World Electric Vehicle Journal

16 pages, 3170 KiB

Open AccessArticle

Improvement in Pavement Defect Scenarios Using an Improved YOLOv10 with ECA Attention, RefConv and WIoU

by Xiaolin Zhang, Lei Lu, Hanyun Luo and Lei Wang

World Electr. Veh. J. 2025, 16(6), 328; https://doi.org/10.3390/wevj16060328 - 13 Jun 2025

This study addresses challenges such as multi-scale defects, varying lighting, and irregular shapes by proposing an improved YOLOv10 model that integrates the ECA attention mechanism, RefConv feature enhancement module, and WIoU loss function for complex pavement defect detection. The RefConv dual-branch structure achieves [...] Read more.

This study addresses challenges such as multi-scale defects, varying lighting, and irregular shapes by proposing an improved YOLOv10 model that integrates the ECA attention mechanism, RefConv feature enhancement module, and WIoU loss function for complex pavement defect detection. The RefConv dual-branch structure achieves feature complementarity between local details and global context (mAP increased by 2.1%), the ECA mechanism models channel relationships using 1D convolution (small-object recall rate increased by 27%), and the WIoU loss optimizes difficult sample regression through a dynamic weighting mechanism (location accuracy improved by 37%). Experiments show that on a dataset constructed from 23,949 high-resolution images, the improved model’s mAP reaches 68.2%, which is an increase of 6.2% compared to the baseline YOLOv10, maintaining a stable recall rate of 83.5% in highly reflective and low-light scenarios, with an inference speed of 158 FPS (RTX 4080), providing a high-precision real-time solution for intelligent road inspection. Full article

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14 pages, 469 KiB

Open AccessReview

Translation of Electric Vehicle Research into Education

by Kwok-Tong Chau, Tianyi Liu, Wei Liu, Shuangxia Niu and Ching-Chuen Chan

World Electr. Veh. J. 2025, 16(6), 327; https://doi.org/10.3390/wevj16060327 - 13 Jun 2025

Abstract

Electric vehicles (EVs) are one of the most important technological innovations that can save the environment. Over the years, there has been substantial EV research, which has been successfully transformed into EV products, leading to the recent commercialization and popularization of EVs. Nevertheless, [...] Read more.

Electric vehicles (EVs) are one of the most important technological innovations that can save the environment. Over the years, there has been substantial EV research, which has been successfully transformed into EV products, leading to the recent commercialization and popularization of EVs. Nevertheless, the translation of EV research into EV education is lagging behind the technology transfer from EV research to EV products and is quite ad hoc in nature. In this paper, an overview of translating EV research into EV education is presented, which is systematically categorized into individual EV education, classroom EV education and professional EV education. Then, relevant surveys are conducted and discussed. Finally, some findings and suggestions are given to enhance the translation of EV research into EV education. Full article

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17 pages, 2972 KiB

Open AccessArticle

An Improved Extraction Scheme for High-Frequency Injection in the Realization of Effective Sensorless PMSM Control

by Indra Ferdiansyah and Tsuyoshi Hanamoto

World Electr. Veh. J. 2025, 16(6), 326; https://doi.org/10.3390/wevj16060326 - 11 Jun 2025

Abstract

High-frequency (HF) injection is a widely used technique for low-speed implementation of position sensorless permanent magnet synchronous motor control. A key component of this technique is the tracking loop control system, which extracts rotor position error and utilizes proportional–integral regulation as a position [...] Read more.

High-frequency (HF) injection is a widely used technique for low-speed implementation of position sensorless permanent magnet synchronous motor control. A key component of this technique is the tracking loop control system, which extracts rotor position error and utilizes proportional–integral regulation as a position observer for estimating the rotor position. Generally, this process relies on band-pass filters (BPFs) and low-pass filters (LPFs) to modulate signals in the quadrature current to obtain rotor position error information. However, limitations in filter accuracy and dynamic response lead to prolonged convergence times and timing inconsistencies in the estimation process, which affects real-time motor control performance. To address these issues, this study proposes an exponential moving average (EMA)-based scheme for rotor position error extraction, offering a rapid response under dynamic conditions such as direction reversals, step speed changes, and varying loads. EMA is used to pass the original rotor position information carried by the quadrature current signal, which contains HF components, with a specified smoothing factor. Then, after the synchronous demodulation process, EMA is employed to extract rotor position error information for the position observer to estimate the rotor position. Due to its computational simplicity and fast response in handling dynamic conditions, the proposed method can serve as an alternative to BPF and LPF, which are commonly used for rotor position information extraction, while also reducing computational burden and improving performance. Finally, to demonstrate its feasibility and effectiveness in improving rotor position estimation accuracy, the proposed system is experimentally validated by comparing it with a conventional system. Full article

(This article belongs to the Special Issue Permanent Magnet Motors and Driving Control for Electric Vehicles)

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18 pages, 5033 KiB

Open AccessArticle

Research on Multi-Target Detection and Tracking of Intelligent Vehicles in Complex Traffic Environments Based on Deep Learning Theory

by Xuewen Chen, Shilong Yan and Chenxi Xia

World Electr. Veh. J. 2025, 16(6), 325; https://doi.org/10.3390/wevj16060325 - 11 Jun 2025

Abstract

To address the issues of missed detections and false detections of small target missed detections caused by dense occlusion in complex traffic environments, a non-maximum suppression method, Bot-NMS, is proposed to achieve accurate prediction and localization of occluded targets. In the backbone network [...] Read more.

To address the issues of missed detections and false detections of small target missed detections caused by dense occlusion in complex traffic environments, a non-maximum suppression method, Bot-NMS, is proposed to achieve accurate prediction and localization of occluded targets. In the backbone network of YOLOv7, the Ghost module, the ECA attention mechanism, and the multi-scale feature detection structure are introduced to enhance the network’s capacity to learn small target features. The SCSTD and KITTI datasets were used to train and test the improved YOLOv7 target detection network model. The results demonstrate that the improved YOLOv7 method significantly enhances the recall rate and detection accuracy of various targets. A multi-target tracking method based on target re-identification (ReID) is proposed. Utilizing deep learning theory, a ReID model for target identification is constructed to comprehensively capture global and foreground target features. By establishing the correlation cost matrix of the cosine distance and IoU overlap, the correlation between target detection objects, the tracking trajectory, and ReID feature similarity is realized. The VERI-776 vehicle re-identification dataset and MARKET1501 pedestrian re-identification dataset were used to train the proposed ReID model, and multi-target tracking performance comparison experiments were conducted on the MOT16 dataset. The results show that the multi-target tracking method by introducing the ReID model and improving the cost matrix can better deal with the dense occlusion of the target, and can effectively and accurately track the road target in the realistic complex traffic environment. Full article

(This article belongs to the Special Issue Recent Advances in Intelligent Vehicle)

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29 pages, 973 KiB

Open AccessArticle

Connected Vehicles Security: A Lightweight Machine Learning Model to Detect VANET Attacks

by Muawia A. Elsadig, Abdelrahman Altigani, Yasir Mohamed, Abdul Hakim Mohamed, Akbar Kannan, Mohamed Bashir and Mousab A. E. Adiel

World Electr. Veh. J. 2025, 16(6), 324; https://doi.org/10.3390/wevj16060324 - 11 Jun 2025

Abstract

Vehicular ad hoc networks (VANETs) aim to manage traffic, prevent accidents, and regulate various parts of traffic. However, owing to their nature, the security of VANETs remains a significant concern. This study provides insightful information regarding VANET vulnerabilities and attacks. It investigates a [...] Read more.

Vehicular ad hoc networks (VANETs) aim to manage traffic, prevent accidents, and regulate various parts of traffic. However, owing to their nature, the security of VANETs remains a significant concern. This study provides insightful information regarding VANET vulnerabilities and attacks. It investigates a number of security models that have recently been introduced to counter VANET security attacks with a focus on machine learning detection methods. This confirms that several challenges remain unsolved. Accordingly, this study introduces a lightweight machine learning model with a gain information feature selection method to detect VANET attacks. A balanced version of the well-known and recent dataset CISDS2017 was developed by applying a random oversampling technique. The developed dataset was used to train, test, and evaluate the proposed model. In other words, two layers of enhancements were applied—using a suitable feature selection technique and fixing the dataset imbalance problem. The results show that the proposed model, which is based on the Random Forest (RF) classifier, achieved excellent performance in terms of classification accuracy, computational cost, and classification error. It achieved an accuracy rate of 99.8%, outperforming all benchmark classifiers, including AdaBoost, decision tree (DT), K-nearest neighbors (KNNs), and multi-layer perceptron (MLP). To the best of our knowledge, this model outperforms all the existing classification techniques. In terms of processing cost, it consumes the least processing time, requiring only 69%, 59%, 35%, and 1.4% of the AdaBoost, DT, KNN, and MLP processing times, respectively. It causes negligible classification errors. Full article

(This article belongs to the Special Issue Internet of Vehicles and Autonomous Connected Vehicle: Privacy and Security)

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27 pages, 1103 KiB

Open AccessArticle

Leveraging Fuzzy Set Qualitative Comparative Analysis to Explore Determinants of Intention to Use Self-Driving Vehicles in Ghana

by Nelson Opoku-Mensah, Zhiguang Qin, Evans Opoku-Mensah and Shadrach Twumasi Ankrah

World Electr. Veh. J. 2025, 16(6), 323; https://doi.org/10.3390/wevj16060323 - 10 Jun 2025

Abstract

The transformative potential of self-driving vehicles (SDVs) in enhancing mobility and transportation safety is well documented, yet their adoption in developing countries remains understudied. While existing research has primarily focused on SDV adoption in developed nations using variance-based methods, limited attention has been [...] Read more.

The transformative potential of self-driving vehicles (SDVs) in enhancing mobility and transportation safety is well documented, yet their adoption in developing countries remains understudied. While existing research has primarily focused on SDV adoption in developed nations using variance-based methods, limited attention has been given to understanding how multiple factors interact to influence adoption decisions in developing economies. This study addresses this gap by examining the determinants of SDV adoption intention in Ghana using fuzzy set qualitative comparative analysis (fsQCA). Drawing on the Technology Acceptance Model and incorporating additional constructs of perceived reliability, technological competence, and perceived risk, the study analyzed survey data from 1248 respondents across Ghana’s 16 regions. The findings reveal multiple pathways to high adoption intention, with the most effective combination being perceived reliability, perceived ease of use, and technological competence working together. For low adoption intention, two main configurations emerged, both highlighting how the combination of low technological competence and high perceived risk significantly hinders adoption. These findings provide valuable insights for policymakers and stakeholders in developing economies, emphasizing the need for targeted interventions that address both technological and socio-cultural factors influencing SDV adoption. Full article

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14 pages, 2404 KiB

Open AccessArticle

The Development of a 1 kW Mid-Range Wireless Power Transfer Platform for Autonomous Guided Vehicle Applications Using an LCC-S Resonant Compensator

by Worapong Pairindra, Suwaphit Phongsawat, Teeraphon Phophongviwat and Surin Khomfoi

World Electr. Veh. J. 2025, 16(6), 322; https://doi.org/10.3390/wevj16060322 - 9 Jun 2025

Abstract

This study presents the development, simulation, and hardware implementation of a 48 V, 1 kW mid-range wireless power transfer (WPT) platform for autonomous guided vehicle (AGV) charging in industrial applications. The system uses an LCC-S compensation topology, selected for its ability to maintain [...] Read more.

This study presents the development, simulation, and hardware implementation of a 48 V, 1 kW mid-range wireless power transfer (WPT) platform for autonomous guided vehicle (AGV) charging in industrial applications. The system uses an LCC-S compensation topology, selected for its ability to maintain a constant output voltage and deliver high efficiency even under load variations at a typical coil distance of 15 cm. It can also operate at different distances by adjusting the compensator circuit. A proportional–integral (PI) controller is implemented for current regulation, offering a practical, low-cost solution well suited to industrial embedded systems. Compared to advanced control strategies, the PI controller provides sufficient accuracy with minimal computational demand, enabling reliable operation in real-world environments. Current adjustment can be dynamically carried out in response to real-time changes and continuously monitored based on the AGV battery’s state of charge (SOC). Simulation and experimental results validate the system’s performance, achieving over 80% efficiency and demonstrating its feasibility for scalable, robust AGV charging in Industry 4.0 Manufacturing Settings. Full article

(This article belongs to the Special Issue Wireless Power Transfer Technology for Electric Vehicles)

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21 pages, 3497 KiB

Open AccessArticle

Structural Optimization Design and Analysis of Interior Permanent Magnet Synchronous Motor with Low Iron Loss Based on the Adhesive Lamination Process

by Liyan Guo, Huatuo Zhang, Xinmai Gao, Ying Zhou, Yan Cheng and Huimin Wang

World Electr. Veh. J. 2025, 16(6), 321; https://doi.org/10.3390/wevj16060321 - 9 Jun 2025

Abstract

The interior permanent magnet synchronous motors (IPMSMs) are extensively applied in the field of new energy vehicles due to their high-power density and excellent performance control. However, the iron loss has a significant impact on their performance. This study conducts an optimization analysis [...] Read more.

The interior permanent magnet synchronous motors (IPMSMs) are extensively applied in the field of new energy vehicles due to their high-power density and excellent performance control. However, the iron loss has a significant impact on their performance. This study conducts an optimization analysis on the processing technology of silicon steel sheets and motor structure, targeting the reduction of iron loss and the improvement of the motor’s integrated efficiency. Firstly, the influences of two iron core processing technologies on iron loss, namely gluing and welding, are compared. Through experimental tests, it is found that the iron loss density of the gluing process is lower than that of the welding process, and as the magnetic flux density increases, the difference between the two is expanding. Therefore, the iron loss test data from the adhesive process are employed to develop a variable-coefficient iron loss model, enabling precise calculation of the motor’s iron loss. On this basis, aiming at the problem of excessive iron loss of the motor, a novel topological structure of the stator and rotor is proposed. With the optimization goal of reducing the motor iron loss and taking the connection port of the air magnetic isolation slot and the gap of the stator module as the optimization variables, the optimized design of the IPMSM with low iron loss is achieved based on the Taguchi method. After optimization, the stator iron loss decreases by 13.60%, the rotor iron loss decreases by 20.14%, and the total iron loss is reduced by 15.34%. The optimization scheme takes into account both the electromagnetic performance and the process feasibility, it offers technical backing for the high-efficiency operation of new energy vehicle drive motors. Full article

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

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36 pages, 2787 KiB

Open AccessReview

A Comprehensive Analysis Perspective on Path Optimization of Multimodal Electric Transportation Vehicles: Problems, Models, Methods and Future Research Directions

by Wenxin Li and Yuhonghao Wang

World Electr. Veh. J. 2025, 16(6), 320; https://doi.org/10.3390/wevj16060320 - 9 Jun 2025

Abstract

Multimodal transport refers to the integrated transportation in a logistics system in the form of multiple transportation modes, such as highway, railway, waterway, etc. In recent years, the deep integration of electric trucks and route optimization has significantly improved the cost-effectiveness and operational [...] Read more.

Multimodal transport refers to the integrated transportation in a logistics system in the form of multiple transportation modes, such as highway, railway, waterway, etc. In recent years, the deep integration of electric trucks and route optimization has significantly improved the cost-effectiveness and operational efficiency of multimodal transportation. It has provided strong support for the sustainable development of the logistics system. Based on whether to consider low-carbon requirements, uncertainty, and special cargo transportation, the literature is divided into five areas: traditional multimodal transport path optimization, multimodal transport path optimization considering low-carbon requirements, multimodal transport path optimization considering uncertainty, multimodal transport path optimization considering low-carbon requirements and uncertainty, and multimodal transport path optimization considering special transport needs. In this paper, we searched the literature on multimodal path optimization after 2016 in WOS (Web of Science) and CNKI (China National Knowledge Infrastructure), and found that the number of publications in 2024 is three times that in 2016. We collected 130 relevant studies to summarize the current state of research. Finally, with the development of multimodal transport to collaborative transport and the improvement of the application of in-depth learning in different fields, the research mainly focuses on two future research directions: collaborative transport and the use of in-depth learning to solve uncertain problems, and combining it with the problem of multimodal transport route optimization to explore more efficient and perfect transport solutions. Full article

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32 pages, 445 KiB

Open AccessArticle

Manufacturing Competency from Local Clusters: Roots of the Competitive Advantage of the Chinese Electric Vehicle Battery Industry

by Wei Zhao and Boy Luethje

World Electr. Veh. J. 2025, 16(6), 319; https://doi.org/10.3390/wevj16060319 - 9 Jun 2025

Abstract

China’s leading development of a complete battery value chain for electric vehicles (EVs) is restructuring the global automotive sector. In contrast with the normal point of view, which emphasizes the role of industrial policy, this article argues that the competitive advantage of China’s [...] Read more.

China’s leading development of a complete battery value chain for electric vehicles (EVs) is restructuring the global automotive sector. In contrast with the normal point of view, which emphasizes the role of industrial policy, this article argues that the competitive advantage of China’s EV battery industry lies in firms’ core competency and political economic geography. Based on first-hand empirical material and data obtained from years of fieldwork carried out at an EV battery cluster in south China, this paper identifies the Chinese EV battery industry’s core competency and details how it is built up from below. The current core competency of Chinese battery firms is their mass manufacturing capability, which allows them to supply vehicle manufacturers (OEMs) with lithium-ion batteries of stable and consistent quality at competitive prices. This competency is acquired by firms through technological learning at the workshop level while making use of the experiences they have accumulated while mass producing batteries for consumer electronics sectors. Furthermore, the rapid learning and accumulation of knowledge of battery manufacturing on a large scale is also facilitated by the local industrial cluster environment where firms are embedded. Supported and promoted by local government policies, Chinese EV battery clusters are composed of firms from different segments of a complete battery value chain. The findings have significant implications for battery and car makers in global competition as well as for national and local governments which aim to promote EV battery development. Full article

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24 pages, 1806 KiB

Open AccessArticle

A Framework for Optimal Sizing of Heavy-Duty Electric Vehicle Charging Stations Considering Uncertainty

by Rafi Zahedi, Rachel Sheinberg, Shashank Narayana Gowda, Kourosh SedghiSigarchi and Rajit Gadh

World Electr. Veh. J. 2025, 16(6), 318; https://doi.org/10.3390/wevj16060318 - 8 Jun 2025

Abstract

The adoption of heavy-duty electric vehicles (HDEVs) is key to achieving transportation decarbonization. A major component of this transition is the need for new supporting infrastructure: electric charging stations (CSs). HDEV CSs must be planned considering charging requirements, economic constraints, the rollout plan [...] Read more.

The adoption of heavy-duty electric vehicles (HDEVs) is key to achieving transportation decarbonization. A major component of this transition is the need for new supporting infrastructure: electric charging stations (CSs). HDEV CSs must be planned considering charging requirements, economic constraints, the rollout plan for HDEVs, and local utility grid conditions. Together, these considerations highly differentiate HDEV CS planning from light-duty CS planning. This paper addresses the challenges of HDEV CS planning by presenting a framework for determining the optimal sizing of multiple HDEV CSs using a multi-period expansion model. The framework uses historical data from depots and applies a mixed-approach optimization solver to determine the optimal sizes of two types of CSs: one that relies entirely on power generated by a PV system with local battery storage, and another that relies entirely on utility grid power supply. A two-layer uncertainty model is proposed to account for variations in PV power generation, HDEV arrival/departure times, and charger failures. The multi-period expansion strategy achieves up to a 78% reduction in total annual costs during the first deployment period, compared to fully expanded CSs. Full article

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

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14 pages, 1425 KiB

Open AccessArticle

Multi-Parameter Optimization of Angle Transmission Ratio of Steer-by-Wire Vehicle

by Wenguang Liu, Suo Liu, Huajun Che, Xi Liu and Hua Ding

World Electr. Veh. J. 2025, 16(6), 317; https://doi.org/10.3390/wevj16060317 - 8 Jun 2025

Abstract

Aiming at the problem of the insufficient stability of the unified model of steering angle transmission ratio at high speeds, we introduce a novel control strategy that integrates the yaw rate gain, lateral acceleration gain, vehicle speed and steering wheel angle, achieving great [...] Read more.

Aiming at the problem of the insufficient stability of the unified model of steering angle transmission ratio at high speeds, we introduce a novel control strategy that integrates the yaw rate gain, lateral acceleration gain, vehicle speed and steering wheel angle, achieving great improvements in a simulation. The new control strategy uses a genetic algorithm to optimize the yaw rate and lateral acceleration gain values at different speeds, and the two are weighted. The ideal variable-angle transmission ratio control strategy is designed by using the unified model of steering angle transmission ratio at different speed intervals. The simulation results show that the strategy reduces the steering wheel angle peak by 67.12% compared with the fixed-angle transmission at low speeds. Compared with the unified model of steering angle transmission ratio at high speeds, the peak values of the yaw rate, the lateral acceleration and sideslip angle of the vehicle are reduced by 7%, 5.67% and 11.67%, respectively, which effectively improves the steering stability of the vehicle. Full article

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12 pages, 1494 KiB

Open AccessArticle

Fast Battery Capacity Estimation Method Based on State of Charge and IC Curve Peak Value

by Zhenyang Dai, Bixiong Huang, Xintian Liu and Dong Liu

World Electr. Veh. J. 2025, 16(6), 316; https://doi.org/10.3390/wevj16060316 - 5 Jun 2025

Abstract

How to use efficient and accurate methods to estimate the capacity of lithium batteries has always been an important research topic. Traditional capacity estimation methods are time-consuming and require strict experimental conditions, making them unsuitable for real-time applications. This article introduces the concept [...] Read more.

How to use efficient and accurate methods to estimate the capacity of lithium batteries has always been an important research topic. Traditional capacity estimation methods are time-consuming and require strict experimental conditions, making them unsuitable for real-time applications. This article introduces the concept of the inflection point of the charge/discharge curve in the SOC-V curve and proposes a fast estimation method for battery capacity by combining the advantages of the IC curve peak and SOC inflection point methods. By analyzing the charge and discharge data of grouped batteries, it was found that there is a certain correspondence between the inflection point of the SOC-V curve and the peak point of the IC curve. This relationship remains stable during battery aging and can provide a reliable basis for battery SOH evaluation, further improving the estimation accuracy of SOH. This method significantly reduces experimental time, is more suitable for practical applications, and provided an efficient and practical technical means for battery performance evaluation. Full article

(This article belongs to the Special Issue Electrochemical and Thermal Modeling of Batteries for Electric Vehicle)

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29 pages, 6105 KiB

Open AccessReview

A Review of Control Strategies for Four-Switch Buck–Boost Converters

by Guanzheng Lin, Yan Li and Zhaoyun Zhang

World Electr. Veh. J. 2025, 16(6), 315; https://doi.org/10.3390/wevj16060315 - 5 Jun 2025

Abstract

In order to meet the demand for high-voltage architectures of 400 V and 800 V in electric vehicle systems, high-power DC-DC converters have become a key focus of research. The Four-Switch Buck–Boost converter has gained widespread application due to its wide voltage conversion [...] Read more.

In order to meet the demand for high-voltage architectures of 400 V and 800 V in electric vehicle systems, high-power DC-DC converters have become a key focus of research. The Four-Switch Buck–Boost converter has gained widespread application due to its wide voltage conversion range, consistent input and output polarity, and the capability of bidirectional power transfer. This paper focuses on the energy conversion requirements in high-voltage scenarios for electric vehicles, analyzing the working principle of this converter and typical control strategies. It summarizes the issues encountered under different control strategies and presents improvements. Hard-switching multi-mode control strategies aim to improve control algorithms and logic to mitigate large duty cycle variations and voltage gain discontinuities caused by dead zones. For control strategies based on controlling the inductor current to achieve soft-switching, the discussion mainly focuses on optimizing the implementation of soft-switching, reducing overall system losses, and improving the computation speed. Finally, the paper summarizes FSBB control strategies and outlines future directions, providing theoretical support for high-voltage fast charging and onboard power supplies in electric vehicles. Full article

(This article belongs to the Special Issue Power Electronics for Electric Vehicles)

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15 pages, 331 KiB

Open AccessArticle

A Competency Framework for Electric Vehicle Maintenance Technicians: Addressing the Environmental, Social, and Governance (ESG) Imperatives of the BEV Industry

by Hsiu-Chou Yu, Tzu-Ju Hsueh, Ting-Yi Wu, Chang Liu, Chin-Wen Liao and Yi-Kai Fu

World Electr. Veh. J. 2025, 16(6), 314; https://doi.org/10.3390/wevj16060314 - 5 Jun 2025

Abstract

The fast expanding market of battery electric vehicles (BEVs) demands industry-specific competence requirements for maintenance technicians. We have therefore generated a knowledge structure of BEV maintenance through a literature review and expert consensus. Consensus was achieved following a Delphi study of 15 industry [...] Read more.

The fast expanding market of battery electric vehicles (BEVs) demands industry-specific competence requirements for maintenance technicians. We have therefore generated a knowledge structure of BEV maintenance through a literature review and expert consensus. Consensus was achieved following a Delphi study of 15 industry experts through three rounds of refining a broad initial list of competencies. The resulting framework consists of four core competency categories (Professional Knowledge, Professional Skills, Professional Attitude, and Personal Qualities), which are further divided into a total of 24 subcategories and 106 specific indicators that define the boundary of professional skill as well as core skill essentials. This approved tool can be used strategically for workforce grooming, curriculum design for training, and performance assessment in BEV maintenance to ensure that technical workforce capabilities are in line with sustainable mobility targets of the industry. Full article

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21 pages, 3758 KiB

Open AccessArticle

Driving-Cycle-Adaptive Energy Management Strategy for Hybrid Energy Storage Electric Vehicles

by Zhaocheng Lu, Tiezhu Zhang, Rui Li and Xinyu Ni

World Electr. Veh. J. 2025, 16(6), 313; https://doi.org/10.3390/wevj16060313 - 4 Jun 2025

Abstract

The energy management strategy (EMS) is a critical technology for pure electric vehicles equipped with hybrid energy storage systems. This study addresses the challenges of limited adaptability to driving cycles and significant battery capacity degradation in lithium battery–supercapacitor hybrid energy storage systems by [...] Read more.

The energy management strategy (EMS) is a critical technology for pure electric vehicles equipped with hybrid energy storage systems. This study addresses the challenges of limited adaptability to driving cycles and significant battery capacity degradation in lithium battery–supercapacitor hybrid energy storage systems by proposing an adaptive EMS based on Dynamic Programming-Optimized Control Rules (DP-OCR). Dynamic programming is employed to optimize the rule-based control strategy, while the grey wolf optimizer (GWO) is utilized to enhance the least squares support vector machine (LSSVM) driving cycle recognition model. The optimized driving cycle recognition model is integrated with the improved rule-based control strategy, facilitating adaptive adjustment of control parameters based on driving cycle identification results. This integration enables optimal power distribution between lithium batteries and supercapacitors, thereby improving the EMS’s adaptability to varying driving conditions and extending battery lifespan. Simulation results under complex driving cycles indicate that, compared to conventional deterministic rule-based EMS and single-battery vehicles, the proposed DP-OCR-based adaptive EMS reduces overall energy consumption by 8.29% and 17.48%, respectively. Full article

(This article belongs to the Special Issue Electric Vehicle Technology Development, Energy and Environmental Implications, and Decarbonization: 2nd Edition)

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28 pages, 3215 KiB

Open AccessArticle

Optimization of Solar Generation and Battery Storage for Electric Vehicle Charging with Demand-Side Management Strategies

by César Berna-Escriche, Lucas Álvarez-Piñeiro and David Blanco

World Electr. Veh. J. 2025, 16(6), 312; https://doi.org/10.3390/wevj16060312 - 3 Jun 2025

Abstract

The integration of Electric Vehicles (EVs) with solar power generation is important for decarbonizing the economy. While electrifying transportation reduces Greenhouse Gas (GHG) emissions, its success depends on ensuring that EVs are charged with clean energy, requiring significant increases in photovoltaic capacity and [...] Read more.

The integration of Electric Vehicles (EVs) with solar power generation is important for decarbonizing the economy. While electrifying transportation reduces Greenhouse Gas (GHG) emissions, its success depends on ensuring that EVs are charged with clean energy, requiring significant increases in photovoltaic capacity and robust Demand-Side Management (DSM) solutions. EV charging patterns, such as home, workplace, and public charging, need adapted strategies to match solar generation. This study analyzes a system designed to meet a unitary hourly average energy demand (8760 MWh annually) using an optimization framework that balances PV capacity and battery storage to ensure reliable energy supply. Historical solar data from 22 years is used to analyze seasonal and interannual fluctuations. The results show that solar PV alone can cover around 30% of the demand without DSM, rising to nearly 50% with aggressive DSM measures, using PV capacities of 1.0–2.0 MW. The optimization reveals that incorporating battery storage can achieve near 100% coverage with PV power of 8.0–9.0 MW. Moreover, DSM reduces required storage from 18 to about 10 MWh. These findings highlight the importance of integrating optimization-based energy management strategies to enhance system efficiency and cost-effectiveness, offering a pathway toward a more sustainable and resilient EV charging infrastructure. Full article

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

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19 pages, 1292 KiB

Open AccessArticle

Green Technology Innovation Efficiency of New Energy Vehicles Based on Corporate Profitability Perspective

by Chunqian Zhu, Zhongshuai Wang and Yawei Xue

World Electr. Veh. J. 2025, 16(6), 311; https://doi.org/10.3390/wevj16060311 - 3 Jun 2025

Abstract

In the context of global climate change and the escalating energy crisis, the development of new energy vehicles (NEVs) has become a critical strategy for China to foster green transformation and achieve its carbon neutrality goals. This study focuses on A-share-listed NEV companies [...] Read more.

In the context of global climate change and the escalating energy crisis, the development of new energy vehicles (NEVs) has become a critical strategy for China to foster green transformation and achieve its carbon neutrality goals. This study focuses on A-share-listed NEV companies in China from 2015 to 2023, specifically those listed on the Shanghai or Shenzhen Stock Exchange and subject to domestic regulatory standards and disclosure requirements. These firms were selected due to the representativeness, availability, and quantifiability of their data. A super-efficient-network SBM model based on undesirable outputs and the Malmquist index were employed to assess the static and dynamic green technology innovation efficiency of 260 NEV enterprises. Additionally, the Tobit regression model was applied to analyze the influencing factors. The findings reveal that the overall green technology innovation efficiency of Chinese NEV enterprises is relatively low and has exhibited a declining trend over the years. Furthermore, the efficiency of enterprises in the western regions surpasses that of those in the eastern and central regions. Key factors, including government support, enterprise scale, and R&D investment, significantly inhibit the green technology innovation efficiency of firms. Based on these findings, this paper recommends prioritizing the innovation of core technologies, addressing regional disparities in development, and implementing tailored policies to enhance the green technology innovation efficiency and economic performance of NEV enterprises. Full article

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36 pages, 1612 KiB

Open AccessArticle

Quantum-Inspired Hyperheuristic Framework for Solving Dynamic Multi-Objective Combinatorial Problems in Disaster Logistics

by Kassem Danach, Hassan Harb, Louai Saker and Ali Raad

World Electr. Veh. J. 2025, 16(6), 310; https://doi.org/10.3390/wevj16060310 - 2 Jun 2025

Abstract

Disaster logistics presents a highly complex decision-making challenge under conditions of uncertainty, where the timely and efficient allocation of scarce resources is essential to minimize human suffering. In this context, we propose a novel Quantum-Inspired Hyperheuristic Framework (QHHF) designed to solve Dynamic Multi-Objective [...] Read more.

Disaster logistics presents a highly complex decision-making challenge under conditions of uncertainty, where the timely and efficient allocation of scarce resources is essential to minimize human suffering. In this context, we propose a novel Quantum-Inspired Hyperheuristic Framework (QHHF) designed to solve Dynamic Multi-Objective Combinatorial Optimization Problems (DMOCOPs) arising in disaster relief operations. The proposed framework integrates Quantum-Inspired Evolutionary Algorithms (QIEAs), which facilitate diverse and explorative solution generation, with a Reinforcement Learning (RL)-based hyperheuristic capable of dynamically selecting the most suitable low-level heuristic in response to evolving disaster conditions. A dynamic multi-objective mathematical model is formulated to simultaneously minimize total travel cost and risk exposure, while maximizing priority-weighted demand satisfaction. The model captures real-world complexity through time-dependent variables, stochastic demand variations, and fluctuating transportation risks. Extensive simulations using real-world disaster scenarios demonstrate the effectiveness of the proposed approach in generating high-quality solutions within stringent response time constraints. Comparative evaluations reveal that QHHF consistently outperforms traditional heuristics and metaheuristics in terms of adaptability, scalability, and solution quality across multiple objective trade-offs. Notably, our method achieves a 9.6% reduction in total travel cost, a 6.5% decrease in cumulative risk exposure, and a 4.7% increase in priority-weighted demand satisfaction when benchmarked against existing techniques. This work contributes both to the advancement of hyperheuristic theory and to the development of practical, AI-enabled decision-support tools for emergency logistics management. Full article

(This article belongs to the Special Issue Modeling for Intelligent Vehicles)

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