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)
Latest Articles
One-Dimensional Simulation of Real-World Battery Degradation Using Battery State Estimation and Vehicle System Models
World Electr. Veh. J. 2025, 16(8), 420; https://doi.org/10.3390/wevj16080420 - 25 Jul 2025
Abstract
►
Show Figures
This study aims to develop a method for analyzing real-world battery degradation in electric vehicles in order to identify the optimal battery management system (BMS) during the early digital phase of vehicle development. Battery management of lithium-ion batteries (LiBs) in electric vehicles is
[...] Read more.
This study aims to develop a method for analyzing real-world battery degradation in electric vehicles in order to identify the optimal battery management system (BMS) during the early digital phase of vehicle development. Battery management of lithium-ion batteries (LiBs) in electric vehicles is important to ensure a stable output and to counteract degradation and thermal runaway. To design the optimal system, it is most effective to use a 1D (one-dimensional) vehicle system simulation model, which connects each unit model inside the vehicle, due to the system’s complexity. In order to create a long-term degradation simulation in a vehicle system model, it is important to reduce computational load. Therefore, in this paper, we studied a suitable battery degradation calculation for the vehicle system model based on an equivalent circuit model (ECM) and degradation approximation formulas. After implementing these models, we analyzed long-term degradation behavior through the real-world operation of an electric vehicle driver. We first implemented a high-accuracy ECM using transient charge–discharge tests and Bayesian Optimization. Next, we formulated approximation formulas for degradation prediction based on calendar and cycle degradation tests. Finally, we simulated real-world degradation behavior using these models. The simulation results revealed that even for users who frequently use electric vehicles, degradation under storage conditions is the dominant factor in overall degradation.
Full article
Open AccessArticle
Empirical Energy Consumption Estimation and Battery Operation Analysis from Long-Term Monitoring of an Urban Electric Bus Fleet
by
Tom Klaproth, Erik Berendes, Thomas Lehmann, Richard Kratzing and Martin Ufert
World Electr. Veh. J. 2025, 16(8), 419; https://doi.org/10.3390/wevj16080419 - 25 Jul 2025
Abstract
Electric buses are key in the strategy towards a greenhouse-gas-neutral fleet. However, their restrictions in terms of range and refueling as well as their increased price point present new challenges for public transport companies. This study aims to address, based on real-world operational
[...] Read more.
Electric buses are key in the strategy towards a greenhouse-gas-neutral fleet. However, their restrictions in terms of range and refueling as well as their increased price point present new challenges for public transport companies. This study aims to address, based on real-world operational data, how energy consumption and charging behavior affect battery aging and how operational strategies can be optimized to extend battery life under realistic conditions. This article presents an energy consumption analysis with respect to ambient temperatures and average vehicle speed based exclusively on real-world data of an urban bus fleet, providing a data foundation for range forecasting and infrastructure planning optimized for public transport needs. Additionally, the State of Charge (SOC) window during operation and vehicle idle time as well as the charging power were analyzed in this case study to formulate recommendations towards a more battery-friendly treatment. The central research question is whether battery-friendly operational strategies—such as reduced charging power and lower SOC windows—can realistically be implemented in daily public transport operations. The impact of the recommendations on battery lifetime is estimated using a battery aging model on drive cycles. Finally, the reduction in CO2 emissions compared to diesel buses is estimated.
Full article
(This article belongs to the Special Issue Zero Emission Buses for Public Transport)
►▼
Show Figures

Figure 1
Open AccessArticle
Techno-Economic Analysis of Hydrogen Hybrid Vehicles
by
Dapai Shi, Jiaheng Wang, Kangjie Liu, Chengwei Sun, Zhenghong Wang and Xiaoqing Liu
World Electr. Veh. J. 2025, 16(8), 418; https://doi.org/10.3390/wevj16080418 - 24 Jul 2025
Abstract
Driven by carbon neutrality and peak carbon policies, hydrogen energy, due to its zero-emission and renewable properties, is increasingly being used in hydrogen fuel cell vehicles (H-FCVs). However, the high cost and limited durability of H-FCVs hinder large-scale deployment. Hydrogen internal combustion engine
[...] Read more.
Driven by carbon neutrality and peak carbon policies, hydrogen energy, due to its zero-emission and renewable properties, is increasingly being used in hydrogen fuel cell vehicles (H-FCVs). However, the high cost and limited durability of H-FCVs hinder large-scale deployment. Hydrogen internal combustion engine hybrid electric vehicles (H-HEVs) are emerging as a viable alternative. Research on the techno-economics of H-HEVs remains limited, particularly in systematic comparisons with H-FCVs. This paper provides a comprehensive comparison of H-FCVs and H-HEVs in terms of total cost of ownership (TCO) and hydrogen consumption while proposing a multi-objective powertrain parameter optimization model. First, a quantitative model evaluates TCO from vehicle purchase to disposal. Second, a global dynamic programming method optimizes hydrogen consumption by incorporating cumulative energy costs into the TCO model. Finally, a genetic algorithm co-optimizes key design parameters to minimize TCO. Results show that with a battery capacity of 20.5 Ah and an H-FC peak power of 55 kW, H-FCV can achieve optimal fuel economy and hydrogen consumption. However, even with advanced technology, their TCO remains higher than that of H-HEVs. H-FCVs can only become cost-competitive if the unit power price of the fuel cell system is less than 4.6 times that of the hydrogen engine system, assuming negligible fuel cell degradation. In the short term, H-HEVs should be prioritized. Their adoption can also support the long-term development of H-FCVs through a complementary relationship.
Full article
(This article belongs to the Special Issue Electric Vehicle Technology Development, Energy and Environmental Implications, and Decarbonization: 2nd Edition)
►▼
Show Figures

Figure 1
Open AccessArticle
AI-Driven Automated Test Generation Framework for VCU: A Multidimensional Coupling Approach Integrating Requirements, Variables and Logic
by
Guangyao Wu, Xiaoming Xu and Yiting Kang
World Electr. Veh. J. 2025, 16(8), 417; https://doi.org/10.3390/wevj16080417 - 24 Jul 2025
Abstract
This paper proposes an AI-driven automated test generation framework for vehicle control units (VCUs), integrating natural language processing (NLP) and dynamic variable binding. To address the critical limitation of traditional AI-generated test cases lacking executable variables, the framework establishes a closed-loop transformation from
[...] Read more.
This paper proposes an AI-driven automated test generation framework for vehicle control units (VCUs), integrating natural language processing (NLP) and dynamic variable binding. To address the critical limitation of traditional AI-generated test cases lacking executable variables, the framework establishes a closed-loop transformation from requirements to executable code through a five-layer architecture: (1) structured parsing of PDF requirements using domain-adaptive prompt engineering; (2) construction of a multidimensional variable knowledge graph; (3) semantic atomic decomposition of requirements and logic expression generation; (4) dynamic visualization of cause–effect graphs; (5) path-sensitization-driven optimization of test sequences. Validated on VCU software from a leading OEM, the method achieves 97.3% variable matching accuracy and 100% test case executability, reducing invalid cases by 63% compared to conventional NLP approaches. This framework provides an explainable and traceable automated solution for intelligent vehicle software validation, significantly enhancing efficiency and reliability in automotive testing.
Full article
(This article belongs to the Special Issue Intelligent Electric Vehicle Control, Testing and Evaluation)
Open AccessArticle
A Self-Attention-Enhanced 3D Object Detection Algorithm Based on a Voxel Backbone Network
by
Zhiyong Wang and Xiaoci Huang
World Electr. Veh. J. 2025, 16(8), 416; https://doi.org/10.3390/wevj16080416 - 23 Jul 2025
Abstract
►▼
Show Figures
3D object detection is a fundamental task in autonomous driving. In recent years, voxel-based methods have demonstrated significant advantages in reducing computational complexity and memory consumption when processing large-scale point cloud data. A representative method, Voxel-RCNN, introduces Region of Interest (RoI) pooling on
[...] Read more.
3D object detection is a fundamental task in autonomous driving. In recent years, voxel-based methods have demonstrated significant advantages in reducing computational complexity and memory consumption when processing large-scale point cloud data. A representative method, Voxel-RCNN, introduces Region of Interest (RoI) pooling on voxel features, successfully bridging the gap between voxel and point cloud representations for enhanced 3D object detection. However, its robustness deteriorates when detecting distant objects or in the presence of noisy points (e.g., traffic signs and trees). To address this limitation, we propose an enhanced approach named Self-Attention Voxel-RCNN (SA-VoxelRCNN). Our method integrates two complementary attention mechanisms into the feature extraction phase. First, a full self-attention (FSA) module improves global context modeling across all voxel features. Second, a deformable self-attention (DSA) module enables adaptive sampling of representative feature subsets at strategically selected positions. After extracting contextual features through attention mechanisms, these features are fused with spatial features from the base algorithm to form enhanced feature representations, which are subsequently input into the region proposal network (RPN) to generate high-quality 3D bounding boxes. Experimental results on the KITTI test set demonstrate that SA-VoxelRCNN achieves consistent improvements in challenging scenarios, with gains of 2.49 and 1.87 percentage points at Moderate and Hard difficulty levels, respectively, while maintaining real-time performance at 22.3 FPS. This approach effectively balances local geometric details with global contextual information, providing a robust detection solution for autonomous driving applications.
Full article

Figure 1
Open AccessArticle
Collaborative Optimization Method of Structural Lightweight Design Integrating RSM-GA for an Electric Vehicle BIW
by
Hongjiang Li, Shijie Sun, Hong Fang, Xiaojuan Hu, Junjian Hou and Yudong Zhong
World Electr. Veh. J. 2025, 16(8), 415; https://doi.org/10.3390/wevj16080415 - 23 Jul 2025
Abstract
►▼
Show Figures
The body-in-white (BIW) is an important part of the electric vehicle body, its mass accounts for about 30% of the vehicle mass, and reducing its mass can significantly contribute to energy savings and emission reduction. In this paper, a collaborative optimization method combining
[...] Read more.
The body-in-white (BIW) is an important part of the electric vehicle body, its mass accounts for about 30% of the vehicle mass, and reducing its mass can significantly contribute to energy savings and emission reduction. In this paper, a collaborative optimization method combining the response surface method and genetic algorithm (RSM-GA) is developed to perform the lightweight optimization of the body-in-white of an electric vehicle. Seventeen design variables were screened by relative sensitivity calculations based on modal and stiffness sensitivity analysis, and the data samples were collected using the Taguchi experiment and Hammersley experiment during the designing of the experiment methods. To further maintain the accuracy rate, the least squares regression, moving least squares method, and radial basis function are applied to fitting data to obtain the response surface, and the error analysis of the fitting results is carried out to correct the response surface. Finally, the genetic algorithm based on the response surface is employed to optimize the structure of the body-in-white, and the results are compared with those of the adaptive response surface method and sequential quadratic programming method. Through comparison, the paper found that the optimization effect obtained by the proposed method has a relatively high accuracy rate.
Full article

Figure 1
Open AccessCorrection
Correction: Oloyede et al. A Review on State-of-Charge Estimation Methods, Energy Storage Technologies and State-of-the-Art Simulators: Recent Developments and Challenges. World Electr. Veh. J. 2024, 15, 381
by
Muhtahir O. Oloyede, Godfrey A. Akpakwu, Herman C. Myburgh, Allan De Freitas and Tawanda Kunatsa
World Electr. Veh. J. 2025, 16(8), 414; https://doi.org/10.3390/wevj16080414 - 23 Jul 2025
Abstract
Muhtahir O [...]
Full article
Open AccessArticle
Analytical Modeling and Analysis of Halbach Array Permanent Magnet Synchronous Motor
by
Jinglin Liu, Maixia Shang and Chao Gong
World Electr. Veh. J. 2025, 16(8), 413; https://doi.org/10.3390/wevj16080413 - 23 Jul 2025
Abstract
The Halbach array permanent magnet can improve the power density of motors. This paper uses analytical modeling to analyze and optimize the Halbach array permanent magnet synchronous motor (PMSM). Firstly, a general motor model is established to obtain the air gap flux density.
[...] Read more.
The Halbach array permanent magnet can improve the power density of motors. This paper uses analytical modeling to analyze and optimize the Halbach array permanent magnet synchronous motor (PMSM). Firstly, a general motor model is established to obtain the air gap flux density. Secondly, the flux linkage and back electromotive force (EMF) were calculated. The analytical results are consistent with the finite element model (FEM) results. Thirdly, the effects of slot opening, magnetization angle, and main magnetic pole width on air gap flux density and back-EMF were studied. Finally, based on the optimization results, a prototype was manufactured, and performance testing was conducted successfully. Verification of the back-EMF of the prototype shows that the relative errors between FEM and the measured values are 1.1%, and the relative errors between the analytical values and measured values are 1.6%, which verifies the accuracy of the proposed analytical modeling. The proposed analytical model is universal and can be used to quickly adjust the magnetization form, magnetization angle, and pole width without remodeling in the finite element software, which is convenient for optimizing parameters in the early stage of motor design.
Full article
(This article belongs to the Special Issue Permanent Magnet Motors and Driving Control for Electric Vehicles)
►▼
Show Figures

Figure 1
Open AccessArticle
Dynamic Reconfiguration for Energy Management in EV and RES-Based Grids Using IWOA
by
Hossein Lotfi, Mohammad Hassan Nikkhah and Mohammad Ebrahim Hajiabadi
World Electr. Veh. J. 2025, 16(8), 412; https://doi.org/10.3390/wevj16080412 - 23 Jul 2025
Abstract
Effective energy management is vital for enhancing reliability, reducing operational costs, and supporting the increasing penetration of electric vehicles (EVs) and renewable energy sources (RESs) in distribution networks. This study presents a dynamic reconfiguration strategy for distribution feeders that integrates EV charging stations
[...] Read more.
Effective energy management is vital for enhancing reliability, reducing operational costs, and supporting the increasing penetration of electric vehicles (EVs) and renewable energy sources (RESs) in distribution networks. This study presents a dynamic reconfiguration strategy for distribution feeders that integrates EV charging stations (EVCSs), RESs, and capacitors. The goal is to minimize both Energy Not Supplied (ENS) and operational costs, particularly under varying demand conditions caused by EV charging in grid-to-vehicle (G2V) and vehicle-to-grid (V2G) modes. To improve optimization accuracy and avoid local optima, an improved Whale Optimization Algorithm (IWOA) is employed, featuring a mutation mechanism based on Lévy flight. The model also incorporates uncertainties in electricity prices and consumer demand, as well as a demand response (DR) program, to enhance practical applicability. Simulation studies on a 95-bus test system show that the proposed approach reduces ENS by 16% and 20% in the absence and presence of distributed generation (DG) and EVCSs, respectively. Additionally, the operational cost is significantly reduced compared to existing methods. Overall, the proposed framework offers a scalable and intelligent solution for smart grid integration and distribution network modernization.
Full article
(This article belongs to the Special Issue Power and Energy Systems for E-Mobility, 2nd Edition)
►▼
Show Figures

Figure 1
Open AccessArticle
Cycling-Induced Degradation Analysis of Lithium-Ion Batteries Under Static and Dynamic Charging: A Physical Testing Methodology Using Low-Cost Equipment
by
Byron Patricio Acosta-Rivera, David Sebastian Puma-Benavides, Juan de Dios Calderon-Najera, Leonardo Sanchez-Pegueros, Edilberto Antonio Llanes-Cedeño, Iván Fernando Sinaluisa-Lozano and Bolivar Alejandro Cuaical-Angulo
World Electr. Veh. J. 2025, 16(8), 411; https://doi.org/10.3390/wevj16080411 - 22 Jul 2025
Abstract
Given the rising importance of cost-effective solutions in battery research, this study employs an accessible testing approach using low-cost, sensor-equipped platforms that enable broader research and educational applications. It presents a comparative evaluation of lithium-ion battery degradation under two charging strategies: static charging
[...] Read more.
Given the rising importance of cost-effective solutions in battery research, this study employs an accessible testing approach using low-cost, sensor-equipped platforms that enable broader research and educational applications. It presents a comparative evaluation of lithium-ion battery degradation under two charging strategies: static charging (constant current at 1.2 A) and dynamic charging (stepped current from 400 mA to 800 mA) over 200 charge–discharge cycles. A custom-built, low-cost test platform based on an ESP32 microcontroller was developed to provide real-time monitoring of voltage, current, temperature, and internal resistance, with automated control and cloud-based data logging. The results indicate that static charging provides greater voltage stability and a lower increase in internal resistance (9.3%) compared to dynamic charging (30.17%), suggesting reduced electrochemical stress. Discharge time decreased for both strategies, by 6.25% under static charging and 18.46% under dynamic charging, highlighting capacity fade and aging effects. Internal resistance emerged as a reliable indicator of degradation, closely correlating with reduced runtime. These findings underscore the importance of selecting charging profiles based on specific application needs, as dynamic charging, while offering potential thermal benefits, may accelerate battery aging. Furthermore, the low-cost testing platform proved effective for long-term evaluation and degradation analysis, offering an accessible alternative to commercial battery cyclers. The insights gained contribute to the development of adaptive battery management systems that optimize performance, lifespan, and safety in electric vehicle applications.
Full article
(This article belongs to the Special Issue Impact of Electric Vehicles on Power Systems and Society)
►▼
Show Figures

Figure 1
Open AccessArticle
Forecasting Infrastructure Needs, Environmental Impacts, and Dynamic Pricing for Electric Vehicle Charging
by
Osama Jabr, Ferheen Ayaz, Maziar Nekovee and Nagham Saeed
World Electr. Veh. J. 2025, 16(8), 410; https://doi.org/10.3390/wevj16080410 - 22 Jul 2025
Abstract
In recent years, carbon dioxide (CO2) emissions have increased at the fastest rates ever recorded. This is a trend that contradicts global efforts to stabilise greenhouse gas (GHG) concentrations and prevent long-term climate change. Over 90% of global transport relies on
[...] Read more.
In recent years, carbon dioxide (CO2) emissions have increased at the fastest rates ever recorded. This is a trend that contradicts global efforts to stabilise greenhouse gas (GHG) concentrations and prevent long-term climate change. Over 90% of global transport relies on oil-based fuels. The continued use of diesel and petrol raises concerns related to oil costs, supply security, GHG emissions, and the release of air pollutants and volatile organic compounds. This study explored electric vehicle (EV) charging networks by assessing environmental impacts through GHG and petroleum savings, developing dynamic pricing strategies, and forecasting infrastructure needs. A substantial dataset of over 259,000 EV charging records from Palo Alto, California, was statistically analysed. Machine learning models were applied to generate insights that support sustainable and economically viable electric transport planning for policymakers, urban planners, and other stakeholders. Findings indicate that GHG and gasoline savings are directly proportional to energy consumed, with conversion rates of 0.42 kg CO2 and 0.125 gallons per kilowatt-hour (kWh), respectively. Additionally, dynamic pricing strategies such as a 20% discount on underutilised days and a 15% surcharge during peak hours are proposed to optimise charging behaviour and improve station efficiency.
Full article
(This article belongs to the Special Issue Exploring Beyond Electric Cars: Comprehensive Charging Solutions and Innovations in Land, Sea, and Air Mobility)
►▼
Show Figures

Figure 1
Open AccessArticle
Economic Evaluation of Vehicle Operation in Road Freight Transport—Case Study of Slovakia
by
Miloš Poliak, Kristián Čulík, Milada Huláková and Erik Kováč
World Electr. Veh. J. 2025, 16(8), 409; https://doi.org/10.3390/wevj16080409 - 22 Jul 2025
Abstract
►▼
Show Figures
The European Union is committed to reducing greenhouse gas emissions across all sectors, including the transportation sector. It is possible to assume that road freight transport will need to undergo technological changes, leading to greater use of alternative powertrains. This article builds on
[...] Read more.
The European Union is committed to reducing greenhouse gas emissions across all sectors, including the transportation sector. It is possible to assume that road freight transport will need to undergo technological changes, leading to greater use of alternative powertrains. This article builds on previous research on the energy consumption of battery electric trucks (BETs) and assesses the economic efficiency of electric vehicles in freight transport through a cost calculation. The primary objective was to determine the conditions under which a BET becomes cost-effective for a transport operator. These findings are practically relevant for freight carriers. Unlike other studies, this article does not focus on total cost of ownership (TCO) but rather compares the variable and fixed costs of BETs and conventional internal combustion engine trucks (ICETs). In this article, the operating costs of BETs were calculated and modeled based on real-world measurements of a tested vehicle. The research findings indicate that BETs are economically efficient, primarily when state subsidies are provided, compensating for the significant difference in purchase costs between BETs and conventional diesel trucks. This study found that optimizing operational conditions (daily routes) enables BETs to reach a break-even point at approximately 110,000 km per year, even without subsidies. Another significant finding is that battery capacity degradation leads to a projected annual operating cost increase of approximately 4%.
Full article

Figure 1
Open AccessArticle
Dynamic Machine Learning-Based Simulation for Preemptive Supply-Demand Balancing Amid EV Charging Growth in the Jamali Grid 2025–2060
by
Joshua Veli Tampubolon, Rinaldy Dalimi and Budi Sudiarto
World Electr. Veh. J. 2025, 16(7), 408; https://doi.org/10.3390/wevj16070408 - 21 Jul 2025
Abstract
►▼
Show Figures
The rapid uptake of electric vehicles (EVs) in the Jawa–Madura–Bali (Jamali) grid produces highly variable charging demands that threaten the supply–demand balance. To forestall instability, we developed a predictive simulation based on long short-term memory (LSTM) networks that combines historical generation and consumption
[...] Read more.
The rapid uptake of electric vehicles (EVs) in the Jawa–Madura–Bali (Jamali) grid produces highly variable charging demands that threaten the supply–demand balance. To forestall instability, we developed a predictive simulation based on long short-term memory (LSTM) networks that combines historical generation and consumption patterns with models of EV population growth and initial charging-time (ICT). We introduce a novel supply–demand balance score to quantify weekly and annual deviations between projected supply and demand curves, then use this metric to guide the machine-learning model in optimizing annual growth rate (AGR) and preventing supply demand imbalance. Relative to a business-as-usual baseline, our approach improves balance scores by 64% and projects up to a 59% reduction in charging load by 2060. These results demonstrate the promise of data-driven demand-management strategies for maintaining grid reliability during large-scale EV integration.
Full article

Figure 1
Open AccessArticle
A Multi-Fusion Early Warning Method for Vehicle–Pedestrian Collision Risk at Unsignalized Intersections
by
Weijing Zhu, Junji Dai, Xiaoqin Zhou, Xu Gao, Rui Cheng, Bingheng Yang, Enchu Li, Qingmei Lü, Wenting Wang and Qiuyan Tan
World Electr. Veh. J. 2025, 16(7), 407; https://doi.org/10.3390/wevj16070407 - 21 Jul 2025
Abstract
►▼
Show Figures
Traditional collision risk warning methods primarily focus on vehicle-to-vehicle collisions, neglecting conflicts between vehicles and vulnerable road users (VRUs) such as pedestrians, while the difficulty in predicting pedestrian trajectories further limits the accuracy of collision warnings. To address this problem, this study proposes
[...] Read more.
Traditional collision risk warning methods primarily focus on vehicle-to-vehicle collisions, neglecting conflicts between vehicles and vulnerable road users (VRUs) such as pedestrians, while the difficulty in predicting pedestrian trajectories further limits the accuracy of collision warnings. To address this problem, this study proposes a vehicle-to-everything-based (V2X) multi-fusion vehicle–pedestrian collision warning method, aiming to enhance the traffic safety protection for VRUs. First, Unmanned Aerial Vehicle aerial imagery combined with the YOLOv7 and DeepSort algorithms is utilized to achieve target detection and tracking at unsignalized intersections, thereby constructing a vehicle–pedestrian interaction trajectory dataset. Subsequently, key foundational modules for collision warning are developed, including the vehicle trajectory module, the pedestrian trajectory module, and the risk detection module. The vehicle trajectory module is based on a kinematic model, while the pedestrian trajectory module adopts an Attention-based Social GAN (AS-GAN) model that integrates a generative adversarial network with a soft attention mechanism, enhancing prediction accuracy through a dual-discriminator strategy involving adversarial loss and displacement loss. The risk detection module applies an elliptical buffer zone algorithm to perform dynamic spatial collision determination. Finally, a collision warning framework based on the Monte Carlo (MC) method is developed. Multiple sampled pedestrian trajectories are generated by applying Gaussian perturbations to the predicted mean trajectory and combined with vehicle trajectories and collision determination results to identify potential collision targets. Furthermore, the driver perception–braking time (TTM) is incorporated to estimate the joint collision probability and assist in warning decision-making. Simulation results show that the proposed warning method achieves an accuracy of 94.5% at unsignalized intersections, outperforming traditional Time-to-Collision (TTC) and braking distance models, and effectively reducing missed and false warnings, thereby improving pedestrian traffic safety at unsignalized intersections.
Full article

Figure 1
Open AccessArticle
Active Neutral-Point Voltage Balancing Strategy for Single-Phase Three-Level Converters in On-Board V2G Chargers
by
Qiubo Chen, Zefu Tan, Boyu Xiang, Le Qin, Zhengyang Zhou and Shukun Gao
World Electr. Veh. J. 2025, 16(7), 406; https://doi.org/10.3390/wevj16070406 - 21 Jul 2025
Abstract
►▼
Show Figures
Driven by the rapid advancement of Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) technologies, improving power quality and system stability during charging and discharging has become a research focus. To address this, this paper proposes a Model Predictive Control (MPC) strategy for Active Neutral-Point Voltage
[...] Read more.
Driven by the rapid advancement of Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) technologies, improving power quality and system stability during charging and discharging has become a research focus. To address this, this paper proposes a Model Predictive Control (MPC) strategy for Active Neutral-Point Voltage Balancing (ANPVB) in a single-phase three-level converter used in on-board V2G chargers. Traditional converters rely on passive balancing using redundant vectors, which cannot ensure neutral-point (NP) voltage stability under sudden load changes or frequent power fluctuations. To solve this issue, an auxiliary leg is introduced into the converter topology to actively regulate the NP voltage. The proposed method avoids complex algorithm design and weighting factor tuning, simplifying control implementation while improving voltage balancing and dynamic response. The results show that the proposed Model Predictive Current Control-based ANPVB (MPCC-ANPVB) and Model Predictive Direct Power Control-based ANPVB (MPDPC-ANPVB) strategies maintain the NP voltage within ±0.7 V, achieve accurate power tracking within 50 ms, and reduce the total harmonic distortion of current (THDi) to below 1.89%. The proposed strategies are tested in both V2G and G2V modes, confirming improved power quality, better voltage balance, and enhanced dynamic response.
Full article

Figure 1
Open AccessArticle
A Symmetrical Cross Double-D Coil with Improved Misalignment Tolerance for WPT Systems
by
Ashwini Rathod, Satish M. Mahajan and Taiye Owu
World Electr. Veh. J. 2025, 16(7), 405; https://doi.org/10.3390/wevj16070405 - 18 Jul 2025
Abstract
Inductive Wireless Power Transfer (WPT) technologies are advancing significantly in the electric vehicle (EV) charging applications. Misalignment between transmitting and receiving coils can considerably affect power transmission efficiency in WPT systems. Prior research involved power electronics as well as electromagnetic couplers. This work
[...] Read more.
Inductive Wireless Power Transfer (WPT) technologies are advancing significantly in the electric vehicle (EV) charging applications. Misalignment between transmitting and receiving coils can considerably affect power transmission efficiency in WPT systems. Prior research involved power electronics as well as electromagnetic couplers. This work focuses on the coil design aspect of electromagnetic couplers. A relatively new concept of Symmetrical Cross Double-D (SCDD) type of the coil design is introduced specifically to maximize tolerance to misalignment while sustaining significant amount of power transferred. Mutual inductance was determined for the perfect alignment and misalignment positions of the SCDD coils. Mutual inductance obtained from the simulation was validated from the experimental measurements. The SCDD electromagnetic coupler demonstrated almost 2.5 times superior tolerance to misalignment of coils compared to the conventional circular coupler while maintaining at least 78% of maximum power transfer even at a lateral misalignment of 40 mm.
Full article
(This article belongs to the Special Issue Wireless Power Transfer Technology for Electric Vehicles)
►▼
Show Figures

Figure 1
Open AccessSystematic Review
A Literature Review on Strategic, Tactical, and Operational Perspectives in EV Charging Station Planning and Scheduling
by
Marzieh Sadat Aarabi, Mohammad Khanahmadi and Anjali Awasthi
World Electr. Veh. J. 2025, 16(7), 404; https://doi.org/10.3390/wevj16070404 - 18 Jul 2025
Abstract
Before the onset of global warming concerns, the idea of manufacturing electric vehicles on a large scale was not widely considered. However, electric vehicles offer several advantages that have garnered attention. They are environmentally friendly, with simpler drive systems compared to traditional fossil
[...] Read more.
Before the onset of global warming concerns, the idea of manufacturing electric vehicles on a large scale was not widely considered. However, electric vehicles offer several advantages that have garnered attention. They are environmentally friendly, with simpler drive systems compared to traditional fossil fuel vehicles. Additionally, electric vehicles are highly efficient, with an efficiency of around 90%, in contrast to fossil fuel vehicles, which have an efficiency of about 30% to 35%. The higher energy efficiency of electric vehicles contributes to lower operational costs, which, alongside regulatory incentives and shifting consumer preferences, has increased their strategic importance for many vehicle manufacturers. In this paper, we present a thematic literature review on electric vehicles charging station location planning and scheduling. A systematic literature review across various data sources in the area yielded ninety five research papers for the final review. The research results were analyzed thematically, and three key directions were identified, namely charging station deployment and placement, optimal allocation and scheduling of EV parking lots, and V2G and smart charging systems as the top three themes. Each theme was further investigated to identify key topics, ongoing works, and future trends. It has been found that optimization methods followed by simulation and multi-criteria decision-making are most commonly used for EV infrastructure planning. A multistakeholder perspective is often adopted in these decisions to minimize costs and address the range anxiety of users. The future trend is towards the integration of renewable energy in smart grids, uncertainty modeling of user demand, and use of artificial intelligence for service quality improvement.
Full article
(This article belongs to the Special Issue Electric Vehicles and Charging Facilities for a Sustainable Transport Sector)
►▼
Show Figures

Figure 1
Open AccessArticle
A Study of the Social Identity of Electric Vehicle Consumers from a Social Constructivism Perspective
by
Meishi Jiang, Fei Zhou, Ling Peng and Dan Wan
World Electr. Veh. J. 2025, 16(7), 403; https://doi.org/10.3390/wevj16070403 - 17 Jul 2025
Abstract
►▼
Show Figures
The present study adopts the social constructivism theory and consumer decision-making process model with the aim of examining the social identity that consumers build through the purchase of electric vehicles (EVs) in line with their income, age, gender, and education. The study’s findings
[...] Read more.
The present study adopts the social constructivism theory and consumer decision-making process model with the aim of examining the social identity that consumers build through the purchase of electric vehicles (EVs) in line with their income, age, gender, and education. The study’s findings indicate that this social identity, shaped by income, age, gender and education, exerts a significant influence on consumer decision-making behavior. This identity is shaped not only by the make and model of EVs chosen, but also by their preferences for vehicle performance and technical features. The adoption of EVs by consumers is driven by dual objectives: the fulfilment of practical needs and the shaping of social identities in social interactions that correspond to their income, age, gender, and education. The study’s findings are of significant value in understanding the social identity aspirations of consumers in the electric vehicle consumer market, and provide a theoretical foundation for future electric vehicle companies to create products and corporate cultures that meet their target customers, thereby effectively promoting the popularization of electric vehicles.
Full article

Figure 1
Open AccessArticle
Advancing Sustainable Urban Mobility in Oman: Unveiling the Predictors of Electric Vehicle Adoption Intentions
by
Wafa Said Al-Maamari, Emad Farouk Saleh and Suliman Zakaria Suliman Abdalla
World Electr. Veh. J. 2025, 16(7), 402; https://doi.org/10.3390/wevj16070402 - 17 Jul 2025
Abstract
►▼
Show Figures
The global shift toward sustainable transportation has gained increasing interest, promoting the use of electric vehicles (EVs) as an environmentally friendly alternative to conventional vehicles as a result of a complex interaction between economic incentives, social dynamics, and environmental imperatives. This study is
[...] Read more.
The global shift toward sustainable transportation has gained increasing interest, promoting the use of electric vehicles (EVs) as an environmentally friendly alternative to conventional vehicles as a result of a complex interaction between economic incentives, social dynamics, and environmental imperatives. This study is based on the Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) to understand the key factors influencing consumers’ intentions in the Sultanate of Oman toward adopting electric vehicles. It is based on a mixed methodology combining quantitative data from a questionnaire of 448 participants, analyzed using ordinal logistic regression, with qualitative thematic analysis of in-depth interviews with 18 EV owners. Its results reveal that performance expectations, trust in EV technology, and social influence are the strongest predictors of EV adoption intentions in Oman. These findings suggest that some issues related to charging infrastructure, access to maintenance services, and cost-benefit ratio are key considerations that influence consumers’ intention to accept and use EVs. Conversely, recreational motivation is not a statistically significant factor, which suggests that consumers focus on practical and economic motivations when deciding to adopt EVs rather than on their enjoyment of driving the vehicle. The findings of this study provide valuable insights for decision-makers and practitioners to understand public perceptions of electric vehicles, enabling them to design effective strategies to promote the adoption of these vehicles in the emerging sustainable transportation market of the future.
Full article

Figure 1
Open AccessArticle
Optimal Scheduling of Hybrid Games Considering Renewable Energy Uncertainty
by
Haihong Bian, Kai Ji, Yifan Zhang, Xin Tang, Yongqing Xie and Cheng Chen
World Electr. Veh. J. 2025, 16(7), 401; https://doi.org/10.3390/wevj16070401 - 17 Jul 2025
Abstract
►▼
Show Figures
As the integration of renewable energy sources into microgrid operations deepens, their inherent uncertainty poses significant challenges for dispatch scheduling. This paper proposes a hybrid game-theoretic optimization strategy to address the uncertainty of renewable energy in microgrid scheduling. An energy trading framework is
[...] Read more.
As the integration of renewable energy sources into microgrid operations deepens, their inherent uncertainty poses significant challenges for dispatch scheduling. This paper proposes a hybrid game-theoretic optimization strategy to address the uncertainty of renewable energy in microgrid scheduling. An energy trading framework is developed, involving integrated energy microgrids (IEMS), shared energy storage operators (ESOS), and user aggregators (UAS). A mixed game model combining master–slave and cooperative game theory is constructed in which the ESO acts as the leader by setting electricity prices to maximize its own profit, while guiding the IEMs and UAs—as followers—to optimize their respective operations. Cooperative decisions within the IEM coalition are coordinated using Nash bargaining theory. To enhance the generality of the user aggregator model, both electric vehicle (EV) users and demand response (DR) users are considered. Additionally, the model incorporates renewable energy output uncertainty through distributionally robust chance constraints (DRCCs). The resulting two-level optimization problem is solved using Karush–Kuhn–Tucker (KKT) conditions and the Alternating Direction Method of Multipliers (ADMM). Simulation results verify the effectiveness and robustness of the proposed model in enhancing operational efficiency under conditions of uncertainty.
Full article

Figure 1
Highly Accessed Articles
Latest Books
E-Mail Alert
News
Topics
Topic in
Energies, Applied Sciences, Electronics, Vehicles, Eng, WEVJ
Advances in Electric Vehicle Charging Systems and Vehicle-to-Grid Technology
Topic Editors: Ching Chuen Chan, Kwok Tong Chau, Wei LiuDeadline: 1 September 2025

Conferences
Special Issues
Special Issue in
WEVJ
Electric Vehicle Networking and Traffic Control
Guest Editors: Abdullah Baz, Mahmoud BadrDeadline: 31 July 2025
Special Issue in
WEVJ
Electrochemical and Thermal Modeling of Batteries for Electric Vehicle
Guest Editors: Rongheng Li, Xuan ZhouDeadline: 31 July 2025
Special Issue in
WEVJ
Vehicular Communications for Cooperative and Automated Mobility
Guest Editors: Hugues Tchouankem, Ignacio LlatserDeadline: 31 July 2025
Special Issue in
WEVJ
Thermal Management System for Battery Electric Vehicle
Guest Editors: Chengjiang Li, Xin ZouDeadline: 31 July 2025