Next Issue
Volume 16, October
Previous Issue
Volume 16, August
 
 

World Electr. Veh. J., Volume 16, Issue 9 (September 2025) – 66 articles

Cover Story (view full-size image): This paper investigates the energy consumption of a fully electric vehicle fleet in urban traffic using a validated SUMO simulation of Darmstadt and detailed powertrain models. A key finding is that secondary consumption, such as HVAC and onboard systems, accounts for up to half of total energy demand in dense traffic, as longer travel times amplify their impact relative to traction consumption. While overall driving consumption varies only slightly between traffic scenarios, secondary loads significantly increase fleet demand during congestion. Compared to an ICE vehicle fleet, battery electric fleets reduce energy use and CO₂ emissions by more than half. The results underline the importance of traffic flow coordination and efficient auxiliary systems for sustainable urban e-mobility. View this paper
  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Section
Select all
Export citation of selected articles as:
29 pages, 1150 KB  
Article
Game-Aware MPC-DDP for Mixed Traffic: Safe, Efficient, and Comfortable Interactive Driving
by Zhenhua Wang, Zheng Wu, Shiguang Hu, Fujiang Yuan and Junye Yang
World Electr. Veh. J. 2025, 16(9), 544; https://doi.org/10.3390/wevj16090544 - 22 Sep 2025
Viewed by 390
Abstract
In recent years, achieving safety, efficiency, and comfort among interactive automated driving has been a formidable challenge. Model-based approaches, such as game-theoretic and robust control methods, often result in overly cautious decisions or suboptimal solutions. In contrast, learning-based techniques typically demand high computational [...] Read more.
In recent years, achieving safety, efficiency, and comfort among interactive automated driving has been a formidable challenge. Model-based approaches, such as game-theoretic and robust control methods, often result in overly cautious decisions or suboptimal solutions. In contrast, learning-based techniques typically demand high computational resources and lack interpretability. At the same time, simpler strategies that rely on static assumptions tend to underperform in rapidly evolving traffic environments. To address these limitations, we propose a novel game-based MPC-DDP framework that integrates game-theoretic predictions of human-driven vehicle (HDV) with a Dynamic Differential Programming (DDP) solver under a receding-horizon setting. Our method dynamically adjusts the autonomous vehicle’s (AV) control inputs in response to real-time human-driven vehicle (HDV) behavior. This enables an effective balance between safety and efficiency. Experimental evaluations on lane-change and intersection scenarios demonstrate that the proposed approach achieves smoother trajectories, higher average speeds when needed, and larger safety margins in high-risk conditions. Comparisons against state-of-the-art baselines confirm its suitability for complex, interactive driving environments. Full article
(This article belongs to the Special Issue Recent Advances in Autonomous Vehicles)
Show Figures

Figure 1

28 pages, 1307 KB  
Article
Examining the Influence of Technological Perception, Cost, and Accessibility on Electric Vehicle Consumer Behavior in Thailand
by Adisak Suvittawat, Nutchanon Suvittawat and Buratin Khampirat
World Electr. Veh. J. 2025, 16(9), 543; https://doi.org/10.3390/wevj16090543 - 22 Sep 2025
Viewed by 510
Abstract
This study investigates consumer behavior in electric vehicle (EV) adoption, focusing on how factors like convenience, accessibility, technological perception, and cost influence the travel patterns and usage behavior of EV drivers in Thailand. This study aims to address the research gap in the [...] Read more.
This study investigates consumer behavior in electric vehicle (EV) adoption, focusing on how factors like convenience, accessibility, technological perception, and cost influence the travel patterns and usage behavior of EV drivers in Thailand. This study aims to address the research gap in the comparative behavior between electric vehicles and public transport in a developing country. Using a quantitative approach, the study collected data via surveys distributed online and face-to-face interviews with a stratified sample of 398 respondents. The survey assessed the relationships between convenience and accessibility, technology perception, cost of ownership, and travel patterns using structural equation modeling (SEM). The findings reveal that convenience and accessibility significantly affect consumer perceptions of technology and the cost of ownership, which, in turn, influences travel patterns. Technology perception and performance serve as partial mediators, suggesting that improving the infrastructure enhances EV adoption. Additionally, the cost of ownership, including long-term savings, positively impacts usage behavior. This study provides key insights for policymakers and urban planners aiming to promote the adoption of EVs. Enhancing charging infrastructure, offering government incentives, and improving public awareness of long-term cost benefits are recommended strategies. These findings are particularly relevant in urban environments and offer guidance for developing infrastructure policies that align with consumer preferences. Full article
(This article belongs to the Section Marketing, Promotion and Socio Economics)
Show Figures

Figure 1

26 pages, 8533 KB  
Review
The Energy Management Strategies for Fuel Cell Electric Vehicles: An Overview and Future Directions
by Jinquan Guo, Hongwen He, Chunchun Jia and Shanshan Guo
World Electr. Veh. J. 2025, 16(9), 542; https://doi.org/10.3390/wevj16090542 - 22 Sep 2025
Viewed by 775
Abstract
The rapid development of fuel cell electric vehicles (FCEVs) has highlighted the critical importance of optimizing energy management strategies to improve vehicle performance, energy efficiency, durability, and reduce hydrogen consumption and operational costs. However, existing approaches often face limitations in real-time applicability, adaptability [...] Read more.
The rapid development of fuel cell electric vehicles (FCEVs) has highlighted the critical importance of optimizing energy management strategies to improve vehicle performance, energy efficiency, durability, and reduce hydrogen consumption and operational costs. However, existing approaches often face limitations in real-time applicability, adaptability to varying driving conditions, and computational efficiency. This paper aims to provide a comprehensive review of the current state of FCEV energy management strategies, systematically classifying methods and evaluating their technical principles, advantages, and practical limitations. Key techniques, including optimization-based methods (dynamic programming, model predictive control) and machine learning-based approaches (reinforcement learning, deep neural networks), are analyzed and compared in terms of energy distribution efficiency, computational demand, system complexity, and real-time performance. The review also addresses emerging technologies such as artificial intelligence, vehicle-to-everything (V2X) communication, and multi-energy collaborative control. The outcomes highlight the main bottlenecks in current strategies, their engineering applicability, and potential for improvement. This study provides theoretical guidance and practical reference for the design, implementation, and advancement of intelligent and adaptive energy management systems in FCEVs, contributing to the broader goal of efficient and low-carbon vehicle operation. Full article
(This article belongs to the Section Vehicle and Transportation Systems)
Show Figures

Figure 1

20 pages, 1689 KB  
Article
Prediction of Motor Rotor Temperature Using TCN-BiLSTM-MHA Model Based on Hybrid Grey Wolf Optimization Algorithm
by Changzhi Lv, Guangbo Lin, Dongxin Xu, Zhongxin Song and Di Fan
World Electr. Veh. J. 2025, 16(9), 541; https://doi.org/10.3390/wevj16090541 - 22 Sep 2025
Viewed by 363
Abstract
The permanent magnet synchronous motor (PMSM) is the core of new energy vehicle drive systems, and its temperature status is directly related to the safety of the entire vehicle. However, the temperature of rotor permanent magnets is difficult to measure directly, and traditional [...] Read more.
The permanent magnet synchronous motor (PMSM) is the core of new energy vehicle drive systems, and its temperature status is directly related to the safety of the entire vehicle. However, the temperature of rotor permanent magnets is difficult to measure directly, and traditional sensor schemes are costly and complex to deploy. With the development of Artificial Intelligence (AI) technology, deep learning (DL) provides a feasible path for sensorless modeling. This paper proposes a prediction model that integrates a Temporal Convolutional Network (TCN), Bidirectional Long Short-Term Memory Network (BiLSTM), and multi-head attention mechanism (MHA) and introduces a Hybrid Grey Wolf Optimizer (H-GWO) for hyperparameter optimization, which is applied to PMSM temperature prediction. A public dataset from Paderborn University is used for training and testing. The test set verification results show that the H-GWO-optimized TCN-BiLSTM-MHA model has a mean absolute error (MAE) of 0.3821 °C, a root mean square error (RMSE) of 0.4857 °C, and an R2 of 0.9985. Compared with the CNN-BiLSTM-Attention model, the MAE and RMSE are reduced by approximately 11.8% and 19.3%, respectively. Full article
(This article belongs to the Section Propulsion Systems and Components)
Show Figures

Figure 1

28 pages, 1632 KB  
Review
Surface Waviness of EV Gears and NVH Effects—A Comprehensive Review
by Krisztian Horvath and Daniel Feszty
World Electr. Veh. J. 2025, 16(9), 540; https://doi.org/10.3390/wevj16090540 - 22 Sep 2025
Viewed by 703
Abstract
Electric vehicle (EV) drivetrains operate at high rotational speeds, which makes the noise, vibration, and harshness (NVH) performance of gear transmissions a critical design factor. Without the masking effect of an internal combustion engine, gear whine can become a prominent source of passenger [...] Read more.
Electric vehicle (EV) drivetrains operate at high rotational speeds, which makes the noise, vibration, and harshness (NVH) performance of gear transmissions a critical design factor. Without the masking effect of an internal combustion engine, gear whine can become a prominent source of passenger discomfort. This paper provides the first comprehensive review focused specifically on gear tooth surface waviness, a subtle manufacturing-induced deviation that can excite tonal noise. Periodic, micron-scale undulations caused by finishing processes such as grinding may generate non-meshing frequency “ghost orders,” leading to tonal complaints even in high-quality gears. The article compares finishing technologies including honing and superfinishing, showing their influence on waviness and acoustic behavior. It also summarizes modern waviness detection techniques, from single-flank rolling tests to optical scanning systems, and highlights data-driven predictive approaches using machine learning. Industrial case studies illustrate the practical challenges of managing waviness, while recent proposals such as controlled surface texturing are also discussed. The review identifies gaps in current research: (i) the lack of standardized waviness metrics for consistent comparison across studies; (ii) the limited validation of digital twin approaches against measured data; and (iii) the insufficient integration of machine learning with physics-based models. Addressing these gaps will be essential for linking surface finish specifications with NVH performance, reducing development costs, and improving passenger comfort in EV transmissions. Full article
Show Figures

Figure 1

18 pages, 1367 KB  
Article
Torque Smoothness for a Modified W-Type Inverter-Fed Three-Phase Induction Motor with Finite Set Model Predictive Control for Electric Vehicles
by Muhammad Ayyaz Tariq, Syed Abdul Rahman Kashif, Akhtar Rasool and Ahmed Ali
World Electr. Veh. J. 2025, 16(9), 539; https://doi.org/10.3390/wevj16090539 - 22 Sep 2025
Viewed by 478
Abstract
Ripples in the electromagnetic torque of electric vehicle (EV) motors due to poor stator voltage and control cause jerky movements, equipment failure, discomfort for passengers and drivers, and damage to the associated civil works. This paper presents the implementation of Finite Control Set [...] Read more.
Ripples in the electromagnetic torque of electric vehicle (EV) motors due to poor stator voltage and control cause jerky movements, equipment failure, discomfort for passengers and drivers, and damage to the associated civil works. This paper presents the implementation of Finite Control Set Model Predictive Control (FCSMPC) for a high-level modified W-type inverter (MWI) driving a three-phase induction motor (IM), along with validation of its performance. The proposed control strategy aims to minimize motor torque ripples and has been tested under various driving torque patterns. The results demonstrate a significant reduction in torque ripples—down to less than 1%—and acceptable levels of total harmonic distortion (THD), as verified through quality analysis of the stator currents. Moreover, a comparative assessment of voltage profiles for the electromagnetic torque and rotor speed curves has been presented for nine cases of simultaneous variations in multiple motor parameters; the results indicate that the MWI-fed motor has the best performance and the lowest sensitivity to the variations. Full article
Show Figures

Figure 1

19 pages, 6310 KB  
Article
Enhanced A*–Fuzzy DWA Hybrid Algorithm for AGV Path Planning in Confined Spaces
by Yang Xu and Wei Liu
World Electr. Veh. J. 2025, 16(9), 538; https://doi.org/10.3390/wevj16090538 - 22 Sep 2025
Viewed by 439
Abstract
Addressing the challenges of inefficient prolonged trajectory resolution and unreliable dynamic obstacle avoidance for intelligent vehicles in complex confined environments, this study proposes an innovative hybrid path planning method. Its core novelty is the deep integration of an enhanced A* algorithm for smooth [...] Read more.
Addressing the challenges of inefficient prolonged trajectory resolution and unreliable dynamic obstacle avoidance for intelligent vehicles in complex confined environments, this study proposes an innovative hybrid path planning method. Its core novelty is the deep integration of an enhanced A* algorithm for smooth global planning with a fuzzy logic-controlled Dynamic Window Approach (DWA). The enhanced A* generates efficient and smooth global paths, while fuzzy control significantly improves DWA’s robustness in dynamic, uncertain scenarios. This hybrid strategy achieves efficient synergy between global optimality and local reactive obstacle avoidance. Simulations demonstrate its superiority over conventional A* or DWA in path length, planning efficiency, and obstacle avoidance success rate. Experimental validation on a physical platform in simulated complex scenarios shows an average trajectory deviation ≤ 7.14%. The work provides an effective theoretical and methodological framework for navigation in constrained spaces, offering significant value for practical applications like logistics and automated parking. Full article
(This article belongs to the Section Automated and Connected Vehicles)
Show Figures

Figure 1

26 pages, 1938 KB  
Article
A Demand Factor Analysis for Electric Vehicle Charging Infrastructure
by Vyacheslav Voronin, Fedor Nepsha and Pavel Ilyushin
World Electr. Veh. J. 2025, 16(9), 537; https://doi.org/10.3390/wevj16090537 - 21 Sep 2025
Viewed by 562
Abstract
This paper investigates the factors influencing the power consumption of electric vehicle (EV) charging infrastructure and develops a methodology for determining the design electrical loads of EV charging stations (EVCSs). A comprehensive review of existing research on demand factor (DF) calculations for EVCSs [...] Read more.
This paper investigates the factors influencing the power consumption of electric vehicle (EV) charging infrastructure and develops a methodology for determining the design electrical loads of EV charging stations (EVCSs). A comprehensive review of existing research on demand factor (DF) calculations for EVCSs is presented, highlighting discrepancies in current approaches and identifying key influencing factors. To address these gaps, a simulation model was developed in Python 3.11.9, generating minute-by-minute power consumption profiles based on EVCS parameters, EV fleet characteristics, and charging behavior patterns. In contrast with state-of-the-art methods that often provide limited reference values or scenario-specific analyses, this study quantifies the influence of key factors and demonstrates that the average number of daily charging sessions, EVCS power rating, and the number of charging ports are the most significant determinants of DF. For instance, increasing the number of sessions from 0.5 to 4 per day raises DF by 2.4 times, while higher EVCS power ratings reduce DF by 32–56%. This study proposes a practical generalized algorithm for calculating DF homogeneous and heterogeneous EVCS groups. The proposed model demonstrates superior accuracy (MAPE = 6.01%, R2 = 0.987) compared with existing SOTA approaches, which, when applied to our dataset, yielded significantly higher errors (MAPE of 50.36–67.72%). The derived expressions enable efficient planning of distribution networks, minimizing overestimation of design loads and associated infrastructure costs. This work contributes to the field by quantifying the impact of behavioral and technical factors on EVCS power consumption, offering a robust tool for grid planners and policymakers to optimize EV charging infrastructure deployment. Full article
Show Figures

Graphical abstract

25 pages, 4454 KB  
Article
Investigation of Flow Channel Configurations in Liquid-Cooled Plates for Electric Vehicle Battery Thermal Management
by Muhammad Hasan Albana, Ninda Hardina Batubara, Novebriantika Novebriantika, Meschac Timothee Silalahi, Yogantara Yogantara and Harus Laksana Guntur
World Electr. Veh. J. 2025, 16(9), 536; https://doi.org/10.3390/wevj16090536 - 19 Sep 2025
Viewed by 473
Abstract
Mitigating heat generation in electric vehicle (EV) batteries is crucial for safety, operational efficiency, and battery lifespan. Liquid-cooled cold plates are widely used; however, comparative studies of channel geometries are often hindered by inconsistent experimental conditions. This study systematically compares six cold plate [...] Read more.
Mitigating heat generation in electric vehicle (EV) batteries is crucial for safety, operational efficiency, and battery lifespan. Liquid-cooled cold plates are widely used; however, comparative studies of channel geometries are often hindered by inconsistent experimental conditions. This study systematically compares six cold plate configurations under identical cross-sectional areas and uniform thermal boundary conditions. These controls isolate the effect of geometry on performance. Computational fluid dynamics (CFDs) was used to evaluate six configurations, derived from three main channel layouts (serpentine with eight U-turns, serpentine with six U-turns, and elliptical) and two cross-sectional shapes (circular and square). The serpentine square-tube design with eight U-turns exhibited the lowest thermal resistance (0.0159 K/W). The circular-tube variant achieved the most uniform temperature distribution (TUI > 0.53). The six U-turn circular-tube configuration demonstrated the lowest pressure drop (11.7 kPa). The results indicate that no single design optimizes all performance metrics, highlighting trade-offs between cooling effectiveness, temperature uniformity, and hydraulic efficiency. By isolating geometric variables, this study offers targeted design recommendations for engineers developing battery thermal management systems (BTMS). Full article
(This article belongs to the Section Storage Systems)
Show Figures

Figure 1

19 pages, 3729 KB  
Article
Optimal Design of Dual Pantograph Parameters for Electrified Roads
by Libo Yuan, Wei Zhou, Huifu Jiang, Yongjian Ma and Sijun Huang
World Electr. Veh. J. 2025, 16(9), 535; https://doi.org/10.3390/wevj16090535 - 19 Sep 2025
Viewed by 306
Abstract
Electrified roads represent an emerging transportation solution in the context of global energy transition. These systems enable vehicles equipped with roof-mounted pantographs to draw power from overhead contact lines while in motion, allowing continuous energy replenishment. The effectiveness of this energy transfer—namely, the [...] Read more.
Electrified roads represent an emerging transportation solution in the context of global energy transition. These systems enable vehicles equipped with roof-mounted pantographs to draw power from overhead contact lines while in motion, allowing continuous energy replenishment. The effectiveness of this energy transfer—namely, the quality of pantograph–catenary interaction—is significantly influenced by the pantograph’s equivalent mechanical parameters. This study develops a three-dimensional overhead catenary model and a five-mass pantograph model tailored to electrified roads. Under conditions of road surface irregularities, it investigates how variations in equivalent pantograph parameters affect key contact performance indicators. Simulation results are used to identify a new set of equivalent pantograph parameters that significantly improve the overall quality of pantograph–catenary interaction compared to the baseline configuration. Sensitivity analysis further reveals that, under road-induced excitation, pan-head stiffness is the most critical factor affecting contact performance, while pan-head damping, upper frame stiffness, and upper frame damping show minimal influence. By constructing a coupled dynamic model and conducting parameter optimization, this study elucidates the role of key pantograph parameters for electrified roads in determining contact performance. The findings provide a theoretical foundation for future equipment development and technological advancement. Full article
(This article belongs to the Section Energy Supply and Sustainability)
Show Figures

Figure 1

17 pages, 2205 KB  
Article
Research on Yaw Stability Control for Distributed-Drive Pure Electric Pickup Trucks
by Zhi Yang, Yunxing Chen, Qingsi Cheng and Huawei Wu
World Electr. Veh. J. 2025, 16(9), 534; https://doi.org/10.3390/wevj16090534 - 19 Sep 2025
Viewed by 428
Abstract
To address the issue of poor yaw stability in distributed-drive electric pickup trucks at medium-to-high speeds, particularly under the influence of continuously varying tire forces and road adhesion coefficients, a novel Kalman filter-based method for estimating the road adhesion coefficient, combined with a [...] Read more.
To address the issue of poor yaw stability in distributed-drive electric pickup trucks at medium-to-high speeds, particularly under the influence of continuously varying tire forces and road adhesion coefficients, a novel Kalman filter-based method for estimating the road adhesion coefficient, combined with a Tube-based Model Predictive Control (Tube-MPC) algorithm, is proposed. This integrated approach enables real-time estimation of the dynamically changing road adhesion coefficient while simultaneously ensuring vehicle yaw stability is maintained under rapid response requirements. The developed hierarchical yaw stability control architecture for distributed-drive electric pickup trucks employs a square root cubature Kalman filter (SRCKF) in its upper layer for accurate road adhesion coefficient estimation; this estimated coefficient is subsequently fed into the intermediate layer’s corrective yaw moment solver where Tube-based Model Predictive Control (Tube-MPC) tracks desired sideslip angle and yaw rate trajectories to derive the stability-critical corrective yaw moment, while the lower layer utilizes a quadratic programming (QP) algorithm for precise four-wheel torque distribution. The proposed control strategy was verified through co-simulation using Simulink and Carsim, with results demonstrating that, compared to conventional MPC and PID algorithms, it significantly improves both the driving stability and control responsiveness of distributed-drive electric pickup trucks under medium- to high-speed conditions. Full article
(This article belongs to the Special Issue Vehicle Control and Drive Systems for Electric Vehicles)
Show Figures

Figure 1

20 pages, 1372 KB  
Article
Cooperative Estimation Method for SOC and SOH of Lithium-Ion Batteries Based on Fractional-Order Model
by Guoping Lei, Tian-Ao Wu, Tao Chen, Juan Yan and Xiaojiang Zou
World Electr. Veh. J. 2025, 16(9), 533; https://doi.org/10.3390/wevj16090533 - 19 Sep 2025
Viewed by 396
Abstract
To overcome the limitations of traditional integer-order models, which fail to accurately capture the dynamic behavior of lithium-ion batteries, and to improve the insufficient accuracy of state of charge (SOC) and state of health (SOH) collaborative estimation, this study proposes a cooperative estimation [...] Read more.
To overcome the limitations of traditional integer-order models, which fail to accurately capture the dynamic behavior of lithium-ion batteries, and to improve the insufficient accuracy of state of charge (SOC) and state of health (SOH) collaborative estimation, this study proposes a cooperative estimation framework based on a fractional-order model. First, a fractional-order second-order RC equivalent circuit model is established, and the whale optimization algorithm is applied for offline parameter identification to improve model accuracy. Second, a strong tracking strategy is introduced into the improved unscented Kalman filter to address the convergence speed issue under inaccurate initial SOC conditions. Meanwhile, the extended Kalman filter is employed for SOH estimation and online parameter identification. Furthermore, a multi-time-scale collaborative estimation algorithm is proposed to enhance overall estimation accuracy. Experimental results under three dynamic operating conditions driving cycles demonstrate that the proposed method effectively solves the SOC/SOH collaborative estimation problem, achieving a mean SOC estimation error of 0.45% and maintaining the SOH estimation error within 0.25%. Full article
(This article belongs to the Section Storage Systems)
Show Figures

Figure 1

21 pages, 781 KB  
Article
A Resilience Entropy-Based Framework for V2G Charging Station Siting and Resilient Reconfiguration of Power Distribution Networks Under Disasters
by Chutao Zheng, Fawen Chen, Zeli Xi, Guowei Guo, Xinsen Yang and Cong Chen
World Electr. Veh. J. 2025, 16(9), 532; https://doi.org/10.3390/wevj16090532 - 19 Sep 2025
Viewed by 407
Abstract
In the post-disaster recovery of power distribution networks (PDNs), electric vehicles (EVs) possess a great potential as mobile energy storage units. When supported by vehicle-to-grid (V2G)-enabled charging stations, EVs can provide effective supplementary power for disaster-stricken areas. However, most existing stations only support [...] Read more.
In the post-disaster recovery of power distribution networks (PDNs), electric vehicles (EVs) possess a great potential as mobile energy storage units. When supported by vehicle-to-grid (V2G)-enabled charging stations, EVs can provide effective supplementary power for disaster-stricken areas. However, most existing stations only support unidirectional charging, limiting the resilience-enhancing potential of V2G. To address this gap, this paper proposes a resilience-oriented restoration optimization model that jointly considers the siting of V2G-enabled charging stations and PDN topology reconfiguration. A novel metric—Resilience Entropy—is introduced to dynamically characterize the recovery process. The model explicitly describes fault propagation and circuit breaker operations, while incorporating power flow and radial topology constraints to ensure secure operation. EV behavioral uncertainty is also considered to enhance model adaptability under real-world post-disaster conditions. The optimal siting scheme is obtained by solving the proposed model. Case studies demonstrate the model’s effectiveness in improving post-disaster supply and recovery efficiency, and analyze the impact of user participation willingness on V2G-based restoration. Full article
(This article belongs to the Section Charging Infrastructure and Grid Integration)
Show Figures

Figure 1

23 pages, 737 KB  
Article
Electric Vehicle Charging: A Business Intelligence Model
by Alexandra Bousia
World Electr. Veh. J. 2025, 16(9), 531; https://doi.org/10.3390/wevj16090531 - 18 Sep 2025
Viewed by 378
Abstract
The adoption of electric vehicles (EVs) has grown substantially in recent years, offering a cleaner and highly promising pathway toward the decarbonization of urban environments. However, this trend introduces new challenges in charging infrastructure and management. This paper proposes a synergistic integration of [...] Read more.
The adoption of electric vehicles (EVs) has grown substantially in recent years, offering a cleaner and highly promising pathway toward the decarbonization of urban environments. However, this trend introduces new challenges in charging infrastructure and management. This paper proposes a synergistic integration of Business Intelligence (BI) and Artificial Intelligence (AI) techniques—including machine learning and data analytics—for solving the EV charging problem. We begin with an in-depth analysis of charging behaviors, leveraging extensive datasets from EVs, charging stations (CSs), and auxiliary sources. Based on this analysis, we introduce a BI framework utilizing advanced data mining methods to utilize large-scale data effectively. We then present a BI-based decision-making model that enables comprehensive analysis and optimized solutions for EV charge scheduling and the cooperation among different CS owners. The model is validated across multiple real-world scenarios and case studies, demonstrating significant improvements in charging efficiency, utilization, and reliability. By showcasing the practical applications of BI-driven analytics, our findings underscore the transformative impact of data-informed methodologies on EV charging operations. This paper concludes with a discussion of open research opportunities in AI- and BI-driven intelligent transportation—specifically in EV charging optimization, grid integration, and predictive analytics. Full article
Show Figures

Figure 1

27 pages, 12572 KB  
Article
Application of Hybrid-Electric Propulsion to ‘Large-Cabin’ Business Aircraft
by Ambar Sarup
World Electr. Veh. J. 2025, 16(9), 530; https://doi.org/10.3390/wevj16090530 - 18 Sep 2025
Viewed by 453
Abstract
This paper aims to fill a critical cap in hybrid-electric propulsion (HEP) research by investigating the feasibility of its application on a ‘large-cabin’ business aircraft by 2040, for which key requirements are a long range of at least 6297 km (3400 nmi), and [...] Read more.
This paper aims to fill a critical cap in hybrid-electric propulsion (HEP) research by investigating the feasibility of its application on a ‘large-cabin’ business aircraft by 2040, for which key requirements are a long range of at least 6297 km (3400 nmi), and a cruise speed of Mach 0.85. Based upon a representative baseline ‘large-cabin’ aircraft, a time-stepping simulation for the distinct phases of an NBAA mission, consisting of takeoff, climb, cruise, landing, and a reserve segment is developed for turbofan, series, and parallel architectures. The simulation enables analysis of range, specific air range, battery weight, battery volume, and energy consumption for various degrees of hybridization and battery specific energy densities. The results find that while both series and parallel architectures are able to meet the requisite range targets, the parallel architecture is better suited as the overall drivetrain weight is lower. The parallel HEP architecture enables the aircraft to fly a maximum distance of 7082 km (3824 nmi), with a 5% energy hybridization. Over a typical 5556 km (3000 nmi) mission this equates to fuel savings of 847 kg compared to a turbofan. The HEP ‘large-cabin’ aircraft is viable provided battery technology reaches a specific energy density of at least 800 Wh/kg. Full article
(This article belongs to the Special Issue Electric and Hybrid Electric Aircraft Propulsion Systems)
Show Figures

Figure 1

23 pages, 7026 KB  
Article
Modeling, Simulation, and Performance Evaluation of a Commercial Electric Scooter
by Sajad Solgi, Andreas Stadler, Kazem Pourhossein, Amra Jahic, Maik Plenz and Detlef Schulz
World Electr. Veh. J. 2025, 16(9), 529; https://doi.org/10.3390/wevj16090529 - 18 Sep 2025
Viewed by 488
Abstract
As electric scooters (e-scooters) continue to populate city streets and gain popularity as a key mode of micro-mobility, issues such as their energy consumption and demand from the power grid, as well as optimizing their electrical systems, become increasingly important. Improving performance requires [...] Read more.
As electric scooters (e-scooters) continue to populate city streets and gain popularity as a key mode of micro-mobility, issues such as their energy consumption and demand from the power grid, as well as optimizing their electrical systems, become increasingly important. Improving performance requires a deep understanding of their electrical behavior and the design of smart control strategies. This paper presents a detailed analysis of the entire electrical system of commercial electric scooters, with a particular focus on the performance of key components such as the permanent magnet brushless direct current motor and the lithium-ion battery system. The study involves modeling and simulation of motor control, battery management, and DC-link voltage stabilization using MATLAB/Simulink. The simulations are complemented by laboratory measurements of the motor performance in an SXT Scooters MAX unit under various operating conditions. Additionally, a complete battery charging cycle is analyzed to evaluate charging characteristics and usable energy storage capacity. This paper presents a first step for researchers interested in studying the electrical systems of e-scooters. Additionally, it can serve as educational material for electrical engineers in the field of e-scooters. Full article
Show Figures

Figure 1

15 pages, 3348 KB  
Article
Performance of Electric Bus Batteries in Rollover Scenarios According to ECE R66 and R100 Standards
by Alexsandro Sordi, Bruno Gabriel Menino, Gabriel Isoton Pistorello, Vagner do Nascimento and Giovani Dambros Telli
World Electr. Veh. J. 2025, 16(9), 528; https://doi.org/10.3390/wevj16090528 - 18 Sep 2025
Viewed by 392
Abstract
With the growing adoption of electric buses in urban transportation systems, ensuring the safety and structural integrity of their battery systems under accident scenarios has become increasingly important. Among potential accidents, rollover events pose a particular risk, as they can lead to the [...] Read more.
With the growing adoption of electric buses in urban transportation systems, ensuring the safety and structural integrity of their battery systems under accident scenarios has become increasingly important. Among potential accidents, rollover events pose a particular risk, as they can lead to the penetration or deformation of the battery pack and, consequently, trigger thermal runaway. In this context, this study evaluates the structural performance of rechargeable energy storage systems (REESS) in electric buses under rollover conditions, following the guidelines of United Nations Economic Commission for Europe (UNECE) Regulations No. 100 and No. 66. The analysis focuses on the structural safety of uniformly distributing the battery pack beneath the vehicle floor during rollover scenarios. The methodology adopted includes detailed finite element modeling to accurately represent the vehicle structure and battery modules, as well as virtual instrumentation using accelerometers. Simulations were conducted to evaluate structural deformations, battery retention integrity, and acceleration levels within the REESS compartments under rollover impact conditions. The results demonstrated compliance with both regulations and highlighted the importance of properly positioning and securing the battery module to the vehicle floor. The findings contribute to the improvement of design and validation criteria for electric buses, reinforcing the need to align technological innovation with international safety standards. Finally, this research supports the development of safer and more reliable vehicles, promoting sustainable mobility solutions for urban transportation systems. Full article
(This article belongs to the Section Storage Systems)
Show Figures

Figure 1

23 pages, 2031 KB  
Article
Exploring the Appeal of Electric Vehicle Interior Design from the Perspective of Innovation
by Kai-Shuan Shen
World Electr. Veh. J. 2025, 16(9), 527; https://doi.org/10.3390/wevj16090527 - 18 Sep 2025
Viewed by 405
Abstract
Electric vehicles now play a critical and promising role in the automotive industry. This study presents how electric car interiors innovatively appeal to consumers’ needs, influencing their preference for interior design based on essential features. It investigates why consumers prefer the interior design [...] Read more.
Electric vehicles now play a critical and promising role in the automotive industry. This study presents how electric car interiors innovatively appeal to consumers’ needs, influencing their preference for interior design based on essential features. It investigates why consumers prefer the interior design of electric vehicles and what specific characteristics influence these preferences from the perspective of innovation. This study applies a preference-based research method to determine the significance of the innovative appeal of electric cars. The evaluation grid method is applied to interpret experts’ professional insights, which are outlined using a semantic hierarchical diagram of electric vehicle interiors. This study also conducts a questionnaire survey based on consumers’ reactions and analyzes their answers using Quantification Theory Type I. The four key original evaluation items for electric car interiors are determined as “tasteful,” “avant-garde,” “technical innovation,” and “sustainable innovation.” These four factors can be applied using their corresponding reasons and characteristics. This study contributes critical suggestions for interior designers and researchers of electric vehicles. The study also provides useful information on user-centered interaction design, sustainability, and consumer psychology. Full article
(This article belongs to the Section Manufacturing)
Show Figures

Figure 1

18 pages, 456 KB  
Article
Machine Learning-Powered IDS for Gray Hole Attack Detection in VANETs
by Juan Antonio Arízaga-Silva, Alejandro Medina Santiago, Mario Espinosa-Tlaxcaltecatl and Carlos Muñiz-Montero
World Electr. Veh. J. 2025, 16(9), 526; https://doi.org/10.3390/wevj16090526 - 18 Sep 2025
Viewed by 507
Abstract
Vehicular Ad Hoc Networks (VANETs) enable critical communication for Intelligent Transportation Systems (ITS) but are vulnerable to cybersecurity threats, such as Gray Hole attacks, where malicious nodes selectively drop packets, compromising network integrity. Traditional detection methods struggle with the intermittent nature of these [...] Read more.
Vehicular Ad Hoc Networks (VANETs) enable critical communication for Intelligent Transportation Systems (ITS) but are vulnerable to cybersecurity threats, such as Gray Hole attacks, where malicious nodes selectively drop packets, compromising network integrity. Traditional detection methods struggle with the intermittent nature of these attacks, necessitating advanced solutions. This study proposes a machine learning-based Intrusion Detection System (IDS) to detect Gray Hole attacks in VANETs. Methods: This study proposes a machine learning-based Intrusion Detection System (IDS) to detect Gray Hole attacks in VANETs. Features were extracted from network traffic simulations on NS-3 and categorized into time-, packet-, and protocol-based attributes, where NS-3 is defined as a discrete event network simulator widely used in communication protocol research. Multiple classifiers, including Random Forest, Support Vector Machine (SVM), Logistic Regression, and Naive Bayes, were evaluated using precision, recall, and F1-score metrics. The Random Forest classifier outperformed others, achieving an F1-score of 0.9927 with 15 estimators and a depth of 15. In contrast, SVM variants exhibited limitations due to overfitting, with precision and recall below 0.76. Feature analysis highlighted transmission rate and packet/byte counts as the most influential for detection. The Random Forest-based IDS effectively identifies Gray Hole attacks, offering high accuracy and robustness. This approach addresses a critical gap in VANET security, enhancing resilience against sophisticated threats. Future work could explore hybrid models or real-world deployment to further validate the system’s efficacy. Full article
Show Figures

Figure 1

13 pages, 2010 KB  
Article
Tire Contact Pressure Distribution and Dynamic Analysis Under Rolling Conditions
by Xintan Ma, Yugang Wang and Haitao You
World Electr. Veh. J. 2025, 16(9), 525; https://doi.org/10.3390/wevj16090525 - 16 Sep 2025
Viewed by 612
Abstract
Tire contact imprint characteristics and pressure distribution directly affect their lateral mechanical characteristics under rolling conditions, which are the key influencing factors for vehicle handling stability. Based on the nonlinear finite element method, an explicit dynamic model of radial tires is established using [...] Read more.
Tire contact imprint characteristics and pressure distribution directly affect their lateral mechanical characteristics under rolling conditions, which are the key influencing factors for vehicle handling stability. Based on the nonlinear finite element method, an explicit dynamic model of radial tires is established using Abaqus, and its contact process is simulated through phased load transfer and kinematic inversion. The modified mathematical model of contact pressure distribution is introduced from the geometric evolution law of contact imprint and the nonlinear characteristics of contact pressure distribution. The corrected lateral force and aligning torque and contact imprint behavior are analyzed. The results show that in the low roll-angle range, with the increase in the roll angle, the contact imprint shrinks asymmetrically, the pressure center shifts to the outer shoulder of the roll direction, and the lateral force and aligning torque show linear growth characteristics. At the critical value ±8°, the growth rate is significantly slowed down due to the stress saturation effect of the shoulder area. The research analyzes the evolution mechanism of the lateral mechanical characteristics of the contact imprint geometry and pressure distribution drive tires under roll conditions, providing theoretical support for vehicle handling stability optimization and tire structure design. Full article
(This article belongs to the Section Vehicle Management)
Show Figures

Figure 1

20 pages, 2376 KB  
Article
Observer-Based Coordinated Control of Trajectory Tracking and Lateral-Roll Stability for Intelligent Vehicles
by Xinli Qiao, Zhanyang Liang, Te Chen and Mengtao Jin
World Electr. Veh. J. 2025, 16(9), 524; https://doi.org/10.3390/wevj16090524 - 16 Sep 2025
Viewed by 366
Abstract
To achieve precise trajectory tracking and lateral-roll stability during the coordinated control of high-speed autonomous vehicles under lane-changing conditions, this paper proposes an integrated control strategy based on state estimation with a high-order sliding mode and a double-power sliding mode. Firstly, establish a [...] Read more.
To achieve precise trajectory tracking and lateral-roll stability during the coordinated control of high-speed autonomous vehicles under lane-changing conditions, this paper proposes an integrated control strategy based on state estimation with a high-order sliding mode and a double-power sliding mode. Firstly, establish a three-degrees-of-freedom vehicle dynamics model and trajectory-tracking error model that includes yaw lateral-roll coupling, and use an extended Kalman filter to estimate real-time unmeasurable states such as the center of mass roll angle, roll angle, and angular velocity. Then, for the trajectory-tracking subsystem, a high-order sliding-mode controller is designed. By introducing a virtual control variable and an arbitrary-order robust differentiator, the switching signal is implicitly integrated into the derivative of the control variable, significantly reducing chattering and ensuring finite-time convergence. Furthermore, in the lateral stability loop, a double-power convergence law sliding-mode controller is constructed to dynamically allocate yaw moment and roll moment with estimated state as feedback, achieving the decoupling optimization of stability and tracking performance. The joint simulation results show that the proposed strategy significantly outperforms traditional sliding-mode schemes in terms of lateral deviation, heading deviation, and key state oscillations under typical high-speed lane-changing conditions. This can provide theoretical basis and engineering reference for integrated control of autonomous vehicles under high dynamic limit conditions. Full article
Show Figures

Figure 1

17 pages, 1337 KB  
Article
Research on Accident Type Prediction for New Energy Vehicles Based on the AS-Naive Bayes Algorithm
by Shubing Huang, Bingshan Hou, Xiaoxuan Yin, Chenchen Kong and Chongming Wang
World Electr. Veh. J. 2025, 16(9), 523; https://doi.org/10.3390/wevj16090523 - 16 Sep 2025
Viewed by 443
Abstract
Developing new energy vehicles (NEVs) is a key strategy for achieving low-carbon and sustainable transportation. However, as the number of NEVs increases, traffic accidents involving these vehicles have risen sharply. To explore the characteristics of NEV accident types, and assess the occurrence of [...] Read more.
Developing new energy vehicles (NEVs) is a key strategy for achieving low-carbon and sustainable transportation. However, as the number of NEVs increases, traffic accidents involving these vehicles have risen sharply. To explore the characteristics of NEV accident types, and assess the occurrence of different accident types, this study proposes an accident type analysis and prediction method based on a novel Naive Bayes algorithm integrating the additive smoothing and synthetic minority over-sampling technique (AS-Naive Bayes). First, typical accident data (such as scraping, collisions, run-overs, rollovers, and battery fires/explosions) are extracted from the traffic management platform. A statistical analysis is then conducted to assess the relationships between accident types and factors including road conditions, time, vehicle status, and driver behavior. Moreover, to reduce the influence of irrelevant factors, Chi-square testing and Mutual Information are used to select features strongly associated with accident types. After that, to address the challenges of limited sample size and imbalanced distribution of accident types, this study proposes an accident type prediction method based on the AS–Naive Bayes algorithm, which integrates the Synthetic Minority Over-sampling Technique (SMOTE) and additive smoothing. Finally, five-fold cross-validation results show that the proposed method achieves a prediction accuracy of 84.8%, outperforming Support Vector Machine (SVM, 74.1%) and Long Short-Term Memory (LSTM, 79.8%), and standard Naive Bayes models, demonstrating its effectiveness in accurately identifying NEV accident types. Full article
(This article belongs to the Section Vehicle and Transportation Systems)
Show Figures

Figure 1

14 pages, 1839 KB  
Article
An Empirical Study on the Impact of Key Technology Configurations on Sales of Battery Electric Vehicles: Evidence from the Chinese Market
by Shufang Huang, Yunpeng Li and Zhen Xi
World Electr. Veh. J. 2025, 16(9), 522; https://doi.org/10.3390/wevj16090522 - 16 Sep 2025
Viewed by 497
Abstract
In the global automotive industry’s transition towards electrification and intelligence, the influence of key technology configurations of battery electric vehicles (BEVs) on consumer purchasing decisions and market sales has become increasingly prominent. This paper empirically investigates the impact of BEVs’ key technology features—specifically, [...] Read more.
In the global automotive industry’s transition towards electrification and intelligence, the influence of key technology configurations of battery electric vehicles (BEVs) on consumer purchasing decisions and market sales has become increasingly prominent. This paper empirically investigates the impact of BEVs’ key technology features—specifically, driving range, Advanced Driver-Assistance Systems (ADASs), and intelligent cockpits—on sales, with a particular focus on the interaction effect between ADAS score and price. Employing panel data from the Chinese market spanning January 2023 to March 2025, this study analyzes 783 observations across 29 models and 13 brands using a multilevel mixed-effects model (MEM). The results indicate that driving range and intelligent cockpit score (ICS) are significantly and positively associated with sales growth, whereas price has a significant negative effect. More importantly, a significant interaction effect exists between the ADAS score and price, which implies that the impact of ADASs on sales varies across different price levels. Specifically, in lower-priced models, a high ADAS score corresponds to a decrease in sales, while its effect trends toward positive in higher-priced models. Furthermore, a high ADAS score significantly reduces consumers’ price sensitivity.Compared with prior macro-level studies, our contribution is jointly quantifying (i) the main effects of range and ICS and (ii) a price-contingent ADAS effect within a model-within-brand MEM, revealing that higher ADAS scores attenuate price sensitivity in premium segments. These findings offer actionable guidance for configuration bundling and pricing across market segments. Full article
(This article belongs to the Section Marketing, Promotion and Socio Economics)
Show Figures

Figure 1

3 pages, 141 KB  
Editorial
Establishment of Ten New Sections in WEVJ
by Joeri Van Mierlo, Michael Fowler, Vladimir Katic, Aymeric Rousseau and Peter Van den Bossche
World Electr. Veh. J. 2025, 16(9), 521; https://doi.org/10.3390/wevj16090521 - 16 Sep 2025
Viewed by 259
Abstract
As the field of electromobility continues its unprecedented evolution, the World Electric Vehicle Journal is proud to announce the establishment of 10 new journal sections [...] Full article
16 pages, 5064 KB  
Article
The Impact of Weight Distribution in Heavy Battery Electric Vehicles on Pavement Performance: A Preliminary Study
by Konstantinos Gkyrtis
World Electr. Veh. J. 2025, 16(9), 520; https://doi.org/10.3390/wevj16090520 - 15 Sep 2025
Viewed by 838
Abstract
The transition to heavy-duty electric vehicles (HDEVs) offers substantial environmental benefits but raises concerns about increased pavement deterioration due to the added mass of large battery packs. A key research question is whether additional structural demands on road infrastructure could offset these benefits. [...] Read more.
The transition to heavy-duty electric vehicles (HDEVs) offers substantial environmental benefits but raises concerns about increased pavement deterioration due to the added mass of large battery packs. A key research question is whether additional structural demands on road infrastructure could offset these benefits. This study investigates the impact of battery weight distribution on asphalt pavement performance by comparing conventional diesel trucks with electric trucks under equivalent gross vehicle weight (36 tons). Three battery placement scenarios were evaluated: (i) concentration at the steering axle, (ii) concentration at the rear tractor axles, and (iii) uniform distribution across all tractor axles. Pavement elastic response was analyzed using a representative cross-section using mechanistic–empirical modeling, with fatigue damage estimated according to the Mechanistic–Empirical Pavement Design Guide (MEPDG) fatigue law. Results indicate that tensile strains at the bottom of asphalt layers may increase by up to 60%, with relative fatigue damage rising by 185% and 34% for scenarios (i) and (iii), respectively, while scenario (ii) produced nearly equivalent damage to conventional trucks. These findings highlight the critical role of battery placement; the optimal performance seems to be achieved when weight is concentrated at the rear tractor axles. Full article
Show Figures

Figure 1

47 pages, 1890 KB  
Article
An Empirical Analysis of the Effectiveness of Local Industrial Policies for China’s New Energy Vehicle Sector
by Chunning Wang, Yingchong Xie, Yifen Yin, Jingwen Cai and Haoqian Hu
World Electr. Veh. J. 2025, 16(9), 519; https://doi.org/10.3390/wevj16090519 - 12 Sep 2025
Viewed by 424
Abstract
Despite China’s success in its new energy vehicle (NEV) transition, significant regional imbalances persist, raising the question of why provincial policy effectiveness is so context-dependent. To investigate this, this study develops a novel framework to measure policy “quality” and “style”, systematically quantifying 2455 [...] Read more.
Despite China’s success in its new energy vehicle (NEV) transition, significant regional imbalances persist, raising the question of why provincial policy effectiveness is so context-dependent. To investigate this, this study develops a novel framework to measure policy “quality” and “style”, systematically quantifying 2455 provincial policy documents from 2013 to 2023. Our empirical analysis reveals that policy quality—encompassing its authoritativeness, instrument strength, and resource commitment—is a far more decisive determinant of effectiveness than sheer policy quantity. We identify three primary policy styles with distinct impacts: substantive-driving policies are crucial for stimulating market demand, whereas coordinative-programmatic policies are more effective in guiding industrial supply, revealing a significant goal-mismatch. Conversely, high-level authoritative policies can unexpectedly inhibit infrastructure development. Crucially, the study finds that provincial policies act more as “catalysts” than “creators”, their effectiveness being highly contingent on local economic, fiscal, and industrial fundamentals. The findings of this research offer direct implications for policymaking: decision-makers should shift their focus from pursuing policy quantity to enhancing policy quality and design targeted, “precision-irrigation” policy instrument portfolios tailored to the specific contexts and development objectives (e.g., promoting sales or guiding production) of different regions. Full article
Show Figures

Figure 1

26 pages, 10737 KB  
Article
Architecture and Pricing Strategies for Commercial EV Battery Swapping—Dual-Market Cournot Model and Degradation-Sensitive Regulated Framework
by Soham Ghosh
World Electr. Veh. J. 2025, 16(9), 518; https://doi.org/10.3390/wevj16090518 - 12 Sep 2025
Viewed by 384
Abstract
The global electric vehicle (EV) market has experienced sustained growth over the last decade; however, adoption within the commercial EV segment remains comparatively sluggish. This disparity is driven by three primary factors: the intrinsic limitations of lithium-ion battery chemistry, which imposes constraints on [...] Read more.
The global electric vehicle (EV) market has experienced sustained growth over the last decade; however, adoption within the commercial EV segment remains comparatively sluggish. This disparity is driven by three primary factors: the intrinsic limitations of lithium-ion battery chemistry, which imposes constraints on charge–discharge cycling, excessive charging durations for large battery packs used in long-haul semi-trucks, and diminished charging effectiveness under cold weather conditions, which further extends downtime and increases grid demand. To address these operational and infrastructural challenges, this article proposes a novel battery swapping station layout with ‘design-integrated safety’ features, enabling rapid battery replacement while ensuring compliance with safety codes and standards. Two complementary pricing strategies are developed for deployment under differing market structures. The first is a Cournot competition, applicable to deregulated environments, where firms strategically allocate battery inventory between EV swapping services and participation in a secondary energy market. As an extension of the Cournot competition model, the profit functions are analytically derived for a duopoly in which one firm engages in dual markets, enabling assessment of equilibrium outcomes under competitive conditions. The second strategy is a degradation-sensitive pricing framework, intended for regulated markets, which dynamically adjusts swap prices based on state-of-charge depletion, duty cycle intensity, environmental exposure, and nonlinear battery degradation effects. This formulation is evaluated for six representative operational cases, demonstrating its ability to incentivize shallow cycling, penalize deep discharges, and incorporate fair usage-based pricing. The proposed architectures and pricing models offer a viable pathway to accelerate commercial EV adoption while optimizing asset utilization and profitability for station operators. Full article
Show Figures

Graphical abstract

19 pages, 589 KB  
Article
The Impact of the Expected Utility and Experienced Utility Gap on Electric Vehicle Repurchase Intention in Jiangsu, China
by Xiao Zheng, Jiaxin Huang, Mengzhe Wang and Wenbo Li
World Electr. Veh. J. 2025, 16(9), 517; https://doi.org/10.3390/wevj16090517 - 12 Sep 2025
Viewed by 485
Abstract
The global automotive industry’ s rapid transformation has led to electric vehicles (EVs) capturing a significant market share as a sustainable transportation option. To sustain this growth, it is crucial to not only attract new users but also retain existing ones through repurchases. [...] Read more.
The global automotive industry’ s rapid transformation has led to electric vehicles (EVs) capturing a significant market share as a sustainable transportation option. To sustain this growth, it is crucial to not only attract new users but also retain existing ones through repurchases. This decision is shaped by both vehicle attributes and users’ prior experiences. This study examines the impact of five dimensions of expected utility and experienced utility gap (including cost utility, functional utility, emotional utility, environmental utility, and social utility) on the repurchase intentions of 863 Chinese EV users. Discrete choice experiments were used to analyze these factors, considering both vehicle and personal attributes. The results show that when emotional utility exceeds expectations, users are more likely to repurchase pure electric and plug-in hybrid electric vehicles. However, if environmental and social utilities fall short of expectations, users may be discouraged from choosing these two vehicle types. In contrast, decisions regarding gasoline vehicles are primarily driven by economic and habitual factors, with minimal influence from emotional, environmental, or social utilities. Additionally, EV users show a preference for medium-sized models that offer shorter charging times and longer driving ranges. These findings offer insights for enhancing consumer acceptance, accelerating EV market penetration, and supporting the automotive industry’s sustainable development, thereby contributing to the achievement of environmental sustainability goals. Full article
Show Figures

Figure 1

24 pages, 921 KB  
Article
Assessing Consumers’ Willingness to Pay for Secondary Utilization of Retired Battery Products: The Role of Incentive Policy, Knowledge, and Perceived Risks
by Ziyi Zhao, Pengyu Dai, Chaoqun Zheng and Huaming Song
World Electr. Veh. J. 2025, 16(9), 516; https://doi.org/10.3390/wevj16090516 - 12 Sep 2025
Viewed by 431
Abstract
The rapid development of the new energy vehicle industry has resulted in a large number of retired power batteries. Creating products from second-use retired batteries (SURB) is crucial for sustainability by extending the batteries’ operational life, which, in turn, conserves resources and protects [...] Read more.
The rapid development of the new energy vehicle industry has resulted in a large number of retired power batteries. Creating products from second-use retired batteries (SURB) is crucial for sustainability by extending the batteries’ operational life, which, in turn, conserves resources and protects the environment. Consequently, this paper establishes a structural equation model (SEM) based on an interpretive structural model (ISM). It investigates consumers’ willingness to pay (WTP) for secondary utilization of retired batteries (SURB) products by extending the theory of planned behavior (TPB)with incentive policy, knowledge, and perceived risk. The study reveals that incentive policies and knowledge are fundamental factors, while subjective norms, perceived risk, and perceived behavioral control exert moderate influence. Attitude emerges as the most significant predictor, directly affecting consumers’ WTP, with perceived behavioral control also playing a key role. Incentive policies and knowledge have an indirect influence through perceived behavioral control and perceived risk. Finally, this paper discusses the theoretical and practical significance of the findings and provides relevant policy recommendations. Full article
Show Figures

Graphical abstract

25 pages, 3429 KB  
Article
Active and Reactive Power Scheduling of Distribution System Based on Two-Stage Stochastic Optimization
by Yangchao Xu, Jia Ren, Qiang He, Dongyang Dong and Haoxiang Zou
World Electr. Veh. J. 2025, 16(9), 515; https://doi.org/10.3390/wevj16090515 - 11 Sep 2025
Viewed by 333
Abstract
With the large-scale integration of distributed resources into the distribution network, such as wind/solar power and electric vehicles (EVs), the uncertainties have rapidly increased in the operation optimization of the distribution network. In this context, it is of great practical interest to ensure [...] Read more.
With the large-scale integration of distributed resources into the distribution network, such as wind/solar power and electric vehicles (EVs), the uncertainties have rapidly increased in the operation optimization of the distribution network. In this context, it is of great practical interest to ensure the security and economic operation of the distribution network. This paper addresses this issue and makes the following contributions. Firstly, a two-stage stochastic rolling optimization framework for active–reactive power scheduling is established. In the first stage, it dispatches the active power of distributed resources. In the second stage, it optimizes the reactive power compensation based on the first-stage scheduling plan. Secondly, the simulation-based Rollout method is proposed to obtain the improved active power dispatching policy for cost optimization in the first stage. Meanwhile, the aggregated power of EVs can be determined based on the mobility and charging demand of EVs. Thirdly, based on the aggregated power of EVs, a scenario-based second-order cone programming is applied to perform the rolling optimization of reactive power compensation for voltage performance improvement in the second stage. The numerical results demonstrate that this method can effectively improve the economic operation of the distribution network while enhancing its operational security by leveraging the charging elasticity of EVs. Full article
Show Figures

Figure 1

Previous Issue
Next Issue
Back to TopTop