomous electric vehicles into the urban system. The results of this research also indicate an insufficient number of papers that research and describe the application of autonomous electric vehicles in distribution logistics. This paper provides an overview of existing research related to autonomous electric vehicles and the challenges of transition in the context of infrastructure and the development of a culture of sustainability among urban residents. [ABSTRACT FROM AUTHOR]

Sustainable development in the 21st century faces significant challenges due to finite reserves of fossil fuels and environmental pollution. In the context of new energy electric vehicles (NEEVs), the wide-bandgap semiconductor known as the silicon carbide-metal oxide-semiconductor field-effect transistor (SiC MOSFET) and the permanent magnet synchronous motor (PMSM) have emerged as advantageous sources. However, the use of these components gives rise to electromagnetic interference (EMI) issues, which impede the achievement of electromagnetic compatibility (EMC) standards in the motor drive control system. This paper aims to elucidate the generation mechanism, propagation path, and test infrastructure of EMI. Furthermore, it proposes a system-level conducted EMI equivalent circuit model for the motor drive control system, encompassing the power battery pack, busbar cable, LISN, three-phase inverter, and PMSM. Building upon this foundation, the principles for suppressing and optimizing EMI noise are discussed. The paper concludes with the validation of simulations and experimental results, which demonstrate the effectiveness of the proposed approach. It is anticipated that professionals with an interest in the field of EMI/EMC will find this paper to be of both theoretical and practical importance. [ABSTRACT FROM AUTHOR]

This paper introduces a new hybrid DC–DC converter with enhanced voltage gain and synchronized multiple output capabilities, specifically tailored for smart grid applications. The proposed converter is based on the integration of non-isolated Zeta and Mahafzah converters, comprising a single controlled switch, two diodes, three inductors, and two coupling capacitors. The primary objective of this novel hybrid converter is to improve voltage gain as compared to conventional Zeta and Mahafzah topologies. By achieving higher voltage gain at lower duty cycles, the converter effectively reduces voltage stress on semiconductor switches and output diodes, thereby enhancing overall performance and reliability. A comprehensive examination of the hybrid converter's operating principle is presented, along with detailed calculations of duty cycle and switching losses. The paper also explores the converter's application in smart grids, specifically in the context of renewable energy systems and electric vehicles. Two distinct scenarios are analyzed to evaluate the converter's efficacy. Firstly, the converter is assessed as a DC–DC converter for renewable energy systems, highlighting its relevance in sustainable energy applications. Secondly, the converter is evaluated as an electric vehicle adapter, showcasing its potential in the transportation sector. To validate the converter's performance, extensive simulations are carried out using MATLAB/SIMULINK with parameters set at 25 kW, 200 V, and 130 A. The simulation results demonstrate the converter's ability to efficiently supply multiple loads with opposing energy flows, making it a promising technology for optimized grid management and energy distribution. Moreover, the paper investigates the total harmonic distortion (THD) of the grid current, focusing on its impact in smart grid environments. Notably, the new hybrid converter topology achieves a THD of 21.11% for the grid current, indicating its ability to effectively mitigate harmonics and improve power quality. Overall, this research introduces a cutting-edge hybrid DC–DC converter that enhances voltage gain and synchronizes multiple outputs, specifically catering to the requirements of smart grid applications. The findings underscore the converter's potential to significantly contribute to the advancement of efficient and resilient power conversion technologies for smart grids, enabling seamless integration of renewable energy systems and electric vehicles into the grid. [ABSTRACT FROM AUTHOR]

The digitalization of the automotive industry presents significant potential for technical advantages, such as the online collection of customer driving data. These data can be used for customer-oriented development to improve the durability of components or systems. However, due to current limitations in data transfer, the sampling frequency is typically lower than that of classic dataloggers. This paper examines the importance of low-frequency data in the development of drivetrain durability and investigates the extent to which these data can be utilized for a drivetrain durability analysis. Real driving data were utilized as a database to demonstrate the impact of downsampling on data significance, with the deviation in damage serving as the criteria. The findings suggest that low-frequency data, when available in sufficient quantities, can provide valuable information for predicting durability in rollover and time at level classification. The deviation in the damage prediction is less than 2% for distances exceeding 5000 km. However, low-frequency data are not suitable for rainflow analysis. Finally, the database size was adjusted to assess the statistical stability of the durability prediction. A larger dataset typically reduces variance. The paper presents evidence for the quality and usability of cloud data in drivetrain durability design. Cloud data from a significant number of customer vehicles can be used for certain analyses of representative customer load collectives, which can reduce development time and costs. [ABSTRACT FROM AUTHOR]

Electric vehicle (EV) is an ideal solution to resolve the carbon emission issue and the fossil fuels scarcity problem in the future. However, a large number of EVs will be concentrated on charging during the valley hours leading to new load peaks under the guidance of static time-of-use tariff. Therefore, this paper proposes a dynamic time-of-use tariff mechanism, which redefines the peak and valley time periods according to the predicted loads using the fuzzy C-mean (FCM) clustering algorithm, and then dynamically adjusts the peak and valley tariffs according to the actual load of each time period. Based on the proposed tariff mechanism, an EV charging optimization model with the lowest cost to the users and the lowest variance of the grid-side load as the objective function is established. Then, a weight selection principle with an equal loss rate of the two objectives is proposed to transform the multi-objective optimization problem into a single-objective optimization problem. Finally, the EV charging load optimization model under three tariff strategies is set up and solved with the mathematical solver GROUBI. The results show that the EV charging load optimization strategy based on the dynamic time-of-use tariff can better balance the benefits between charging stations and users under different numbers and proportions of EVs connected to the grid, and can effectively reduce the grid load variance and improve the grid load curve. [ABSTRACT FROM AUTHOR]