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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

27 pages, 1356 KB

Open AccessReview

Equity Considerations in Public Electric Vehicle Charging: A Review

by Boyou Chen, Austin Moore, Bochen Jia, Kaihan Zhang and Mengqiu Cao

World Electr. Veh. J. 2025, 16(10), 553; https://doi.org/10.3390/wevj16100553 - 25 Sep 2025

Public electric vehicle (EV) charging infrastructure is crucial for accelerating EV adoption and reducing transportation emissions; however, disparities in infrastructure access have raised significant equity concerns. This review synthesizes existing knowledge and identifies gaps regarding equity in EV public charging research. Following structured [...] Read more.

Public electric vehicle (EV) charging infrastructure is crucial for accelerating EV adoption and reducing transportation emissions; however, disparities in infrastructure access have raised significant equity concerns. This review synthesizes existing knowledge and identifies gaps regarding equity in EV public charging research. Following structured review protocols, 91 peer-reviewed studies from Scopus and Google Scholar were analyzed, focusing explicitly on equity considerations. The findings indicate that current research on EV public charging equity mainly adopts geographic information systems (GIS), network optimization, behavioral modeling, and hybrid analytical frameworks, yet lacks consistent normative frameworks for assessing equity outcomes. Equity assessments highlight four key dimensions: spatial accessibility, cost burdens, reliability and usability, and user awareness and trust. Socio-economic disparities, particularly income, housing tenure, and ethnicity, frequently exacerbate inequitable access, disproportionately disadvantaging low-income, renter, and minority populations. Additionally, infrastructure-specific choices, including charger reliability, strategic location, and pricing strategies, significantly influence adoption patterns and equity outcomes. However, the existing literature primarily reflects the contexts of North America, Europe, and China, revealing substantial geographical and methodological limitations. This review suggests the need for more robust normative evaluations of equity, comprehensive demographic data integration, and advanced methodological frameworks, thereby guiding targeted, inclusive, and context-sensitive infrastructure planning and policy interventions. Full article

(This article belongs to the Section Charging Infrastructure and Grid Integration)

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15 pages, 1898 KB

Open AccessArticle

Design and Cost Evaluation of Additively Manufactured Electric Vehicle Gearbox Housings

by Steffen Jäger and Tilmann Linde

World Electr. Veh. J. 2025, 16(10), 552; https://doi.org/10.3390/wevj16100552 - 25 Sep 2025

Additive manufacturing technologies enable the design of complex lightweight structures for electric powertrain applications. This study evaluates the topology optimization and conceptual additive manufacturing of a real electric vehicle gearbox housing, aiming to reduce weight while maintaining structural stiffness. Based on an existing [...] Read more.

Additive manufacturing technologies enable the design of complex lightweight structures for electric powertrain applications. This study evaluates the topology optimization and conceptual additive manufacturing of a real electric vehicle gearbox housing, aiming to reduce weight while maintaining structural stiffness. Based on an existing industrial component, a topology-optimized design featuring an X-shaped rib structure was developed. The manufacturing concept combines Laser Metal Deposition (LMD) with a pre-machined turned part. A comparative material study was carried out using finite element simulations to assess aluminum, magnesium, titanium, and stainless steel in terms of weight, deformation, and natural frequency. The results indicate that aluminum alloys offer the best balance of stiffness and weight due to their high specific modulus and favorable processability. The optimized design achieved a simulated weight reduction of approximately 21% with only a minor increase in rotational deformation. A cost analysis of different manufacturing methods suggests that, while conventional casting remains more economical at higher volumes, additive processes are becoming increasingly viable for small series. The study provides a theoretical foundation for future development of lightweight functionally integrated gearbox housings in electric mobility. Full article

(This article belongs to the Special Issue EVS38—International Electric Vehicle Symposium and Exhibition (Gothenburg, Sweden))

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29 pages, 3071 KB

Open AccessArticle

Enhancing Multi-Objective Performance: Optimizing the Efficiency of an Electric Racing Vehicle

by Ingry N. Gomez-Miranda, Arley. F. Villa-Salazar, Andrés Pérez-González, Andres. F. Romero-Maya, Juan. D. Velásquez-Gómez, Elkin. M. Gonzalez and Sergio Estrada

World Electr. Veh. J. 2025, 16(10), 551; https://doi.org/10.3390/wevj16100551 - 25 Sep 2025

The multi-objective optimization of an electric prototype racing vehicle is addressed in this study. The goal was to identify the optimal combination of battery type, pilot weight, and power mode to maximize operational time and distance while minimizing energy consumption. A structured [...] Read more.

The multi-objective optimization of an electric prototype racing vehicle is addressed in this study. The goal was to identify the optimal combination of battery type, pilot weight, and power mode to maximize operational time and distance while minimizing energy consumption. A structured

2 \times 3 \times 3

factorial design was implemented, and the resulting data were analyzed through Response Surface Methodology (RSM) in combination with the Desirability Function Approach (DFA). The experimental design included two battery configurations, three weight levels, and three power settings, while data acquisition was performed through a custom Arduino-based system validated against commercial instruments. The results revealed that the configuration with the smallest battery, the lowest weight (66 kg), and the lowest power mode (N5) achieved the most efficient performance, yielding an operating time of 1.12 h, a travel distance of 24.63 km, and an energy performance index of 2.90 km/Ah. The integration of RSM with DFA provided a robust framework for identifying optimal multiparameter conditions under competition constraints. Unlike previous studies that examined these variables in isolation, this work advances the state of the art by demonstrating the feasibility of multiparameter optimization in real-world racing contexts, offering methodological and practical insights for sustainable electric mobility. Full article

(This article belongs to the Special Issue Advanced Electrical Machine and Power Electronics for the Charging and Drive System of Electric Vehicles (EVs))

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18 pages, 268 KB

Open AccessArticle

Research on the Impact of Data Elements on the Innovation Capability of New Energy Vehicle Enterprises—Evidence from Chinese Listed Companies

by Hongying Wang and Lingyi Ai

World Electr. Veh. J. 2025, 16(10), 550; https://doi.org/10.3390/wevj16100550 - 24 Sep 2025

Based on panel data from 173 Chinese listed companies in the new energy vehicle industry from 2016 to 2023, this study constructs a two-way fixed effects model to examine the impact of data elements on corporate innovation capability. Systematically address issues and validate [...] Read more.

Based on panel data from 173 Chinese listed companies in the new energy vehicle industry from 2016 to 2023, this study constructs a two-way fixed effects model to examine the impact of data elements on corporate innovation capability. Systematically address issues and validate results through multiple measurement approaches, including robustness checks, instrumental variables methods, moderation effect analysis, and heterogeneity tests. The results indicate: (1) Data elements significantly enhance the innovation capability of new energy vehicle enterprises, and a conclusion that remains robust after a series of endogeneity and robustness checks. (2) Moderating effect tests reveal that human resources strengthen the relationship between data elements and corporate innovation capability. (3) Heterogeneity analysis shows that, in terms of capital sources, data elements have a more substantial promoting effect on the innovation capability of domestic enterprises compared to foreign-funded ones; regionally, the innovation-driven effect of data elements is more pronounced in eastern and central China than in the western region. This study not only offers practical guidance for new energy vehicle enterprises to allocate data elements and human resources effectively, but also provides an empirical basis for policymakers to formulate market-oriented data policies, thereby offering a new perspective for enhancing the innovation capabilities of new energy vehicle enterprises. Full article

(This article belongs to the Section Marketing, Promotion and Socio Economics)

3 pages, 153 KB

Open AccessCorrection

Correction: Kousar et al. A Deep Learning Approach for Real-Time Intrusion Mitigation in Automotive Controller Area Networks. World Electr. Veh. J. 2025, 16, 492

by Anila Kousar, Saeed Ahmed and Zafar A. Khan

World Electr. Veh. J. 2025, 16(10), 549; https://doi.org/10.3390/wevj16100549 - 24 Sep 2025

Error in Table [...] Full article

17 pages, 1775 KB

Open AccessArticle

Direct Torque Control of Switched Reluctance Motor Based on Improved Sliding Mode Reaching Law Strategy

by Qiang Ma, Liang Qiao, Zhichong Wang and Yun Hu

World Electr. Veh. J. 2025, 16(10), 548; https://doi.org/10.3390/wevj16100548 - 24 Sep 2025

The conventional sliding mode control (SMC) strategy for direct torque control of switched reluctance motors suffers from severe chattering and prolonged dynamic response. Accordingly, an enhanced SMC strategy is proposed to mitigate motor chattering and suppress torque ripple. On the basis of the [...] Read more.

The conventional sliding mode control (SMC) strategy for direct torque control of switched reluctance motors suffers from severe chattering and prolonged dynamic response. Accordingly, an enhanced SMC strategy is proposed to mitigate motor chattering and suppress torque ripple. On the basis of the conventional exponential approximation rate, a compensation factor and a fractional order are incorporated. Meanwhile, the sigmoid function, characterized by superior smoothness, is employed to replace the sign function that induces severe chattering, thereby attenuating the motor torque ripple. At the same time, in response to the challenge of parameter tuning arising from motor nonlinearity and the abundance of parameters, the sparrow search algorithm (SSA) is employed to optimize the controller parameters. The motor control models before and after the improvement are constructed in MATLAB/Simulink, and the sparrow search algorithm (SSA) is employed to optimize the controller parameters for both cases. Comparative results indicate that the improved control strategy and parameter optimization method can effectively suppress motor torque ripple and enhance the dynamic response characteristics of the system under various operating conditions and rotational speeds. Full article

(This article belongs to the Section Propulsion Systems and Components)

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17 pages, 554 KB

Open AccessArticle

The Potential of Light Electric Vehicles to Substitute Car Trips in Commercial Transport in Germany

by Robert Seiffert, Mascha Brost and Laura Gebhardt

World Electr. Veh. J. 2025, 16(10), 547; https://doi.org/10.3390/wevj16100547 - 23 Sep 2025

Achieving climate protection goals in the transport sector requires the adoption of innovative mobility solutions and new vehicle concepts. In Germany, commercial transport accounts for one-quarter of the total car mileage. Many of these trips are comparatively short, often involve a single passenger, [...] Read more.

Achieving climate protection goals in the transport sector requires the adoption of innovative mobility solutions and new vehicle concepts. In Germany, commercial transport accounts for one-quarter of the total car mileage. Many of these trips are comparatively short, often involve a single passenger, and require the transport of only small or lightweight goods—yet they are typically carried out by car. Substituting cars with small and light electric vehicles (LEVs) wherever feasible could make commercial transport more efficient and environmentally friendly. LEVs combine a favorable weight-to-payload ratio with the high efficiency of electric drivetrains. This study estimates the share of car trips in commercial transport in Germany that could theoretically be substituted by LEVs. The analysis is based on a comparison of trip characteristics from a national travel survey with the technical capabilities of selected LEV categories. Our results indicate that up to 73% of commercial car trips and 44% of mileage could theoretically be covered by LEVs, with particularly high potential for trips in commercial passenger transport. Although limitations in range and payload restrict the universal applicability of LEVs, the findings reveal substantial opportunities to make commercial transport cleaner and more sustainable. These insights highlight the relevance of LEVs for sustainable commercial transport and offer a data-driven basis for further discussion of their potential and for guiding targeted policy and planning. Full article

(This article belongs to the Section Vehicle and Transportation Systems)

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18 pages, 5108 KB

Open AccessArticle

Dual-Mode PID Control for Automotive Resolver Angle Compensation Based on a Fuzzy Self-Tuning Divide-and-Conquer Framework

by Xin Zeng, Yongyuan Wang, Julian Zhu, Yubo Chu, Hao Li and Hao Peng

World Electr. Veh. J. 2025, 16(10), 546; https://doi.org/10.3390/wevj16100546 - 23 Sep 2025

Electric vehicle (EV) drivetrains often suffer from degraded control precision due to resolver zero-position deviation. This issue becomes particularly critical under diverse automotive-grade operating conditions, posing challenges for achieving reliable and efficient drivetrain performance. To tackle this problem, we propose a dual-mode PID [...] Read more.

Electric vehicle (EV) drivetrains often suffer from degraded control precision due to resolver zero-position deviation. This issue becomes particularly critical under diverse automotive-grade operating conditions, posing challenges for achieving reliable and efficient drivetrain performance. To tackle this problem, we propose a dual-mode PID dynamic compensation control methodology. This approach establishes a divide-and-conquer framework that differentiates between weak-magnetic and non-weak-magnetic regions. It integrates current loop feedback with a fuzzy self-tuning mechanism, enabling real-time dynamic compensation of the resolver’s initial angle. To ensure system stability under extreme automotive conditions (−40 °C to 125 °C, ±0.5 g vibration, and electromagnetic interference), a triple-redundancy architecture is implemented. This architecture combines hardware filtering, software verification, and fault diagnosis. Our contribution lies in presenting a reliable solution for intelligent EV drivetrain calibration. The proposed method effectively mitigates resolver zero-position deviation, not only enhancing drivetrain performance under challenging automotive environments but also ensuring compliance with ISO 26262 ASIL-C safety standards. This research has been validated through its implementation in a 3.5-ton commercial logistics vehicle by a leading automotive manufacturer, demonstrating its practical viability and potential for widespread adoption in the EV industry. Full article

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16 pages, 1035 KB

Open AccessReview

Electric Vehicles and Urban Tourism in Smart Cities: A Bibliometric Review of Sustainable Mobility Trends and Infrastructure Development

by Ye-Zhi Liu, Huan Minh Nguyen and Minh Tri Nguyen

World Electr. Veh. J. 2025, 16(10), 545; https://doi.org/10.3390/wevj16100545 - 23 Sep 2025

This study presents a bibliometric review of global research trends on electric vehicles (EVs) and urban tourism within the context of smart cities, emphasizing the economic and policy dimensions of sustainable mobility and infrastructure investment. Drawing from 593 publications indexed in the Web [...] Read more.

This study presents a bibliometric review of global research trends on electric vehicles (EVs) and urban tourism within the context of smart cities, emphasizing the economic and policy dimensions of sustainable mobility and infrastructure investment. Drawing from 593 publications indexed in the Web of Science from 2005 to April 2025, the analysis explores document types, leading research areas, alignment with Sustainable Development Goals (SDGs), influential authors, and highly cited works. A keyword co-occurrence analysis reveals six major thematic clusters, highlighting key topics such as EV adoption behavior, renewable energy policy, wireless charging technology, and semiconductor innovation. Engineering and physics emerged as dominant research areas, with SDG 7 (Affordable and Clean Energy) and SDG 11 (Sustainable Cities and Communities) most frequently represented. The findings underline a growing interdisciplinary effort to integrate EV technologies with urban tourism through smart, carbon-neutral transport systems, supported by policy frameworks, green investment incentives, and digital infrastructure. This review identifies research gaps and opportunities to advance energy-efficient, economically viable, and tourism-oriented mobility solutions in smart cities by mapping the current knowledge landscape. Full article

(This article belongs to the Special Issue Autonomous Electric Vehicles Combined with Non-connected Vehicles in Smart Cities)

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29 pages, 1150 KB

Open AccessArticle

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

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)

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28 pages, 1307 KB

Open AccessArticle

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

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)

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26 pages, 8533 KB

Open AccessReview

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

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)

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20 pages, 1689 KB

Open AccessArticle

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

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)

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28 pages, 1632 KB

Open AccessReview

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

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

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18 pages, 1367 KB

Open AccessArticle

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

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

(This article belongs to the Special Issue Design, Analysis and Optimization of Electrical Machines and Drives for Electric Vehicles, 2nd Edition)

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19 pages, 6310 KB

Open AccessArticle

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

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)

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26 pages, 1938 KB

Open AccessArticle

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

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

(This article belongs to the Special Issue Electric Vehicles and Charging Facilities for a Sustainable Transport Sector)

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25 pages, 4454 KB

Open AccessArticle

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

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)

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19 pages, 3729 KB

Open AccessArticle

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

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)

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17 pages, 2205 KB

Open AccessArticle

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

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)

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