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Developing a Unified Framework for PMSM Speed Regulation: Active Disturbance Rejection Control via Generalized PI Control

Journal Description

World Electric Vehicle Journal

World Electric Vehicle Journal is the first peer-reviewed, international, scientific journal that comprehensively covers all studies related to battery, hybrid, and fuel cell electric vehicles. The journal is owned by the World Electric Vehicle Association (WEVA) and its members, the E-Mobility Europe, Electric Drive Transportation Association (EDTA), and Electric Vehicle Association of Asia Pacific (EVAAP). It has been published monthly online by MDPI since Volume 9, Issue 1 (2018).

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, ESCI (Web of Science), Ei Compendex, and other databases.
Journal Rank: JCR - Q2 (Engineering, Electrical and Electronic) / CiteScore - Q2 (Automotive Engineering)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 17.6 days after submission; acceptance to publication is undertaken in 2.9 days (median values for papers published in this journal in the first half of 2025).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.

Impact Factor: 2.6 (2024)

Imprint Information Journal Flyer Open Access ISSN: 2032-6653

Latest Articles

27 pages, 4703 KiB

Open AccessArticle

Fault Diagnosis Method for Position Sensors in Multi-Phase Brushless DC Motor Drive Systems Based on Position Signals and Fault Current Characteristics

by Jianwen Li, Wei Zhang, Shi Zhang, Wei Chen and Xinmin Li

World Electr. Veh. J. 2025, 16(8), 454; https://doi.org/10.3390/wevj16080454 (registering DOI) - 9 Aug 2025

Multi-phase brushless DC motors (BLDCMs) have broad prospects in the power propulsion systems of electric vehicles, submarines, electric ships, etc., due to their advantages of high efficiency and high power density. In the above application scenarios, accurately obtaining the rotor position information is [...] Read more.

Multi-phase brushless DC motors (BLDCMs) have broad prospects in the power propulsion systems of electric vehicles, submarines, electric ships, etc., due to their advantages of high efficiency and high power density. In the above application scenarios, accurately obtaining the rotor position information is crucial for ensuring the efficient and stable operation of multi-phase BLDCMs. Therefore, by analyzing the fault conditions of position sensors in this paper, a fault diagnosis method for position sensors in multi-phase brushless DC motor drive systems based on position signals and fault current characteristics is proposed, with the aim of improving the reliability of the system. This method utilizes the Hall state value determined by the Hall position signal and the current characteristics under the fault state to achieve rapid fault diagnosis and precise positioning of the position sensor. Its advantage lies in the fact that it does not require additional hardware support or complex calculations, and can efficiently identify the fault conditions of position sensors. To verify the effectiveness of the proposed method, this paper conducts experiments based on a nine-phase brushless DC motor equipped with nine Hall position sensors. The results of steady-state and dynamic experiments show that this method can achieve rapid fault diagnosis and location. Full article

(This article belongs to the Special Issue Design and Control of Electrical Machines in Electric Vehicles, 2nd Edition)

17 pages, 1441 KiB

Open AccessProject Report

The Implementation of the Mechanical System for Automatic Charging of Electric Vehicles: A Project Overview

by Zoltan Kiraly, Ervin Burkus, Tibor Szakall, Akos Odry, Peter Odry and Vladimir Tadic

World Electr. Veh. J. 2025, 16(8), 453; https://doi.org/10.3390/wevj16080453 (registering DOI) - 8 Aug 2025

With the advancement of autonomous and electric vehicles, an increasing demand has been observed for the automatic robot-controlled charging of electric vehicles. The idea of developing such charging stations was raised at several research institutions and universities as early as the 2010s, however [...] Read more.

With the advancement of autonomous and electric vehicles, an increasing demand has been observed for the automatic robot-controlled charging of electric vehicles. The idea of developing such charging stations was raised at several research institutions and universities as early as the 2010s, however the appearance of automatic charging stations with higher Technology Readiness Levels (TRL) can only be dated from 2019 onwards. In most of the developed concepts and solutions, a dedicated parking system is required by vehicle drivers, since the operating range of the robots used for charging is limited. In most cases, solutions do not incorporate robots with unique geometries; instead, proven industrial solutions are applied. The robots in these prototypes are typically installed in a fixed position, similar to industrial applications, and are not mobile. The charging of one vehicle is usually performed by one robot. A high-level summary of the developed mechanical system is presented in this project overview. In this research, an automated, robot-controlled electric vehicle charging system was designed, in which vehicles are parked perpendicularly adjacent to each other, and multiple vehicles are charged using a single collaborative robot. The mechanical system was implemented with a robot mounted on an extendable arm attached to a carriage, which is guided in two directions along rails. In this manner, the automatic charging system is positioned precisely at the parking location of the vehicle to be charged. Full article

19 pages, 7952 KiB

Open AccessArticle

Research on Combined Thermal Management System of Power Battery and Air Conditioning Based on MPC

by Xiaojun Xia, Libo Chen, Yi Huang, Yan Zhang and Xiaoliu Xu

World Electr. Veh. J. 2025, 16(8), 452; https://doi.org/10.3390/wevj16080452 (registering DOI) - 8 Aug 2025

Efficient thermal management of power batteries is critical for the safety of new energy vehicles. In this study, we present a novel combined thermal management system that integrates the battery-cooling system with the air-conditioning system. The system employs model predictive control (MPC) to [...] Read more.

Efficient thermal management of power batteries is critical for the safety of new energy vehicles. In this study, we present a novel combined thermal management system that integrates the battery-cooling system with the air-conditioning system. The system employs model predictive control (MPC) to regulate the battery water pump. To evaluate its performance, the MPC strategy is compared with ON-OFF, PID, and fuzzy control strategies. The system model was established and simulated in a high-temperature environment (40 °C) based on a plug-in hybrid electric vehicle (PHEV). The results demonstrate that the MPC-controlled pump exhibits the fastest response speed, maintaining battery temperature fluctuations within 1 °C, comparable to fuzzy control but with significantly lower power consumption. Specifically, the MPC strategy reduces pump power consumption by 46.3% compared to ON-OFF control, 31.3% compared to PID control, and 36% compared to fuzzy control. Based on a comprehensive evaluation of pump response speed, battery temperature fluctuation, and pump power consumption, MPC exhibits the best overall performance. Full article

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34 pages, 1084 KiB

Open AccessReview

Battery Management System for Electric Vehicles: Comprehensive Review of Circuitry Configuration and Algorithms

by Andrey Kurkin, Alexander Chivenkov, Dmitriy Aleshin, Ivan Trofimov, Andrey Shalukho and Danil Vilkov

World Electr. Veh. J. 2025, 16(8), 451; https://doi.org/10.3390/wevj16080451 - 8 Aug 2025

Electric vehicles (EVs) are the fastest-growing type of transport. Battery packs are a key component in EVs. Modern lithium-ion battery cells are characterized by low self-discharge current, high power density, and durability. At the same time, the battery management system (BMS) plays a [...] Read more.

Electric vehicles (EVs) are the fastest-growing type of transport. Battery packs are a key component in EVs. Modern lithium-ion battery cells are characterized by low self-discharge current, high power density, and durability. At the same time, the battery management system (BMS) plays a pivotal role in ensuring high efficiency and durability of battery cells and packs. The BMS monitors and controls the battery charge and discharge to ensure EV safety and optimum operation. This paper is devoted to analyzing BMS circuitry configurations and algorithms. The analysis includes circuit solutions and algorithms for implementing the main BMS functions, such as parameter monitoring, protection, cell balancing, state estimation, charging and discharging management, communication, and data logging. The paper provides insights into the recent research literature on BMS, and the advantages and disadvantages of methods for implementing BMS functions are compared. The paper also discusses the application of artificial intelligence technologies and aspects of further work on next-generation BMS technologies. Full article

(This article belongs to the Special Issue Smart Battery Systems: Advanced Modeling, State Estimation, Prognostics and Control)

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33 pages, 5099 KiB

Open AccessArticle

Scalable Energy Management Model for Integrating V2G Capabilities into Renewable Energy Communities

by Niccolò Pezzati, Eleonora Innocenti, Lorenzo Berzi and Massimo Delogu

World Electr. Veh. J. 2025, 16(8), 450; https://doi.org/10.3390/wevj16080450 - 7 Aug 2025

To promote a more decentralized energy system, the European Commission introduced the concept of Renewable Energy Communities (RECs). Meanwhile, the increasing penetration of Electric Vehicles (EVs) may significantly increase peak power demand and consumption ramps when charging sessions are left uncontrolled. However, by [...] Read more.

To promote a more decentralized energy system, the European Commission introduced the concept of Renewable Energy Communities (RECs). Meanwhile, the increasing penetration of Electric Vehicles (EVs) may significantly increase peak power demand and consumption ramps when charging sessions are left uncontrolled. However, by integrating smart charging strategies, such as Vehicle-to-Grid (V2G), EV storage can actively support the energy balance within RECs. In this context, this work proposes a comprehensive and scalable model for leveraging smart charging capabilities in RECs. This approach focuses on an external cooperative framework to optimize incentive acquisition and reduce dependence on Medium Voltage (MV) grid substations. It adopts a hybrid strategy, combining Mixed-Integer Linear Programming (MILP) to solve the day-ahead global optimization problem with local rule-based controllers to manage power deviations. Simulation results for a six-month case study, using historical demand data and synthetic charging sessions generated from real-world events, demonstrate that V2G integration leads to a better alignment of overall power consumption with zonal pricing, smoother load curves with a 15.5% reduction in consumption ramps, and enhanced cooperation with a 90% increase in shared power redistributed inside the REC. Full article

(This article belongs to the Special Issue Power and Energy Systems for E-Mobility, 2nd Edition)

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25 pages, 1288 KiB

Open AccessArticle

A Multi-Dimensional Psychological Model of Driver Takeover Safety in Automated Vehicles: Insights from User Experience and Behavioral Moderators

by Ruiwei Li, Xiangyu Li and Xiaoqing Li

World Electr. Veh. J. 2025, 16(8), 449; https://doi.org/10.3390/wevj16080449 - 7 Aug 2025

With the rapid adoption of automated driving systems, ensuring safe and efficient driver takeover has become a crucial challenge for road safety. This study introduces a novel psychological framework for understanding and predicting takeover behavior in conditionally automated vehicles, leveraging an extended Theory [...] Read more.

With the rapid adoption of automated driving systems, ensuring safe and efficient driver takeover has become a crucial challenge for road safety. This study introduces a novel psychological framework for understanding and predicting takeover behavior in conditionally automated vehicles, leveraging an extended Theory of Planned Behavior (TPB) model enriched by real-world driver experience. Drawing on survey data from 385 automated driving system users recruited in Shaoguan City, China, through face-to-face questionnaire administration covering various ADS types (ACC, lane-keeping, automatic parking), we demonstrate that driver attitudes, perceived behavioral control, and subjective norms are significant determinants of takeover intention, collectively explaining nearly half of its variance (R² = 48.7%). Importantly, our analysis uncovers that both intention and perceived behavioral control have robust, direct effects on actual takeover behavior. Crucially, this work is among the first to reveal that individual user characteristics—such as driving experience and ADS (automated driving system) usage frequency—substantially moderate these psychological pathways: experienced or frequent users rely more on perceived control and attitude, while less experienced drivers are more susceptible to social influences. By advancing a multi-dimensional psychological model that integrates personal, social, and experiential moderators, our findings deliver actionable insights for the design of adaptive human–machine interfaces, tailored driver training, and targeted safety interventions in the context of automated driving. Using structural equation modeling with maximum likelihood estimation (χ²/df = 2.25, CFI = 0.941, RMSEA = 0.057), this psychological approach complements traditional engineering models by revealing that takeover behavior variance is explained at 58.3%. Full article

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16 pages, 3989 KiB

Open AccessArticle

Secure Context-Aware Traffic Light Scheduling System: Integrity of Vehicles’ Identities

by Marah Yahia, Maram Bani Younes, Firas Najjar, Ahmad Audat and Said Ghoul

World Electr. Veh. J. 2025, 16(8), 448; https://doi.org/10.3390/wevj16080448 - 7 Aug 2025

Autonomous vehicles and intelligent traffic transportation are widely investigated for road networks. Context-aware traffic light scheduling algorithms determine signal phases by analyzing the real-time characteristics and contextual information of competing traffic flows. The context of traffic flows mainly considers the existence of regular, [...] Read more.

Autonomous vehicles and intelligent traffic transportation are widely investigated for road networks. Context-aware traffic light scheduling algorithms determine signal phases by analyzing the real-time characteristics and contextual information of competing traffic flows. The context of traffic flows mainly considers the existence of regular, emergency, or heavy vehicles. This is an important factor in setting the phases of the traffic light schedule and assigning a high priority for emergency vehicles to pass through the signalized intersection first. VANET technology, through its communication capabilities and the exchange of data packets among moving vehicles, is utilized to collect real-time traffic information for the analyzed road scenarios. This introduces an attractive environment for hackers, intruders, and criminals to deceive drivers and intelligent infrastructure by manipulating the transmitted packets. This consequently leads to the deployment of less efficient traffic light scheduling algorithms. Therefore, ensuring secure communications between traveling vehicles and verifying the integrity of transmitted data are crucial. In this work, we investigate the possible attacks on the integrity of transferred messages and vehicles’ identities and their effects on the traffic light schedules. Then, a new secure context-aware traffic light scheduling system is proposed that guarantees the integrity of transmitted messages and verifies the vehicles’ identities. Finally, a comprehensive series of experiments were performed to assess the proposed secure system in comparison to the absence of security mechanisms within a simulated road intersection. We can infer from the experimental study that attacks on the integrity of vehicles have different effects on the efficiency of the scheduling algorithm. The throughput of the signalized intersection and the waiting delay time of traveling vehicles are highly affected parameters. Full article

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29 pages, 28606 KiB

Open AccessArticle

The Speed of Shared Autonomous Vehicles Is Critical to Their Demand Potential

by Tilmann Schlenther and Kai Nagel

World Electr. Veh. J. 2025, 16(8), 447; https://doi.org/10.3390/wevj16080447 - 7 Aug 2025

Under a 2021 amendment to German law, the KelRide project became the first public on-demand service operating electric autonomous vehicles (AVs) without fixed routes on public roads. This paper addresses two notable gaps in the literature by (1) conducting an ex post evaluation [...] Read more.

Under a 2021 amendment to German law, the KelRide project became the first public on-demand service operating electric autonomous vehicles (AVs) without fixed routes on public roads. This paper addresses two notable gaps in the literature by (1) conducting an ex post evaluation of demand predictions for a non-infrastructure (Mobility-on-Demand (MoD)) project and (2) using real-world data to analyze how demand responds to key Autonomous Mobility-on-Demand (AMoD) system parameters in a rural context. Earlier simulation-based demand forecasts are compared to observed booking data, and the recalibrated model is used to investigate the sensitivity of passenger numbers to vehicle speed, fleet size, service area, operating hours, and idle vehicle positioning. Results show that increasing vehicle speed leads to a superlinear rise in passenger numbers—especially at small fleet sizes—while demand saturates at large fleet sizes. A linear increase in demand is observed with expanding service areas, provided fleet size is sufficient. Extending operating hours from 9 a.m.–4 p.m. to full-day service increases demand by a factor of two to four. Passengers numbers also vary notably depending on the positioning of idle vehicles. Consistent with empirical findings, the analysis underscores that raising AV speed is essential for ensuring the long-term viability of autonomous mobility services. Full article

(This article belongs to the Special Issue Changes in Travel Behavior When Autonomous Vehicles Are Integrated into the Existing Transport System in Urban Cities)

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17 pages, 3637 KiB

Open AccessArticle

Design and Experimental Research on a New Integrated EBS with High Response Speed

by Feng Chen, Zhiquan Fu, Baoxiang Qiu, Xiaoyi Song, Gangqiang Chen, Zhanming Li, Qijiang He, Guo Lu and Xiaoqing Sun

World Electr. Veh. J. 2025, 16(8), 446; https://doi.org/10.3390/wevj16080446 - 7 Aug 2025

With the development of the automotive industry, the performance of commercial vehicle braking systems is crucial for road traffic safety. However, traditional braking systems are no longer able to meet the growing demand for response speed, control accuracy, and adaptability to complex operating [...] Read more.

With the development of the automotive industry, the performance of commercial vehicle braking systems is crucial for road traffic safety. However, traditional braking systems are no longer able to meet the growing demand for response speed, control accuracy, and adaptability to complex operating conditions. To this end, this article focuses on improving the braking performance of commercial vehicles, designs and develops a new integrated high-response-speed EBS, explains its structure and function, proposes a pressure delay compensation control method for wire-controlled braking systems, establishes relevant models, designs control processes, and conducts braking simulations. Braking experiments are also conducted on a commercial 6 × 4 tractor on different road surfaces. The research results show that the system has good braking response performance under typical working conditions such as low adhesion, high adhesion, and opposite docking. The braking time is short (for example, the initial braking time at 40 km/h on high-adhesion roads is only 2.209 s, and the initial braking time at 50 km/h on opposite roads is 6.68 s), and the braking safety performance is superior, meeting the requirements of relevant standards. The contribution of this study lies in the proposed time delay compensation control method for wire-controlled braking, which effectively solves the problem of low control accuracy caused by time delay in wire-controlled braking systems. The integrated EBS designed integrates multiple functions, improves driving safety and comfort, and provides strong support for the upgrade of commercial vehicle braking technology, with good application prospects. Full article

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28 pages, 15106 KiB

Open AccessArticle

A Spatially Aware Machine Learning Method for Locating Electric Vehicle Charging Stations

by Yanyan Huang, Hangyi Ren, Xudong Jia, Xianyu Yu, Dong Xie, You Zou, Daoyuan Chen and Yi Yang

World Electr. Veh. J. 2025, 16(8), 445; https://doi.org/10.3390/wevj16080445 - 6 Aug 2025

The rapid adoption of electric vehicles (EVs) has driven a strong need for optimizing locations of electric vehicle charging stations (EVCSs). Previous methods for locating EVCSs rely on statistical and optimization models, but these methods have limitations in capturing complex nonlinear relationships and [...] Read more.

The rapid adoption of electric vehicles (EVs) has driven a strong need for optimizing locations of electric vehicle charging stations (EVCSs). Previous methods for locating EVCSs rely on statistical and optimization models, but these methods have limitations in capturing complex nonlinear relationships and spatial dependencies among factors influencing EVCS locations. To address this research gap and better understand the spatial impacts of urban activities on EVCS placement, this study presents a spatially aware machine learning (SAML) method that combines a multi-layer perceptron (MLP) model with a spatial loss function to optimize EVCS sites. Additionally, the method uses the Shapley additive explanation (SHAP) technique to investigate nonlinear relationships embedded in EVCS placement. Using the city of Wuhan as a case study, the SAML method reveals that parking site (PS), road density (RD), population density (PD), and commercial residential (CR) areas are key factors in determining optimal EVCS sites. The SAML model classifies these grid cells into no EVCS demand (0 EVCS), low EVCS demand (from 1 to 3 EVCSs), and high EVCS demand (4+ EVCSs) classes. The model performs well in predicting EVCS demand. Findings from ablation tests also indicate that the inclusion of spatial correlations in the model’s loss function significantly enhances the model’s performance. Additionally, results from case studies validate that the model is effective in predicting EVCSs in other metropolitan cities. Full article

(This article belongs to the Special Issue Fast-Charging Station for Electric Vehicles: Challenges and Issues)

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25 pages, 77176 KiB

Open AccessArticle

Advancing Energy Management Strategies for Hybrid Fuel Cell Vehicles: A Comparative Study of Deterministic and Fuzzy Logic Approaches

by Mohammed Essoufi, Mohammed Benzaouia, Bekkay Hajji, Abdelhamid Rabhi and Michele Calì

World Electr. Veh. J. 2025, 16(8), 444; https://doi.org/10.3390/wevj16080444 - 6 Aug 2025

The increasing depletion of fossil fuels and their environmental impact have led to the development of fuel cell hybrid electric vehicles. By combining fuel cells with batteries, these vehicles offer greater efficiency and zero emissions. However, their energy management remains a challenge requiring [...] Read more.

The increasing depletion of fossil fuels and their environmental impact have led to the development of fuel cell hybrid electric vehicles. By combining fuel cells with batteries, these vehicles offer greater efficiency and zero emissions. However, their energy management remains a challenge requiring advanced strategies. This paper presents a comparative study of two developed energy management strategies: a deterministic rule-based approach and a fuzzy logic approach. The proposed system consists of a proton exchange membrane fuel cell (PEMFC) as the primary energy source and a lithium-ion battery as the secondary source. A comprehensive model of the hybrid powertrain is developed to evaluate energy distribution and system behaviour. The control system includes a model predictive control (MPC) method for fuel cell current regulation and a PI controller to maintain DC bus voltage stability. The proposed strategies are evaluated under standard driving cycles (UDDS and NEDC) using a simulation in MATLAB/Simulink. Key performance indicators such as fuel efficiency, hydrogen consumption, battery state-of-charge, and voltage stability are examined to assess the effectiveness of each approach. Simulation results demonstrate that the deterministic strategy offers a structured and computationally efficient solution, while the fuzzy logic approach provides greater adaptability to dynamic driving conditions, leading to improved overall energy efficiency. These findings highlight the critical role of advanced control strategies in improving FCHEV performance and offer valuable insights for future developments in hybrid-vehicle energy management. Full article

(This article belongs to the Special Issue Power and Energy Systems for E-Mobility, 2nd Edition)

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21 pages, 2441 KiB

Open AccessArticle

Reliability Enhancement of Puducherry Smart Grid System Through Optimal Integration of Electric Vehicle Charging Station–Photovoltaic System

by M. A. Sasi Bhushan, M. Sudhakaran, Sattianadan Dasarathan and V. Sowmya Sree

World Electr. Veh. J. 2025, 16(8), 443; https://doi.org/10.3390/wevj16080443 - 6 Aug 2025

Distributed generation strengthens distribution network reliability by placing generators close to load centers. The integration of electric vehicle charging stations (EVCSs) with PV systems mitigates the effects of EV charging burden. In this research, the objective is to combineEVCSs with distributed generation (DG) [...] Read more.

Distributed generation strengthens distribution network reliability by placing generators close to load centers. The integration of electric vehicle charging stations (EVCSs) with PV systems mitigates the effects of EV charging burden. In this research, the objective is to combineEVCSs with distributed generation (DG) units in the Puducherry smart grid system to obtain optimized locations and enhance their reliability. To determine the right nodes for DGs and EVCSs in an uneven distribution network, the modified decision-making (MDM) algorithm and the model predictive control (MPC) approach are used. The Indian utility 29-node distribution network (IN29NDN), which is an unbalanced network, is used for testing. The effects of PV systems and EVCS units are studied in several settings and at various saturation levels. This study validates the correctness of its findings by evaluating the outcomes of proposed methodological approaches. DIgSILENT Power Factory is used to conduct the simulation experiments. The results show that optimizing the location of the DG unit and the size of the PV system can significantly minimize power losses and make a distribution network (DN) more reliable. Full article

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41 pages, 7308 KiB

Open AccessReview

Challenges and Opportunities for Extending Battery Pack Life Using New Algorithms and Techniques for Battery Electric Vehicles

by Pedro S. Gonzalez-Rodriguez, Jorge de J. Lozoya-Santos, Hugo G. Gonzalez-Hernandez, Luis C. Felix-Herran and Juan C. Tudon-Martinez

World Electr. Veh. J. 2025, 16(8), 442; https://doi.org/10.3390/wevj16080442 - 5 Aug 2025

The shift from Internal Combustion Engine Vehicles (ICEVs) to Battery Electric Vehicles (BEVs) has accelerated global efforts to decarbonize transportation. However, battery degradation, high costs, and limited lifespan remain critical barriers. This review synthesizes recent innovations to extend Li-ion battery life in BEVs [...] Read more.

The shift from Internal Combustion Engine Vehicles (ICEVs) to Battery Electric Vehicles (BEVs) has accelerated global efforts to decarbonize transportation. However, battery degradation, high costs, and limited lifespan remain critical barriers. This review synthesizes recent innovations to extend Li-ion battery life in BEVs by exploring advances in degradation modeling, adaptive Battery Management Systems (BMSs), electronic component simulations, and real-world usage profiling. The authors have systematically analyzed over 80 recent studies using a PRISMA-guided review protocol. A novel comparative framework highlights gaps in current literature, particularly regarding real-world driving impacts, ripple current effects, and second-life battery applications. This review article critically compares model-driven, data-driven, and hybrid model approaches, emphasizing trade-offs in interpretability, accuracy, and deployment feasibility. Finally, the review links battery life extension to broader sustainability metrics, including circular economy models and predictive maintenance algorithms. This review offers actionable insights for researchers, engineers, and policymakers aiming to design longer-lasting and more sustainable electric mobility systems. Full article

(This article belongs to the Special Issue Electric Vehicle Battery Pack and Electric Motor Sizing Methods)

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22 pages, 2053 KiB

Open AccessArticle

Enhanced Real-Time Method Traffic Light Signal Color Recognition Using Advanced Convolutional Neural Network Techniques

by Fakhri Yagob and Jurek Z. Sasiadek

World Electr. Veh. J. 2025, 16(8), 441; https://doi.org/10.3390/wevj16080441 - 5 Aug 2025

Real-time traffic light detection is essential for the safe navigation of autonomous vehicles, where timely and accurate recognition of signal states is critical. YOLOv8, a state-of-the-art object detection model, offers enhanced speed and precision, making it well-suited for real-time applications in complex driving [...] Read more.

Real-time traffic light detection is essential for the safe navigation of autonomous vehicles, where timely and accurate recognition of signal states is critical. YOLOv8, a state-of-the-art object detection model, offers enhanced speed and precision, making it well-suited for real-time applications in complex driving environments. This study presents a modified YOLOv8 architecture optimized for traffic light detection by integrating Depth-Wise Separable Convolutions (DWSCs) throughout the backbone and head. The model was first pretrained on a public traffic light dataset to establish a strong baseline and then fine-tuned on a custom real-time dataset consisting of 480 images collected from video recordings under diverse road conditions. Experimental results demonstrate high detection performance, with precision scores of 0.992 for red, 0.995 for yellow, and 0.853 for green lights. The model achieved an average mAP@0.5 of 0.947, with stable F1 scores and low validation losses over 80 epochs, confirming effective learning and generalization. Compared to existing YOLO variants, the modified architecture showed superior performance, especially for red and yellow lights. Full article

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21 pages, 1952 KiB

Open AccessArticle

Research on Consumer Purchase Intention for New Energy Vehicles Based on Text Mining and Bivariate Logit Model: Empirical Evidence from Urumqi, China

by Zhenxiang Hao, Jianping Hu, Jin Ran, Qiong Lu, Yuhang Zheng and Xuetao Zhang

World Electr. Veh. J. 2025, 16(8), 440; https://doi.org/10.3390/wevj16080440 - 5 Aug 2025

This study combines text mining and binary logit model to analyze the main influencing factors of consumers’ purchase intention for new energy vehicles (NEVs) in Urumqi. Through the analysis of 34,561 consumer reviews and 400 valid questionnaire data, the study found that battery [...] Read more.

This study combines text mining and binary logit model to analyze the main influencing factors of consumers’ purchase intention for new energy vehicles (NEVs) in Urumqi. Through the analysis of 34,561 consumer reviews and 400 valid questionnaire data, the study found that battery technology, sales price, and policy support have a significant impact on purchase intention. Based on the differences in consumers’ price sensitivity, technology preference, and policy support, this paper segments consumers into six groups. Based on these findings, we propose policy recommendations to optimize subsidy policies, promote battery technology upgrades, and improve charging infrastructure, in order to drive the development of the NEV market. Full article

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21 pages, 3788 KiB

Open AccessArticle

An Optimization Design Method for Flat-Wire Motors Based on Combined Rotor Slot Structures

by Xiangjun Bi, Hongbin Yin, Yan Chen, Mingyang Luo, Xiaojun Wang and Wenjing Hu

World Electr. Veh. J. 2025, 16(8), 439; https://doi.org/10.3390/wevj16080439 - 4 Aug 2025

To enhance the electromagnetic performance of flat-wire permanent magnet synchronous motors, three different groove structures were designed for the rotor, and a multi-objective optimization algorithm combining a genetic algorithm (GA) with the TOPSIS method was proposed. Firstly, an 8-pole 48-slot flat-wire motor model [...] Read more.

To enhance the electromagnetic performance of flat-wire permanent magnet synchronous motors, three different groove structures were designed for the rotor, and a multi-objective optimization algorithm combining a genetic algorithm (GA) with the TOPSIS method was proposed. Firstly, an 8-pole 48-slot flat-wire motor model was established, and the cogging torque was analytically calculated to compare the motor’s performance under different groove schemes. Secondly, global multi-objective optimization of the rotor groove dimensions was performed using a combined simulation approach involving Maxwell, Workbench, and Optislang, and the optimal rotor groove size structure was selected using the TOPSIS method. Finally, a comparative analysis of the motor’s performance under both rated-load and no-load conditions was conducted for the pre- and post-optimization designs, followed by verification of the mechanical strength of the optimized rotor structure. The research results demonstrate that the combined optimization approach utilizing the genetic algorithm and the TOPSIS method significantly enhances the torque characteristics of the motor. The computational results indicate that the average torque is increased to 165.32 N·m, with the torque ripple reduced from 28.37% to 13.32% and the cogging torque decreased from 896.88 mN·m to 187.9 mN·m. Moreover, the total distortion rates of the air-gap magnetic flux density and the no-load back EMF are significantly suppressed, confirming the rationality of the proposed motor design. Full article

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52 pages, 1100 KiB

Open AccessArticle

The Impact of Renewable Generation Variability on Volatility and Negative Electricity Prices: Implications for the Grid Integration of EVs

by Marek Pavlík, Martin Vojtek and Kamil Ševc

World Electr. Veh. J. 2025, 16(8), 438; https://doi.org/10.3390/wevj16080438 - 4 Aug 2025

The introduction of Renewable Energy Sources (RESs) into the electricity grid is changing the price dynamics of the electricity market and creating room for flexibility on the consumption side. This paper investigates different aspects of the interaction between the RES share, electricity spot [...] Read more.

The introduction of Renewable Energy Sources (RESs) into the electricity grid is changing the price dynamics of the electricity market and creating room for flexibility on the consumption side. This paper investigates different aspects of the interaction between the RES share, electricity spot prices, and electric vehicle (EV) charging strategies. Based on empirical data from Germany, France, and the Czech Republic for the period 2015–2025, four research hypotheses are tested using correlation and regression analysis, cost simulations, and classification algorithms. The results confirm a negative correlation between the RES share and electricity prices, as well as the effectiveness of smart charging in reducing costs. At the same time, it is shown that the occurrence of negative prices is significantly affected by a high RES share. The correlation analysis further suggests that higher production from RESs increases the potential for price optimisation through smart charging. The findings have implications for policymaking aimed at flexible consumption and efficient RES integration. Full article

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32 pages, 3972 KiB

Open AccessArticle

A Review and Case of Study of Cooling Methods: Integrating Modeling, Simulation, and Thermal Analysis for a Model Based on a Commercial Electric Permanent Magnet Synchronous Motor

by Henrry Gabriel Usca-Gomez, David Sebastian Puma-Benavides, Victor Danilo Zambrano-Leon, Ramón Castillo-Díaz, Milton Israel Quinga-Morales, Javier Milton Solís-Santamaria and Edilberto Antonio Llanes-Cedeño

World Electr. Veh. J. 2025, 16(8), 437; https://doi.org/10.3390/wevj16080437 - 4 Aug 2025

The efficiency of electric motors is highly dependent on their operating temperature, with lower temperatures contributing to enhanced performance, reliability, and extended service life. This study presents a comprehensive review of state-of-the-art cooling technologies and evaluates their impact on the thermal behavior of [...] Read more.

The efficiency of electric motors is highly dependent on their operating temperature, with lower temperatures contributing to enhanced performance, reliability, and extended service life. This study presents a comprehensive review of state-of-the-art cooling technologies and evaluates their impact on the thermal behavior of a commercial motor–generator system in high-demand applications. A baseline model of a permanent magnet synchronous motor (PMSM) was developed using MotorCAD 2023^® software, which was supported by reverse engineering techniques to accurately replicate the motor’s physical and thermal characteristics. Subsequently, multiple cooling strategies were simulated under consistent operating conditions to assess their effectiveness. These strategies include conventional axial water jackets as well as advanced oil-based methods such as shaft cooling and direct oil spray to the windings. The integration of these systems in hybrid configurations was also explored to maximize thermal efficiency. Simulation results reveal that hybrid cooling significantly reduces the temperature of critical components such as stator windings and permanent magnets. This reduction in thermal stress improves current efficiency, power output, and torque capacity, enabling reliable motor operation across a broader range of speeds and under sustained high-load conditions. The findings highlight the effectiveness of hybrid cooling systems in optimizing both thermal management and operational performance of electric machines. Full article

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14 pages, 650 KiB

Open AccessArticle

Determining the Spanish Public’s Intention to Adopt Hydrogen Fuel-Cell Vehicles

by Roser Sala, Lila Gonçalves, Hitomi Sato, Ning Huan, Toshiyuki Yamamoto, Dimitrios Tzioutzios and Jose-Blas Navarro

World Electr. Veh. J. 2025, 16(8), 436; https://doi.org/10.3390/wevj16080436 - 4 Aug 2025

Understanding what people think about hydrogen energy and how this influences their acceptance of the associated technology is a critical area of research. The public’s willingness to adopt practical applications of hydrogen energy, such as hydrogen fuel-cell vehicles (HFCVs), is a key factor [...] Read more.

Understanding what people think about hydrogen energy and how this influences their acceptance of the associated technology is a critical area of research. The public’s willingness to adopt practical applications of hydrogen energy, such as hydrogen fuel-cell vehicles (HFCVs), is a key factor in their deployment. To analyse the direct and indirect effects of key attitudinal variables that could influence the intention to use HFCVs in Spain, an online questionnaire was administered to a representative sample of the Spanish population (N = 1000). A path analysis Structural Equation Model (SEM) was applied to determine the effect of different attitudinal variables. A high intention to adopt HFCVs in Spain was found (3.8 out of 5), assuming their wider availability in the future. The path analysis results indicated that general acceptance of hydrogen technology and perception of its benefits had the greatest effect on the public’s intention to adopt HFCVs. Regarding indirect effects, the role of trust in hydrogen technology was notable, having significant mediating effects not only through general acceptance of hydrogen energy and local acceptance of hydrogen refuelling stations (HRS), but also through positive and negative emotions and benefits perception. The findings will assist in focusing the future hydrogen communication strategies of both the government and the private (business) sector. Full article

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24 pages, 4441 KiB

Open AccessArticle

Simulation of Trip Chains in a Metropolitan Area to Evaluate the Energy Needs of Electric Vehicles and Charging Demand

by Pietro Antonio Centrone, Giuseppe Brancaccio and Francesco Deflorio

World Electr. Veh. J. 2025, 16(8), 435; https://doi.org/10.3390/wevj16080435 - 4 Aug 2025

The typical ranges available for electric vehicles (EVs) may be considered by users to be inadequate when compared to long, real-life trips, and charging operations may need to be planned along journeys. To evaluate the compatibility between vehicle features and charging options for [...] Read more.

The typical ranges available for electric vehicles (EVs) may be considered by users to be inadequate when compared to long, real-life trips, and charging operations may need to be planned along journeys. To evaluate the compatibility between vehicle features and charging options for realistic journeys performed by car, a simulation approach is proposed here, using travel data collected from real vehicles to obtain trip chains for multiple consecutive days. Car travel activities, including stops with the option of charging, were simulated by applying an agent-based approach. Charging operations can be integrated into trip chains for user activities, assuming that they remain unchanged in the event that vehicles switch to electric. The energy consumption of the analyzed trips, disaggregated by vehicle type, was estimated using the average travel speed, which is useful for capturing the main route features (ranging from urban to motorways). Data were recorded for approximately 25,000 vehicles in the Turin Metropolitan Area for six consecutive days. Market segmentation of the vehicles was introduced to take into consideration different energy consumption rates and charging times, given that the electric power, battery size, and consumption rate can be related to the vehicle category. Charging activities carried out using public infrastructure during idle time between consecutive trips, as well as those carried out at home or work, were identified in order to model different needs. Full article

(This article belongs to the Special Issue Exploring Beyond Electric Cars: Comprehensive Charging Solutions and Innovations in Land, Sea, and Air Mobility)

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