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Advancing Energy Management Strategies for Hybrid Fuel Cell Vehicles: A Comparative Study of Deterministic and Fuzzy Logic Approaches
Analytical Modelling of Arc Flash Consequences in High-Power Systems with Energy Storage for Electric Vehicle Charging
One-Dimensional Simulation of Real-World Battery Degradation Using Battery State Estimation and Vehicle System Models
Electromagnetic Analysis and Multi-Objective Design Optimization of a WFSM with Hybrid GOES-NOES Core
An Optimal Multi-Zone Fast-Charging System Architecture for MW-Scale EV Charging Sites

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

21 pages, 8515 KiB

Open AccessArticle

Analytical Calculation and Verification of Radial Electromagnetic Force Under Multi-Type Air Gap Eccentricity of Hub Motor

by Chao Yang, Shudi Jia, Wujun Ji and Chuanxing Yang

World Electr. Veh. J. 2025, 16(8), 473; https://doi.org/10.3390/wevj16080473 (registering DOI) - 19 Aug 2025

This paper presents a method for calculating radial forces in switched reluctance motors (SRMs) under radial and tilted air gap eccentricity states. Firstly, the Fourier series method is used to establish a nonlinear model of a switched reluctance motor, which calculates the air [...] Read more.

This paper presents a method for calculating radial forces in switched reluctance motors (SRMs) under radial and tilted air gap eccentricity states. Firstly, the Fourier series method is used to establish a nonlinear model of a switched reluctance motor, which calculates the air gap length at different positions around the motor circumference and applies the Radial Electromagnetic Force (REF) equation to compute the radial force values at various positions under the air gap eccentricity states. Secondly, the finite element method is employed to analyze the factors influencing radial forces in switched reluctance motors under air gap eccentricity states, considering different winding phases and structural parameters as influencing factors. Finally, a measurement platform for radial forces under multiple types of air gap eccentricity states is established to validate the effectiveness of the analytical results for radial forces under radial and tilted air gap eccentricity states. Full article

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55 pages, 11479 KiB

Open AccessArticle

Optimizing Autonomous Vehicle Navigation through Reinforcement Learning in Dynamic Urban Environments

by Mohammed Abdullah Alsuwaiket

World Electr. Veh. J. 2025, 16(8), 472; https://doi.org/10.3390/wevj16080472 - 18 Aug 2025

Autonomous vehicle (AV) navigation in dynamic urban environments faces challenges such as unpredictable traffic conditions, varying road user behaviors, and complex road networks. This study proposes a novel reinforcement learning-based framework that enhances AV decision making through spatial-temporal context awareness. The framework integrates [...] Read more.

Autonomous vehicle (AV) navigation in dynamic urban environments faces challenges such as unpredictable traffic conditions, varying road user behaviors, and complex road networks. This study proposes a novel reinforcement learning-based framework that enhances AV decision making through spatial-temporal context awareness. The framework integrates Proximal Policy Optimization (PPO) and Graph Neural Networks (GNNs) to effectively model urban features like intersections, traffic density, and pedestrian zones. A key innovation is the urban context-aware reward mechanism (UCARM), which dynamically adapts the reward structure based on traffic rules, congestion levels, and safety considerations. Additionally, the framework incorporates a Dynamic Risk Assessment Module (DRAM), which uses Bayesian inference combined with Markov Decision Processes (MDPs) to proactively evaluate collision risks and guide safer navigation. The framework’s performance was validated across three datasets—Argoverse, nuScenes, and CARLA. Results demonstrate significant improvements: an average travel time of 420 ± 20 s, a collision rate of 3.1%, and energy consumption of 11,833 ± 550 J in Argoverse; 410 ± 20 s, 2.5%, and 11,933 ± 450 J in nuScenes; and 450 ± 25 s, 3.6%, and 13,000 ± 600 J in CARLA. The proposed method achieved an average navigation success rate of 92.5%, consistently outperforming baseline models in safety, efficiency, and adaptability. These findings indicate the framework’s robustness and practical applicability for scalable AV deployment in real-world urban traffic conditions. Full article

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

24 pages, 1733 KiB

Open AccessArticle

The Soft Fixed Route Hybrid Electric Aircraft Charging Problem with Variable Speed

by Anthony Deschênes, Raphaël Boudreault, Jonathan Gaudreault and Claude-Guy Quimper

World Electr. Veh. J. 2025, 16(8), 471; https://doi.org/10.3390/wevj16080471 - 18 Aug 2025

The shift toward sustainable aviation has accelerated research into hybrid electric aircraft, particularly in the context of regional air mobility. To support this transition, we introduce the Soft Fixed Route Hybrid Electric Aircraft Charging Problem with Variable Speed (S-FRHACP-VS), a novel optimization problem [...] Read more.

The shift toward sustainable aviation has accelerated research into hybrid electric aircraft, particularly in the context of regional air mobility. To support this transition, we introduce the Soft Fixed Route Hybrid Electric Aircraft Charging Problem with Variable Speed (S-FRHACP-VS), a novel optimization problem for managing hybrid electric aircraft operations that considers variable speed. The objective is to minimize total costs by determining charging strategies, refueling decisions, hybridization ratios, and speed decisions while adhering to a soft schedule. This paper introduces an iterative variable-based fixation heuristic, named Iterative Two-Stage Mixed-Integer Programming Heuristic (ITS-MIP-H), that alternatively optimizes speed and hybridization ratios while considering the soft schedule constraints, nonlinear charging, and nonlinear energy consumption functions. In addition, a metaheuristic genetic algorithm is proposed as an alternative optimization approach. Experiments on ten realistic flight instances demonstrate that optimizing speed leads to an average cost reduction of 7.64% compared to the best non-speed-optimized model, with reductions of up to 18.64% compared to an all-fuel-based heuristic. Although genetic algorithm provides a viable alternative that performs better than the best non-speed-optimized model, the proposed iterative variable-based fixation heuristic approach consistently outperforms the metaheuristic, achieving the best solutions within seconds. These results provide new insights into the integration of hybrid electric aircraft within transportation networks, contributing to advancements in aircraft routing optimization, energy-efficient operations, and sustainable aviation policy development. Full article

(This article belongs to the Special Issue Electric and Hybrid Electric Aircraft Propulsion Systems)

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25 pages, 8171 KiB

Open AccessArticle

Design of an Optimal Enhanced Quadratic Controller for a Four-Wheel Independent Driven Electric Vehicle (4WID-EV) Under Failure Cases

by Sasikala Durairaj and Mohamed Rabik Mohamed Ismail

World Electr. Veh. J. 2025, 16(8), 470; https://doi.org/10.3390/wevj16080470 - 18 Aug 2025

Owing to the recent attention towards the growing issue of global warming, the automotive industry is shifting towards more capable and eco-friendly vehicles with longer ranges than conventional vehicles. Although the transition to eco-friendly vehicles faces several challenges, including component failures due to [...] Read more.

Owing to the recent attention towards the growing issue of global warming, the automotive industry is shifting towards more capable and eco-friendly vehicles with longer ranges than conventional vehicles. Although the transition to eco-friendly vehicles faces several challenges, including component failures due to mechanical wear, electrical voltage fluctuations, motor damage from overloads, infrastructure, and external environmental disturbances. The four-wheel independent drive electric vehicle (4WID-EV) is often used as an alternative to the single-drive electric vehicle, providing improved traction control and reducing the increased load on the individual motors. This study proposes an optimally enhanced controller to control the linear and nonlinear trajectories of four independent motors to evaluate the electric vehicle’s speed and address challenges involved in torque distribution to the independent drive, especially under various motor failure conditions. The computed results reveal that the proposed optimal linear quadratic regulator (LQR) controller accurately predicts better than the conventional proportional integral derivative (PID) controller in terms of the vehicle’s speed under various motor failures. Specifically, the optimal LQR controller achieves a faster settling time of 2.5 s, a lower overshoot of 0.8%, a mean error of 0.0441 rad/s, and a mean squared error (MSE) of 0.0820 (rad/s²). These results indicate that the proposed controller enhances stability and accuracy, improving adaptability even under motor failure conditions in 4WID-EVs. Full article

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20 pages, 3174 KiB

Open AccessReview

Threat Landscape and Integrated Cybersecurity Framework for V2V and Autonomous Electric Vehicles

by Kithmini Godewatte Arachchige, Ghanem Alkaabi, Mohsin Murtaza, Qazi Emad Ul Haq, Abedallah Zaid Abualkishik and Cheng-Chi Lee

World Electr. Veh. J. 2025, 16(8), 469; https://doi.org/10.3390/wevj16080469 - 18 Aug 2025

This study conducts a detailed analysis of cybersecurity threats, including artificial intelligence (AI)-driven cyber-attacks targeting vehicle-to-vehicle (V2V) and electric vehicle (EV) communications within the rapidly evolving field of connected and autonomous vehicles (CAVs). As autonomous and electric vehicles become increasingly integrated into daily [...] Read more.

This study conducts a detailed analysis of cybersecurity threats, including artificial intelligence (AI)-driven cyber-attacks targeting vehicle-to-vehicle (V2V) and electric vehicle (EV) communications within the rapidly evolving field of connected and autonomous vehicles (CAVs). As autonomous and electric vehicles become increasingly integrated into daily life, their susceptibility to cyber threats such as replay, jamming, spoofing, and denial-of-service (DoS) attacks necessitates the development of robust cybersecurity measures. Additionally, EV-specific threats, including battery management system (BMS) exploitation and compromised charging interfaces, introduce distinct vulnerabilities requiring specialized attention. This research proposes a comprehensive and integrated cybersecurity framework that rigorously examines current V2V, vehicle-to-everything (V2X), and EV-specific systems through systematic threat assessments, vulnerability analyses, and the deployment of advanced security controls. Unlike previous state-of-the-art approaches, which primarily focus on isolated threats or specific components such as V2V protocols, the proposed framework provides a holistic cybersecurity strategy addressing the entire communication stack, EV subsystems, and incorporates AI-driven threat detection mechanisms. This comprehensive and integrated approach addresses critical gaps found in the existing literature, making it significantly more adaptable and resilient against evolving cyber-attacks. Our framework aligns with industry standards and regulatory requirements, significantly enhancing the security, safety, and reliability of modern transportation systems. By incorporating specialized cryptographic techniques, secure protocols, and continuous monitoring mechanisms, the proposed approach ensures robust protection against sophisticated cyber threats, thereby safeguarding vehicle operations and user privacy. Full article

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

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17 pages, 1684 KiB

Open AccessArticle

Privacy-Preserving EV Charging Authorization and Billing via Blockchain and Homomorphic Encryption

by Amjad Aldweesh and Someah Alangari

World Electr. Veh. J. 2025, 16(8), 468; https://doi.org/10.3390/wevj16080468 - 17 Aug 2025

Electric vehicle (EV) charging infrastructures raise significant concerns about data security and user privacy because traditional centralized authorization and billing frameworks expose sensitive information to breaches and profiling. To address these vulnerabilities, we propose a novel decentralized framework that couples a permissioned blockchain [...] Read more.

Electric vehicle (EV) charging infrastructures raise significant concerns about data security and user privacy because traditional centralized authorization and billing frameworks expose sensitive information to breaches and profiling. To address these vulnerabilities, we propose a novel decentralized framework that couples a permissioned blockchain with fully homomorphic encryption (FHE). Unlike prior blockchain-only or blockchain-and-machine-learning solutions, our architecture performs all authorization and billing computations on encrypted data and records transactions immutably via smart contracts. We implemented the system on Hyperledger Fabric using the CKKS-based TenSEAL library, chosen for its efficient arithmetic on real-valued vectors, and show that homomorphic operations are executed off-chain within a secure computation layer while smart contracts handle only encrypted records. In a simulation involving 20 charging stations and up to 100 concurrent users, the proposed system achieved an average authorization latency of 610 ms, a billing computation latency of 310 ms, and transaction throughput of 102 Tx min while maintaining energy overhead below 0.14 kWh day per station. When compared to state-of-the-art blockchain-only approaches, our method reduces data exposure by 100%, increases privacy from “moderate” to “very high,” and achieves similar throughput with acceptable computational overhead. These results demonstrate that privacy-preserving EV charging is practical using present-day cryptography, paving the way for secure, scalable EV charging and billing services. Full article

(This article belongs to the Special Issue New Trends in Electrical Drives for EV Applications)

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26 pages, 9055 KiB

Open AccessArticle

Genetic Algorithm-Based Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles

by Xingliang Yang and Yujie Wang

World Electr. Veh. J. 2025, 16(8), 467; https://doi.org/10.3390/wevj16080467 - 16 Aug 2025

Enhancing system durability and fuel economy stands as a crucial factor in the energy management of fuel cell hybrid vehicles. This paper proposes an Equivalent Consumption Minimization Strategy (ECMS) based on the Genetic Algorithm (GA), aiming to minimize the overall operating cost of [...] Read more.

Enhancing system durability and fuel economy stands as a crucial factor in the energy management of fuel cell hybrid vehicles. This paper proposes an Equivalent Consumption Minimization Strategy (ECMS) based on the Genetic Algorithm (GA), aiming to minimize the overall operating cost of the system. First, this study establishes a dynamic model of the hydrogen–electric hybrid vehicle, a static input–output model of the hybrid power system, and an aging model. Next, a speed prediction method based on an Autoregressive Integrated Moving Average (ARIMA) model is designed. This method fits a predictive model by collecting historical speed data in real time, ensuring the robustness of speed prediction. Finally, based on the speed prediction results, an adaptive Equivalence Factor (EF) method using a GA is proposed. This method comprehensively considers fuel consumption and the economic costs associated with the aging of the hydrogen–electric hybrid system, forming a total operating cost function. The GA is then employed to dynamically search for the optimal EF within the cost function, optimizing the system’s economic performance while ensuring real-time feasibility. Simulation outcomes demonstrate that the proposed energy management strategy significantly enhances both the durability and fuel economy of the fuel cell hybrid vehicle. Full article

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22 pages, 6295 KiB

Open AccessArticle

Enhanced Tire–Snow Sinkage Modeling for Optimized Electric Vehicle Traction Control in Northern China Snow Conditions

by Jingyi Gu, Bo Li, Shaoyi Bei and Chenyu Hu

World Electr. Veh. J. 2025, 16(8), 466; https://doi.org/10.3390/wevj16080466 - 15 Aug 2025

The interaction between tires and snow layer is fundamental for vehicle safety on snowy roads. Due to the instantaneous high torque output characteristics of electric vehicles, they are more prone to slipping when driving in snow, which exacerbates the complexity of tire–snow interaction. [...] Read more.

The interaction between tires and snow layer is fundamental for vehicle safety on snowy roads. Due to the instantaneous high torque output characteristics of electric vehicles, they are more prone to slipping when driving in snow, which exacerbates the complexity of tire–snow interaction. In order to construct a more accurate tire–snow interaction model in Northern China, the Bekker formula is introduced to establish the snow pressure–sinkage relationship formula, and the parameters are calibrated by disk experiments. Then the improved tire–snow interaction model is proposed by combining the use of the brush model on the rigid road surface and the dynamic discussion of the tire’s motion behavior on the snow. A coupled finite element (FE) tire model and discrete element (DE) snow terrain model are established, with interactions governed by snow–rubber contact mechanics. The simulation tests the sinking depth of tires on snowy road surface under different slip rates and different loads, as well as the force on tires. The model provides high-precision input to the EV snow traction control algorithm to optimize motor torque distribution to improve energy efficiency. By comparing and analyzing with theoretical values, the traditional empirical model, and the modified physical model, it is finally concluded that the modified model has better reliability than the original model. Compared with the empirical model, the improved model reduces the vertical stress prediction error from 5% to less than 1%, and the motion resistance error from 6% to approximately 2%, providing high-precision input for the snow traction control of electric vehicles. Full article

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22 pages, 1961 KiB

Open AccessArticle

Research and Quantitative Analysis on Dynamic Risk Assessment of Intelligent Connected Vehicles

by Kailong Li, Feng Zhang, Min Li and Li Wang

World Electr. Veh. J. 2025, 16(8), 465; https://doi.org/10.3390/wevj16080465 - 14 Aug 2025

Ensuring dynamic risk management for intelligent connected vehicles (ICVs) in complex urban environments is critical as autonomous driving technology advances. This study presents three key contributions: (1) a comprehensive risk indicator system, constructed using entropy-based weighting, extracts 13-dimensional data on abnormal behaviors (e.g., [...] Read more.

Ensuring dynamic risk management for intelligent connected vehicles (ICVs) in complex urban environments is critical as autonomous driving technology advances. This study presents three key contributions: (1) a comprehensive risk indicator system, constructed using entropy-based weighting, extracts 13-dimensional data on abnormal behaviors (e.g., speed, acceleration, position) to enhance safety and efficiency; (2) a multidimensional risk quantification method, simulated under single-vehicle and platooning modes on a CARLA-SUMO co-simulation platform, achieved >98% accuracy; (3) a cloud takeover strategy for high-level autonomous vehicles, directly linking risk assessment to real-time control. Analysis of 56,117 risk data points shows a 32% reduction in safety risks during simulations. These contributions provide methodological innovations and substantial data support for ICV field testing. Full article

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23 pages, 2882 KiB

Open AccessArticle

Electrify the Field: Designing and Optimizing Electric Tractor Drivetrains with Real-World Cycles

by Korbinian Götz, Markus Pointner, Lukas Mayr, Simon Mailhammer and Markus Lienkamp

World Electr. Veh. J. 2025, 16(8), 463; https://doi.org/10.3390/wevj16080463 - 14 Aug 2025

The electrification of tractors can increase the self-supply of renewable energy produced on the farm and reduce the operating costs of tractors. However, electric tractors face higher upfront costs than their diesel counterparts, as well as limited operating time. A drivetrain that is [...] Read more.

The electrification of tractors can increase the self-supply of renewable energy produced on the farm and reduce the operating costs of tractors. However, electric tractors face higher upfront costs than their diesel counterparts, as well as limited operating time. A drivetrain that is highly efficient in a wide range of agricultural applications reduces operating costs and enables long operating times. Thus, we propose a method to design electric tractor drivetrain configurations that incorporates longitudinal dynamic simulations to enable the development of such efficient drivetrains. To represent a diverse application profile, we include real-world load cycles recorded from a 104 kW diesel tractor. Our investigation focuses on the axle-individual drivetrain topology (eAxle) and the central motor topology as the configurations that offer the most promising trade-off between efficiency and complexity. The design method includes the top-down design of the topology including its individual components, such as the inverter, motor, and transmission, which are varied based on the load. Our method derives drivetrains with average efficiencies of 83% for an axle-individual topology with two gears. With a 100 kWh battery, such a drivetrain enables operating times of 7.5 h when fertilizing and 2.4 h when seeding. Full article

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18 pages, 577 KiB

Open AccessArticle

Total Cost of Ownership of Electric Buses in Europe

by Rishabh Ghotge, Daan van Rooij and Sanne van Breukelen

World Electr. Veh. J. 2025, 16(8), 464; https://doi.org/10.3390/wevj16080464 - 13 Aug 2025

This study presents the total cost of ownership (TCO) of battery electric buses across Europe (the EU27 + UK + Türkiye). A comprehensive review of the assumptions and data used for the TCO calculation of buses in the literature is provided, along with [...] Read more.

This study presents the total cost of ownership (TCO) of battery electric buses across Europe (the EU27 + UK + Türkiye). A comprehensive review of the assumptions and data used for the TCO calculation of buses in the literature is provided, along with calculations of the different bus TCO excluding labor costs, across these countries. The calculated TCO is compared with diesel costs in each country to identify the countries in which bus electrification is financially most competitive. The study reveals that the financial case for bus electrification is strongest in Finland, France, Belgium and Greece (TCOs around €750k to €850k and high diesel costs in the range of €1.70 per liter) and is weakest in Malta, Bulgaria and Cyprus. These results are expected to be of interest for operators, academics, policy makers, and financial investors in bus electrification. Full article

(This article belongs to the Special Issue Zero Emission Buses for Public Transport)

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31 pages, 1321 KiB

Open AccessArticle

A Method for Fault Tolerance of AES Encryption Systems Focused on Improving the Cybersecurity of VANET Through the Use of Residue Codes

by Igor Anatolyevich Kalmykov, Alexandr Anatolyevich Olenev, Daniil Vyacheslavovich Dukhovnyj, Igor Alexandrovich Provornov and Vladimir Sergeyevich Slyadnev

World Electr. Veh. J. 2025, 16(8), 462; https://doi.org/10.3390/wevj16080462 - 13 Aug 2025

The problem of cybersecurity of vehicular ad hoc network (VANET) is far from being fully solved. This is due to the fact that when exchanging data between On Board Units (OBUs) and Roadside Units (RSUs) a wireless channel is used, which is subject [...] Read more.

The problem of cybersecurity of vehicular ad hoc network (VANET) is far from being fully solved. This is due to the fact that when exchanging data between On Board Units (OBUs) and Roadside Units (RSUs) a wireless channel is used, which is subject to many cyberattacks. It is known that the use of encryption algorithms, particularly Advanced Encryption Standard (AES), can effectively counter many of them. However, during the operation of AES encryption systems, failures may occur, as a result of which closed communication channels may become open and accessible to attackers. Therefore, giving the property of fault tolerance to the used encryption systems is an urgent task. To solve this problem, the article proposes to use redundant residue codes in the polynomial ring (RCPR). The article describes a method of providing fault tolerance of AES encryption systems based on RCPR. Using the developed error correction algorithm for RCPR with one control module, the redundant RCPR can detect 100% of single and double errors, as well as correct 100% of single and 75% of double errors that occur during encryption and decryption. Thus, the developed method based on error correction of AES encryption system allows to parry cyberattacks on vehicles and ensure a higher level of cyber security of VANET. Full article

(This article belongs to the Special Issue Recent Developments and Research in Vehicular Ad Hoc Networks (VANETs))

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24 pages, 907 KiB

Open AccessArticle

Users’ Perceived Value of Electric Vehicles in China: A Latent Class Model-Based Analysis

by Wenbo Li, Ke Cui, Leixing Wu and Bin Zheng

World Electr. Veh. J. 2025, 16(8), 461; https://doi.org/10.3390/wevj16080461 - 13 Aug 2025

Future promotional strategies for electric vehicles (EVs) need to be tailored to the initial users’ perceptions regarding these vehicles. This study aims to evaluate EV users’ perceived value in terms of the following five key dimensions: economic, environmental, social, emotional, and technological value. [...] Read more.

Future promotional strategies for electric vehicles (EVs) need to be tailored to the initial users’ perceptions regarding these vehicles. This study aims to evaluate EV users’ perceived value in terms of the following five key dimensions: economic, environmental, social, emotional, and technological value. Recognizing the diversity of users’ perceived value, a latent class model is employed to categorize respondents, integrating predictive and outcome variables for a comprehensive analysis. The results indicate that 62% of users fall into a high endorsement group, indicating the widespread acceptance of the multidimensional value brought by EVs. Another 21% fall into a moderate endorsement group, signifying the partial approval of select EV values (e.g., emotional and social). Conversely, 17% are categorized as low endorsement users, expressing a low level of acceptance in terms of all the dimensions of EV value. Demographic characteristics such as family size and income significantly influence these user classifications, and there are marked differences in perceptions of certain vehicle attributes. Full article

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27 pages, 922 KiB

Open AccessArticle

A Qualitative Analysis of Factors Influencing Chinese Consumers’ Willingness to Purchase Used Electric Vehicles

by Yi Zhang, Nan Liu, Qianran Zhang and Chunyue Liu

World Electr. Veh. J. 2025, 16(8), 460; https://doi.org/10.3390/wevj16080460 - 12 Aug 2025

Based on SWOT and TOWS analyses and combined with expert interviews, this study proposes a series of marketing strategies to enhance consumers’ willingness to purchase used electric vehicles (UEVs). In terms of the strengths and opportunities (SO) strategy, it is recommended that enterprises [...] Read more.

Based on SWOT and TOWS analyses and combined with expert interviews, this study proposes a series of marketing strategies to enhance consumers’ willingness to purchase used electric vehicles (UEVs). In terms of the strengths and opportunities (SO) strategy, it is recommended that enterprises strengthen marketing and brand building, customize services and special features, use price advantages and environmental awareness to attract specific groups, provide convenient charging services, give full play to technical support advantages, and expand channels through cooperation with the government and manufacturers. The strategies for the strengths and threats (ST) scenario include establishing a government relations department, improving product quality and brand image, enhancing information transparency and quality assurance, and building a partner network and customer relationships. In terms of weaknesses and opportunities (WO), it is proposed to transform corporate weaknesses into opportunities by investing in evaluation technology and expanding charging facilities, strengthening market promotion and consumer education, and providing personalized car purchase advice and high-quality after-sales services. In the face of weaknesses and threats (WT), the emphasis is on reducing risks and improving competitiveness by improving quality management, internal management, and providing long-term after-sales and warranty services. The main innovation of this study lies in integrating SWOT-TOWS strategic frameworks with qualitative expert insights to develop actionable and scenario-specific marketing strategies for the UEV market—an area previously underexplored in existing literature. The comprehensive strategy proposed in this study provides a practical path for UEV companies to enhance consumer trust and purchase willingness and promote the industry’s sustainable development. Full article

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17 pages, 2166 KiB

Open AccessArticle

Dyn-Pri: A Dynamic Privacy Sensitivity Assessment Framework for V2G Interactive Service Scenarios

by Tianbao Liu, Jingyang Wang, Nan Zhang, Jing Guo, Yanyan Tao, Qingyao Li and Zi Li

World Electr. Veh. J. 2025, 16(8), 459; https://doi.org/10.3390/wevj16080459 - 11 Aug 2025

In V2G service operations, highly efficient data sharing among participants is useful in grid load balancing and renewable energy integration. However, the data quality and sharing efficiency greatly rely on entities’ willingness to share. Moreover, there is no rational assessment framework for the [...] Read more.

In V2G service operations, highly efficient data sharing among participants is useful in grid load balancing and renewable energy integration. However, the data quality and sharing efficiency greatly rely on entities’ willingness to share. Moreover, there is no rational assessment framework for the privacy sensitivity of sharing data, which highly affects data sharing willingness. Existing privacy sensitivity assessment methods rely on static privacy attributes and fail to rationally assess privacy threats within V2G service scenarios. To address these limitations, this paper proposes Dyn-Pri, a novel multi-dimensional privacy sensitivity assessment framework for large-scale V2G interactive service scenarios. Dyn-Pri features an adaptive comprehensive multi-dimensional quantification model that integrates both the three privacy elements’ intrinsic effects and the dynamic, intertwining influences among them. The experimental validations in three typical V2G scenarios demonstrate that Dyn-Pri has significant advantages in the precision of sensitivity assessments, and balancing data utilization and privacy protection enhances renewable energy integration efficiency while ensuring cross-domain data security. Full article

(This article belongs to the Topic Advances in Electric Vehicle Charging Systems and Vehicle-to-Grid Technology)

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26 pages, 5479 KiB

Open AccessArticle

A Bibliometric Analysis of the Research on Electromobility and Its Implications for Kuwait

by Hidab Hamwi, Andri Ottesen, Rajeev Alasseri and Sara Aldei

World Electr. Veh. J. 2025, 16(8), 458; https://doi.org/10.3390/wevj16080458 - 11 Aug 2025

This article examines the evolution of the most extensively researched subjects in e-mobility during the previous two decades. The objective of this analysis is to identify the lessons that the State of Kuwait, which is falling behind other nations in terms of e-mobility, [...] Read more.

This article examines the evolution of the most extensively researched subjects in e-mobility during the previous two decades. The objective of this analysis is to identify the lessons that the State of Kuwait, which is falling behind other nations in terms of e-mobility, can learn from in its efforts to adopt electric vehicles (EVs). To strengthen the body of knowledge and determine the most effective and efficient route to an “EV-ready” nation, the authors compiled data on the latest developments in the EV industry. A bibliometric analysis was performed on 3962 articles using VOSviewer software, which identified six noteworthy clusters that warranted further discussion. Additionally, we examined the sequential progression of these clusters as follows: (1) the environmental ramifications of electric mobility; (2) advancements in EV technology, including range extension and soundless engines, as well as the capital expenditure (CAPEX) and operating expenditure (OPEX) of purchasing and operating EVs; (3) concerns regarding the effectiveness and durability of EV batteries; (4) the availability of EV charging stations and grid integration; (5) charging time; and, finally, (6) the origin and source of the energy used in the development of e-mobility. Delineating critical aspects in the development of e-mobility can help to equip policymakers and decision makers in Kuwait in formulating timely and economical choices pertaining to sustainable transportation. This study contributes by cross-walking six global bibliometric clusters to Kuwait’s ten EV adoption barriers and mapping each to actionable policy levers, linking evidence to deployment guidance for an emerging market grid. Unlike prior bibliometric overviews, our analysis is Kuwait-specific and heat-contextual, and it reports each cluster’s size and recency to show where the field is moving. Using Kuwait driving logs, we found that summer (avg 43.2 °C) reduced the effective full-charge range by 24% versus pre-winter (approximately 244 km vs. 321 km), underscoring the need for shaded PV-coupled hyper-hubs and active thermal management. Full article

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18 pages, 5865 KiB

Open AccessArticle

Multi-Lane Congestion Control Model for Intelligent Connected Vehicles Integrating Optimal Traffic Flow Difference Information in V2X Environment

by Li Zhou, Chuan Tian and Shuhong Yang

World Electr. Veh. J. 2025, 16(8), 457; https://doi.org/10.3390/wevj16080457 - 11 Aug 2025

In the V2X environment, intelligent connected vehicles can obtain multi-dimensional traffic flow data in real time through the vehicle–road collaborative cyber–physical fusion system. Based on this, this study proposes a multi-lane traffic flow lattice model integrating optimal traffic flow difference estimation information to [...] Read more.

In the V2X environment, intelligent connected vehicles can obtain multi-dimensional traffic flow data in real time through the vehicle–road collaborative cyber–physical fusion system. Based on this, this study proposes a multi-lane traffic flow lattice model integrating optimal traffic flow difference estimation information to effectively suppress traffic congestion. The linear stability criterion of the system is derived through linear stability analysis, proving that the optimal traffic flow difference estimation can significantly expand the stable region and suppress traffic fluctuations caused by small disturbances. Furthermore, the perturbation method is used to derive the mKdV equation near the critical stability point of the system, revealing the nonlinear characteristics of traffic congestion propagating in the form of kink solitary waves, and indicating that the new consideration effect can effectively slow down the congestion propagation speed by adjusting the parameters of solitary waves (such as wave speed and amplitude). The numerical simulation results show that compared to the traditional model, the improved model exhibits enhanced traffic flow stability and robustness. Meanwhile, it reveals the nonlinear relationship between the increase of the number of lanes and the alleviation of congestion, and there is an optimal lane configuration threshold. The research results not only provide theoretical support for the optimization of traffic flow efficiency in intelligent transportation systems, but also provide a decision-making basis for dynamic lane management strategies in the V2X environment. Full article

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19 pages, 3018 KiB

Open AccessArticle

Research on the Spatiotemporal Patterns of New Energy Vehicle Promotion Level in China

by Yanmei Wang, Fanlong Zeng and Mingke He

World Electr. Veh. J. 2025, 16(8), 456; https://doi.org/10.3390/wevj16080456 - 11 Aug 2025

Exploring the regional disparities in and spatiotemporal evolution of the new energy vehicle promotion level (NEVPL) is essential for facilitating low-carbon and environmentally sustainable development. This study employs a multidimensional index system to assess the NEVPL across 31 Chinese provinces from 2017 to [...] Read more.

Exploring the regional disparities in and spatiotemporal evolution of the new energy vehicle promotion level (NEVPL) is essential for facilitating low-carbon and environmentally sustainable development. This study employs a multidimensional index system to assess the NEVPL across 31 Chinese provinces from 2017 to 2023, utilizing data on NEV ownership, annual NEV sales, the number of public charging piles, and the vehicle-to-pile ratio. The NEVPL scores were estimated using the Entropy-TOPSIS method. Spatial patterns and the mechanisms of regional disparities were examined using a suite of spatial analytical techniques, including the standard deviation ellipse (SDE), gravity center analysis, Dagum Gini coefficient decomposition, and kernel density estimation. The results reveal three principal findings. First, NEVPL exhibited a sustained upward trend nationwide. The eastern region consistently maintained a leading position, the central and western regions demonstrated steady growth, and the northeastern region remained underdeveloped, leading to an expanding regional gap. Second, spatial distribution transitioned from an early dispersed pattern to a structure characterized by both agglomeration and differentiation. The promotional center gradually shifted toward the southeast, and high-value regions became increasingly isolated, forming island-like clusters. Third, spatial inequality was mainly driven by inter-regional differences, which contributed to over 70 percent of the total variance. The rising intra-regional disparity within the eastern region emerged as the predominant factor widening the national gap. These findings offer empirical evidence to support the refinement of new energy vehicle (NEV) policy frameworks and the promotion of balanced regional development. Full article

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28 pages, 2274 KiB

Open AccessArticle

Vibration Control and Energy-Regenerative Performance Analysis of an Energy-Regenerative Magnetorheological Semi-Active Suspension

by Wenkai Wei, Jiayu Lu, Cao Tan, Haodong Wu and Xiaoxuan Xie

World Electr. Veh. J. 2025, 16(8), 455; https://doi.org/10.3390/wevj16080455 - 10 Aug 2025

To improve both ride comfort and energy efficiency, this study proposes a semi-active suspension system equipped with an electromagnetic linear energy-regenerative magnetorheological damper (ELEMRD). The ELEMRD integrates a magnetorheological damper (MRD) with a linear generator. A neural network-based surrogate model was employed to [...] Read more.

To improve both ride comfort and energy efficiency, this study proposes a semi-active suspension system equipped with an electromagnetic linear energy-regenerative magnetorheological damper (ELEMRD). The ELEMRD integrates a magnetorheological damper (MRD) with a linear generator. A neural network-based surrogate model was employed to optimize the key parameters of the linear generator for better compatibility with semi-active suspensions. A prototype was fabricated and tested. Experimental results show that with an excitation current of 1.5 A, the prototype generates a peak output force of 1415 N. Under harmonic excitation at 5 Hz, the no-load regenerative power reaches 11.1 W and 37.3 W at vibration amplitudes of 5 mm and 10 mm, respectively. An energy-regenerative magnetorheological semi-active suspension model was developed and controlled using a Linear Quadratic Regulator (LQR). Results indicate that, on a Class C road at 20 m/s, the proposed system reduces sprung mass acceleration and suspension working space by 14.2% and 7.5% compared to a passive suspension. The root mean square and peak regenerative power reach 49.8 W and 404.2 W, respectively. The proposed semi-active suspension also exhibits enhanced low-frequency vibration isolation, demonstrating its effectiveness in improving ride quality while achieving energy recovery. Full article

(This article belongs to the Special Issue Vehicle System Dynamics and Intelligent Control for Electric Vehicles)

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26 pages, 7480 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 - 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)

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