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Developing a Unified Framework for PMSM Speed Regulation: Active Disturbance Rejection Control via Generalized PI Control
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Recommendation of Electric Vehicle Charging Stations in Driving Situations Based on a Preference Objective Function
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From Map to Policy: Road Transportation Emission Mapping and Optimizing BEV Incentives for True Emission Reductions
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Affordable Road Obstacle Detection and Active Suspension Control Using Inertial and Motion Sensors
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 (Transportation Science and Technology) / CiteScore - Q2 (Automotive Engineering)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 16.2 days after submission; acceptance to publication is undertaken in 2.8 days (median values for papers published in this journal in the second half of 2024).
- 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 (2023)
Latest Articles
Enhancing Grid Stability Through Physics-Informed Machine Learning Integrated-Model Predictive Control for Electric Vehicle Disturbance Management
World Electr. Veh. J. 2025, 16(6), 292; https://doi.org/10.3390/wevj16060292 - 25 May 2025
Abstract
Integrating electric vehicles (EVs) has become integral to modern power grids to enhance grid stability and support green energy transportation solutions. EVs emerged as a promising energy solution that introduces a significant challenge to the unpredictable and dynamic nature of EV charging and
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Integrating electric vehicles (EVs) has become integral to modern power grids to enhance grid stability and support green energy transportation solutions. EVs emerged as a promising energy solution that introduces a significant challenge to the unpredictable and dynamic nature of EV charging and discharging behaviors. These EV behaviors are performed by grid-to-vehicle (G2V) and vehicle-to-grid (V2G) operations that create unpredictable disturbances in the power grid. These disturbances introduced a nonlinear dynamic that compromises grid stability and power quality. Due to the unpredictable nature of these disturbances, the conventional control design with dynamic model prediction cannot manage these disturbances. To address these challenges, a Physics-Informed Machine Learning (PIML)-enhanced Model Predictive Control (MPC) framework is proposed to learn the stochastic behaviors of the EV-introduced disturbance in the power grid. The learned PIML model is integrated into an MPC framework to enable an accurate prediction of EV-driven disturbances with minimal data requirements. The MPC formulation optimizes pre-emptive control actions to mitigate the disturbance and ensure robust grid stability and enhanced EV integration. A comprehensive convergence and stability analysis of the proposed MPC formulation uses Lyapunov-based proofs. The efficacy of the proposed control design is evaluated on IEEE benchmark systems, demonstrating a significant improvement in performance metrics, such as frequency deviation, voltage stability, and scalability, compared to the conventional MPC design. The proposed MPC framework offers scalable and robust real-time EV grid integration in modern power grids.
Full article
(This article belongs to the Special Issue Modeling, Prediction and Management of Charging and Discharging Loads for Electric Vehicle–Grid Interaction Under the “Double Carbon” Strategy)
Open AccessArticle
Optimizing State of Charge Estimation in Lithium–Ion Batteries via Wavelet Denoising and Regression-Based Machine Learning Approaches
by
Mohammed Isam Al-Hiyali, Ramani Kannan and Hussein Shutari
World Electr. Veh. J. 2025, 16(6), 291; https://doi.org/10.3390/wevj16060291 - 24 May 2025
Abstract
Accurate state of charge (SOC) estimation is key for the efficient management of lithium–ion (Li-ion) batteries, yet is often compromised by noise levels in measurement data. This study introduces a new approach that uses wavelet denoising with a machine learning regression model to
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Accurate state of charge (SOC) estimation is key for the efficient management of lithium–ion (Li-ion) batteries, yet is often compromised by noise levels in measurement data. This study introduces a new approach that uses wavelet denoising with a machine learning regression model to enhance SOC prediction accuracy. The application of wavelet transform in data pre-processing is investigated to assess the impact of denoising on SOC estimation accuracy. The efficacy of the proposed technique has been evaluated using various polynomial and ensemble regression models. For empirical validation, this study employs four Li-ion battery datasets from NASA’s prognostics center, implementing a holdout method wherein one cell is reserved for testing to ensure robustness. The results, optimized through wavelet-denoised data using polynomial regression models, demonstrate improved SOC estimation with RMSE values of 0.09, 0.25, 0.28, and 0.19 for the respective battery datasets. In particular, significant improvements (p-value < 0.05) with variations of 0.18, 0.20, 0.16, and 0.14 were observed between the original and wavelet-denoised SOC estimates. This study proves the effectiveness of wavelet-denoised input in minimizing prediction errors and establishes a new standard for reliable SOC estimation methods.
Full article
(This article belongs to the Special Issue Smart Battery Systems: Advanced Modeling, State Estimation, Prognostics and Control)
Open AccessArticle
BL-DATransformer Lifespan Degradation Prediction Model of Fuel Cell Using Relative Voltage Loss Rate Health Indicator
by
Yinjie Xu, Jing Wang, Donghai Hu, Dagang Lu, Xiaoyan Zhang, Wenxuan Wei, Hua Ding and Shupei Zhang
World Electr. Veh. J. 2025, 16(6), 290; https://doi.org/10.3390/wevj16060290 - 22 May 2025
Abstract
The durability of fuel cells is the main obstacle to their large-scale application. Deep learning-based methods improve the accuracy of fuel cell lifespan degradation prediction. However, their reliance on static health indicators and application in bench experiment environments limits their ability to capture
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The durability of fuel cells is the main obstacle to their large-scale application. Deep learning-based methods improve the accuracy of fuel cell lifespan degradation prediction. However, their reliance on static health indicators and application in bench experiment environments limits their ability to capture degradation trends under dynamic conditions. This paper proposes a novel lifespan degradation prediction method for fuel cells operating in real-world traffic environments, utilizing Relative Voltage Loss Rate (RVLR) as the health indicator. Initially, fuel cell lifespan degradation data with varying characteristics are obtained through a dynamic bench experiment and two sets of road driving experiments. Subsequently, a lifespan degradation prediction model based on the Bidirectional Long Short-Term Memory Dual-Attention Transformer (BL-DATransformer) is proposed. An ablation study is conducted on this architecture, with analysis performed to evaluate the influence of diverse input features on model performance. Finally, the comparison results with LSTM, Transformer, and Informer indicate that under smooth traffic conditions, when the training length is 70%, the RMSE is reduced by 84.32%, 74.94%, and 18.49%, respectively. Under congested traffic conditions, with the same training length, the RMSE is reduced by 88.30%, 78.33%, and 26.52%, respectively. The result demonstrates that the prediction method has high accuracy and practical application value.
Full article
(This article belongs to the Special Issue Revolutionizing the Automotive Landscape: Fuel Cell Applications Powering the Future)
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Open AccessArticle
Enhancing E-Bike Efficiency with Intelligent Battery Temperature Control
by
Tiago Gándara, Adriano Figueiredo, José Santos and Tiago Silva
World Electr. Veh. J. 2025, 16(6), 289; https://doi.org/10.3390/wevj16060289 - 22 May 2025
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This work presents an innovative approach to battery thermal management for e-bikes by addressing heat generation at its source rather than relying on conventional cooling techniques. Traditional systems rely on heat sinks, fans, phase change materials, or cooling fluids, which increase cost and
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This work presents an innovative approach to battery thermal management for e-bikes by addressing heat generation at its source rather than relying on conventional cooling techniques. Traditional systems rely on heat sinks, fans, phase change materials, or cooling fluids, which increase cost and complexity. In contrast, this study integrates embedded thermal management algorithms into the e-bike’s motor controller, enabling temperature regulation through performance limitation. Two models are investigated: a reactive algorithm that reduces speed as battery temperature nears a critical threshold, and a predictive algorithm that forecasts future temperature evolution and adjusts speed accordingly. Experimental results show that the reactive algorithm successfully limited battery temperature to 26.7% below the critical value but at the cost of speed reductions up to 40%. The predictive model, tested in two configurations, demonstrated improved performance, limiting speed by a maximum of 20% while maintaining stable temperature profiles. These findings confirm that embedded algorithms can effectively manage battery temperature, with the reactive model being suitable for low-complexity applications and the predictive model offering enhanced performance when more computational resources are available.
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Open AccessArticle
Mediating Role of 6V-Based SBMI Between Competitive Strategies and Firm Performance: An Empirical Study of China’s Electric Vehicle Industry
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Xiaohui Zang, Raja Nazim Abdullah, Yi Feng, Mingling Wu, Yanqiu Lu, Enzhou Zhu and Yingfeng Zhang
World Electr. Veh. J. 2025, 16(5), 288; https://doi.org/10.3390/wevj16050288 - 21 May 2025
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Strategic management and sustainable business model innovation (SBMI) are widely recognized important firm performance. This study develops a theoretical framework that integrates competitive strategy, SBMI, and performance, with SBMI conceptualized through the multidimensional 6V model. While the model is broadly applicable across industries,
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Strategic management and sustainable business model innovation (SBMI) are widely recognized important firm performance. This study develops a theoretical framework that integrates competitive strategy, SBMI, and performance, with SBMI conceptualized through the multidimensional 6V model. While the model is broadly applicable across industries, this study focuses on the electric vehicle (EV) sector in China as an empirical case to test the proposed relationships. Using survey data from 261 managerial respondents across nine major Chinese EV brands, PLS-SEM is employed to examine both direct and mediated effects of differentiation and cost leadership strategies. The results confirm that both strategies positively influence firm performance; however, the mediating roles of SBMI dimensions vary. This study contributes to the literature by demonstrating the explanatory power of the 6V-SBMI framework and offering practical insights for firms seeking to align strategic choices with sustainability-oriented innovation.
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Open AccessArticle
Evaluating Carbon Emissions: A Lifecycle Comparison Between Electric and Conventional Vehicles
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Farhan Hameed Malik, Walid Ayadi, Ghulam Amjad Hussain, Zunaib Maqsood Haider, Fawwaz Alkhatib and Matti Lehtonen
World Electr. Veh. J. 2025, 16(5), 287; https://doi.org/10.3390/wevj16050287 - 21 May 2025
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Due to global warming, ozone depletion and their ramifications on the Arctic and Antarctic snowscapes, there has been an incentivized drive towards net zero-carbon emission policies by several countries. These policies extend to several sectors, including several manufacturing and processing industries and transportation,
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Due to global warming, ozone depletion and their ramifications on the Arctic and Antarctic snowscapes, there has been an incentivized drive towards net zero-carbon emission policies by several countries. These policies extend to several sectors, including several manufacturing and processing industries and transportation, which are a few of their notable stakeholders. In the transportation sector, this journey towards net zero-carbon emissions is aided by the adoption of battery electric vehicles (BEVs) due to their zero-carbon emissions during operation. However, they might have zero running emissions, but they do have emissions when charging through conventional sources. This research paper looks at the carbon emissions produced by both electric vehicles (EVs) and internal combustion engine (ICE) vehicles during their operational stages and compares them based on a 200,000 km driving range, battery manufacturing emissions and different power production alternatives to draw up some very important recommendations. The analysis presented in this paper helps in drawing conclusions and proposes ideas which, when included in transport policies, will help curb global warming and eventually lead to the sustainable development of the transport sector. The analysis in this study shows that the emissions needed to produce a single battery unit have increased by approximately 258.7% with the change in battery production locations. Furthermore, charging EVs with a fossil-fuel-dominated grid has shown an increase in emissions of 17.98% compared to the least emissive ICE car considered in the study. Finally, policy update recommendations which are essential for the sustainable development of the transport sector are discussed.
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Open AccessArticle
Optimization of Electric Vehicle Charging and Discharging Strategies Considering Battery Health State: A Safe Reinforcement Learning Approach
by
Shuifu Gu, Kejun Qian and Yongbiao Yang
World Electr. Veh. J. 2025, 16(5), 286; https://doi.org/10.3390/wevj16050286 - 20 May 2025
Abstract
With the widespread adoption of electric vehicles (EVs), optimizing their charging and discharging strategies to improve energy efficiency and extend battery life has become a focal point of current research. Traditional charging and discharging strategies often fail to adequately consider the battery’s state
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With the widespread adoption of electric vehicles (EVs), optimizing their charging and discharging strategies to improve energy efficiency and extend battery life has become a focal point of current research. Traditional charging and discharging strategies often fail to adequately consider the battery’s state of health (SOH), resulting in accelerated battery aging and decreased efficiency. In response, this paper proposes a safe reinforcement learning–based optimization method for EV charging and discharging strategies, aimed at minimizing charging and discharging costs while accounting for battery SOH. First, a novel battery health status prediction model based on physics-informed hybrid neural networks (PHNN) is designed. Then, the EV charging and discharging decision-making problem, considering battery health status, is formulated as a constrained Markov decision process, and an interior-point policy optimization (IPO) algorithm based on long short-term memory (LSTM) neural networks is proposed to solve it. The algorithm filters out strategies that violate constraints by introducing a logarithmic barrier function. Finally, the experimental results demonstrate that the proposed method significantly enhances battery life while maintaining maximum economic benefits during the EV charging and discharging process. This research provides a novel solution for intelligent and personalized charging strategies for EVs, which is of great significance for promoting the sustainable development of new energy vehicles.
Full article
(This article belongs to the Special Issue Modeling, Prediction and Management of Charging and Discharging Loads for Electric Vehicle–Grid Interaction Under the “Double Carbon” Strategy)
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Open AccessArticle
Applying QFD to the Vehicle Market Deployment Process
by
Marta Pino-Servian, Álvaro de la Puente-Gil, Antonio Colmenar-Santos and Enrique Rosales-Asensio
World Electr. Veh. J. 2025, 16(5), 285; https://doi.org/10.3390/wevj16050285 - 20 May 2025
Abstract
This study presents a practical methodology for systematically incorporating customer expectations and needs into the market implementation of electric vehicles (EVs). Utilising Quality Function Deployment (QFD), companies can evaluate and understand customer requirements, optimise product improvements, and allocate resources efficiently. Though not widely
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This study presents a practical methodology for systematically incorporating customer expectations and needs into the market implementation of electric vehicles (EVs). Utilising Quality Function Deployment (QFD), companies can evaluate and understand customer requirements, optimise product improvements, and allocate resources efficiently. Though not widely adopted in many Western contexts, QFD proves valuable in enhancing strategic decision making and improving market penetration. Moreover, the integration of EVs with renewable energy and advancements in battery and grid technologies strengthens their environmental and economic benefits. As technological progress and policy support continue, EVs are positioned to drive sustainable transportation and contribute to global carbon reduction goals.
Full article
(This article belongs to the Special Issue The Contribution of Electric Vehicles to Realization of Dual Carbon Goal)
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Open AccessArticle
Resource Optimization Method Based on Spatio-Temporal Modeling in a Complex Cluster Environment for Electric Vehicle Charging Scenarios
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Hongwei Wang, Wei Liu, Chenghui Wang, Kao Guo and Zihao Wang
World Electr. Veh. J. 2025, 16(5), 284; https://doi.org/10.3390/wevj16050284 - 20 May 2025
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In intelligent cluster systems, the spatio-temporal complexity of agent data collection and resource allocation, as well as the problems in collaborative organizations, present substantial challenges to efficient resource distribution. To address this, a novel self-organizing prediction method for spatio-temporal resource allocation is proposed.
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In intelligent cluster systems, the spatio-temporal complexity of agent data collection and resource allocation, as well as the problems in collaborative organizations, present substantial challenges to efficient resource distribution. To address this, a novel self-organizing prediction method for spatio-temporal resource allocation is proposed. In the spatio-temporal modeling part, dilated convolution is applied for time modeling. Its dilation rate grows exponentially with the layer depth, allowing it to effectively capture the time trends of graph nodes and handle long time series data. For spatial modeling, an innovative dual-view dynamic graph convolutional network architecture is utilized to accurately explore the static and dynamic correlation information of the spatial layout of charging piles. Meanwhile, a composite self-organizing mechanism integrating a trust model is put forward. The trust model assists agents in choosing partners, and the Q-learning algorithm of the intelligent cluster realizes the independent evaluation of rewards and the optimization of relationship adaptation. In the experimental scenario of electric vehicle charging, considering charging piles as agents, under the home charging mode, the self-organizing charging scheduling can reduce the total load range by up to 90.37%. It effectively shifts the load demand from peak periods to valley periods, minimizes the total peak–valley load difference, and significantly improves the security and reliability of the microgrid, thus providing a practical solution for resource allocation in intelligent clusters.
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Open AccessArticle
Temperature-Influenced SOC Estimation of LiFePO4 Batteries in Hybrid Electric Tractors Based on SAO-LSTM Model
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Yiwei Wu, Xiaohui Liu, Jingyun Zhang, Mengnan Liu, Lin Wang, Xiaoxiao Du and Xianghai Yan
World Electr. Veh. J. 2025, 16(5), 283; https://doi.org/10.3390/wevj16050283 - 19 May 2025
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LiFePO4 batteries are widely used in hybrid electric tractors due to their high energy density, stable working voltage, low self-discharge rate, long cycle life, absence of memory effect, environmental friendliness, and flexible sizing. Accurate State of Charge (SOC) estimation is crucial for
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LiFePO4 batteries are widely used in hybrid electric tractors due to their high energy density, stable working voltage, low self-discharge rate, long cycle life, absence of memory effect, environmental friendliness, and flexible sizing. Accurate State of Charge (SOC) estimation is crucial for Battery Management Systems (BMSs). This study utilizes a LiFePO4 battery dataset from the University of Maryland to improve SOC estimation accuracy. The forgetting factor recursive least squares method was employed for parameter identification, and a temperature-dependent second-order RC equivalent circuit model was developed in MATLAB R2024a/Simulink. The proposed SAO-LSTM model demonstrated superior SOC estimation performance compared to traditional ampere-hour integration, achieving a 98.23% error reduction. Evaluation results showed 0.39% and 0.31% decreases in root mean square error and mean absolute error, respectively, confirming the model’s robustness and high estimation accuracy for LiFePO4 batteries in hybrid electric tractors.
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Open AccessArticle
An Artificial Neural Network-Based Battery Management System for LiFePO4 Batteries
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Roger Painter, Ranganathan Parthasarathy, Lin Li, Irucka Embry, Lonnie Sharpe and S. Keith Hargrove
World Electr. Veh. J. 2025, 16(5), 282; https://doi.org/10.3390/wevj16050282 - 19 May 2025
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We present a reduced-order battery management system (BMS) for lithium-ion cells in electric and hybrid vehicles that couples a physics-based single-particle model (SPM) derived from the Cahn–Hilliard phase-field formulation with a lumped heat-transfer model. A three-dimensional COMSOL® 5.0 simulation of a LiFePO
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We present a reduced-order battery management system (BMS) for lithium-ion cells in electric and hybrid vehicles that couples a physics-based single-particle model (SPM) derived from the Cahn–Hilliard phase-field formulation with a lumped heat-transfer model. A three-dimensional COMSOL® 5.0 simulation of a LiFePO4 particle produced voltage and temperature data across ambient temperatures (253–298 K) and discharge rates (1 C–20.5 C). Principal component analysis (PCA) reduced this dataset to five latent variables, which we then mapped to experimental voltage–temperature profiles of an A123 Systems 26650 2.3 Ah cell using a self-normalizing neural network (SNN). The resulting ROM achieves real-time prediction accuracy comparable to detailed models while retaining essential electrothermal dynamics.
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Open AccessArticle
A Secure Data Collection Method Based on Deep Reinforcement Learning and Lightweight Authentication
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Yunlong Wang, Jie Zhang, Guangjie Han and Dugui Chen
World Electr. Veh. J. 2025, 16(5), 281; https://doi.org/10.3390/wevj16050281 - 19 May 2025
Abstract
Cooperative Unmanned Aerial Vehicle (UAV) technology can significantly improve data acquisition in Internet of Things (IoT) environments, which are characterized by wide distribution and limited capacity of ground-based devices. However, due to the open nature of wireless communications, such applications face security threats
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Cooperative Unmanned Aerial Vehicle (UAV) technology can significantly improve data acquisition in Internet of Things (IoT) environments, which are characterized by wide distribution and limited capacity of ground-based devices. However, due to the open nature of wireless communications, such applications face security threats posed by UAV authentication, especially in scalable IoT environments. To address such challenges, we propose a lightweight chain authentication protocol for scalable IoT environments (LCAP-SIoT), which uses Physical Unclonable Functions (PUFs) and distributed authentication to secure communications, and a secure data collection algorithm, named LS-QMIX, which fuses the LCAP-SIoT and Q-learning Mixer (QMIX) algorithm to optimize the path planning and cooperation efficiency of the multi-UAV system. According to simulation analysis, LCAP-SIoT outperforms existing solutions in terms of computing and communication costs, and LS-QMIX results in superior performance in terms of data collection rate, task completion time, and the success rate of authentication for newly joined UAVs, indicating the feasibility of LS-QMIX in dynamic expansion scenarios.
Full article
(This article belongs to the Special Issue Internet of Vehicles and Autonomous Connected Vehicle: Privacy and Security)
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Open AccessArticle
Hybrid Vehicle Battery Health State Estimation Based on Intelligent Regenerative Braking Control
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Chellappan Kavitha, Gupta Gautam, Ravi Sudeep, Chidambaram Kannan and Bragadeshwaran Ashok
World Electr. Veh. J. 2025, 16(5), 280; https://doi.org/10.3390/wevj16050280 - 19 May 2025
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In response to the evolving transportation landscape, the safety and durability of hybrid electric vehicles (HEVs) necessitate the development of high-performance, reliable health management systems for batteries. The state of health (SOH) provides vital insights about the performance and longevity of batteries, thus
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In response to the evolving transportation landscape, the safety and durability of hybrid electric vehicles (HEVs) necessitate the development of high-performance, reliable health management systems for batteries. The state of health (SOH) provides vital insights about the performance and longevity of batteries, thus enhancing opportunities for efficient energy management in hybrid systems. Despite various research efforts for battery SOH estimation, many of them fall short of the demands for real-time automotive applications. Real-time SOH estimation is crucial for accurate battery fault diagnosis and maintaining precise estimation of the state of charge (SOC) and state of power (SOP), which are essential for the optimal functioning of hybrid systems. In this study, a fuzzy logic estimation method is deployed to determine the tire road friction coefficient (TRFC) and various control strategies are adopted to establish regenerative cut-off points. A MATLAB-based SOH estimation model was developed using a Kalman SOH estimator, which helps to observe the effects of different control strategies on the battery’s SOH. This approach enhances the accuracy and reliability of SOH estimation in real-time applications and improves the effectiveness of battery fault diagnosis. From the results, ANFIS outperformed standard methods, showing approximately 4–6% higher SOH retention across various driving cycles.
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Open AccessArticle
Energy-Efficient Battery Thermal Management in Electric Vehicles Using Artificial-Neural-Network-Based Model Predictive Control
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Kiheon Nam and Changsun Ahn
World Electr. Veh. J. 2025, 16(5), 279; https://doi.org/10.3390/wevj16050279 - 17 May 2025
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This study presents a Model Predictive Control (MPC) strategy for the Battery Thermal Management System (BTMS) in electric vehicles (EVs) to optimize energy efficiency while maintaining battery temperature within the optimal range. Due to the complexity of BTMS dynamics, a high-fidelity model was
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This study presents a Model Predictive Control (MPC) strategy for the Battery Thermal Management System (BTMS) in electric vehicles (EVs) to optimize energy efficiency while maintaining battery temperature within the optimal range. Due to the complexity of BTMS dynamics, a high-fidelity model was developed using MATLAB/Simscape (2021a), and an artificial neural network (ANN)-based model was designed to achieve high accuracy with reduced computational load. To mitigate oscillatory control inputs observed in conventional MPC, an infinity-horizon MPC framework was introduced, incorporating a value function that accounts for system behavior beyond the prediction horizon. The proposed controller was evaluated using a simulation environment against a conventional rule-based controller under varying ambient temperatures. Results demonstrated significant energy savings, including a 78.9% reduction in low-temperature conditions, a 36% reduction in moderate temperatures, and a 27.8% reduction in high-temperature environments. Additionally, the controller effectively stabilized actuator operation, improving system longevity. These findings highlight the potential of ANN-assisted MPC for enhancing BTMS performance while minimizing energy consumption in EVs.
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Open AccessArticle
Planning and Optimizing Charging Infrastructure and Scheduling in Smart Grids with PyPSA-LOPF: A Case Study at Cadi Ayyad University
by
Meriem Belaid, Said El Beid, Said Doubabi and Anas Hatim
World Electr. Veh. J. 2025, 16(5), 278; https://doi.org/10.3390/wevj16050278 - 17 May 2025
Abstract
This paper presents an optimization model for the charging infrastructure of electric vehicles (EV) designed to minimize installation costs, maximize the utilization of photovoltaic energy, reduce dependency on the electrical grid, and optimize charging times. The model utilizes methodologies such as Linear Optimal
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This paper presents an optimization model for the charging infrastructure of electric vehicles (EV) designed to minimize installation costs, maximize the utilization of photovoltaic energy, reduce dependency on the electrical grid, and optimize charging times. The model utilizes methodologies such as Linear Optimal Power Flow (LOPF) to align EV charging schedules with the availability of renewable energy sources. Key inputs for the model include Photovoltaic (PV) production profiles, EV charging demands, specifications of the chargers, and the availability of grid energy. The framework integrates installation costs, grid energy consumption, and charging duration into a weighted objective function, ensuring energy balance and operational efficiency while adhering to budgetary constraints. Five distinct optimization scenarios are analyzed to evaluate the trade-offs between cost, charging duration, and reliance on various energy sources. The simulation results obtained from Cadi Ayyad University validate the model’s effectiveness in balancing costs, enhancing charging performance, and increasing dependence on solar energy. This approach provides a comprehensive solution for the development of sustainable and cost-effective EV charging infrastructure.
Full article
(This article belongs to the Special Issue Electric Vehicles and Charging Facilities for a Sustainable Transport Sector)
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Open AccessArticle
Improving State-of-Health Estimation for Lithium-Ion Batteries Based on a Generative Adversarial Network and Partial Discharge Profiles
by
Hangyu Zhang and Yi-Horng Lai
World Electr. Veh. J. 2025, 16(5), 277; https://doi.org/10.3390/wevj16050277 - 16 May 2025
Abstract
The aging effect weakens the capacity of lithium batteries, seriously affecting the performance of electric vehicles. Developing state-of-health estimation technology for lithium batteries can help to optimize the charging and discharging strategies of electric vehicles. This study investigates the use of partial discharge
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The aging effect weakens the capacity of lithium batteries, seriously affecting the performance of electric vehicles. Developing state-of-health estimation technology for lithium batteries can help to optimize the charging and discharging strategies of electric vehicles. This study investigates the use of partial discharge data for SOH estimation to address the unstable output of traditional estimation models when using partial discharge data under low-voltage conditions. This study first used the DoppelGANger network to generate artificially synthesized data. After the data augmentation process, we trained the temporal convolutional network to construct a data-driven SOH model. Finally, the performance of the SOH model output was evaluated using three indicators: RMSE, MAPE, and delta. The proposed method improved five kinds of low-voltage operating conditions in seven testing scenarios compared with traditional SOH estimation models. The experimental results provide a practical solution for data-driven SOH estimation.
Full article
(This article belongs to the Special Issue Lithium-Ion Battery Diagnosis: Health and Safety)
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Open AccessArticle
A Road-Adaptive Vibration Reduction System with Fuzzy PI Control Approach for Electric Bicycles
by
Chao-Li Meng, Van-Tung Bui, Chyi-Ren Dow, Shun-Ming Chang and Yueh-E (Bonnie) Lu
World Electr. Veh. J. 2025, 16(5), 276; https://doi.org/10.3390/wevj16050276 - 16 May 2025
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Riding comfort and safety are essential requirements for any form of transportation but particularly for electric bicycles (e-bikes), which are highly affected by varying road conditions. These factors largely depend on the effectiveness of the e-bike’s control strategy. While several studies have proposed
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Riding comfort and safety are essential requirements for any form of transportation but particularly for electric bicycles (e-bikes), which are highly affected by varying road conditions. These factors largely depend on the effectiveness of the e-bike’s control strategy. While several studies have proposed control approaches that address comfort and safety, vibration—an influential factor in both structural integrity and rider experience—has received limited attention during the design phase. Moreover, many commercially available e-bikes provide manual assistance-level settings, leaving comfort and safety management to the rider’s experience. This study proposes a Road-Adaptive Vibration Reduction System (RAVRS) that can be deployed on an e-bike rider’s smartphone to automatically maintain riding comfort and safety using manual assistance control. A fuzzy-based control algorithm is adopted to dynamically select the appropriate assistance level, aiming to minimize vibration while maintaining velocity and acceleration within thresholds associated with comfort and safety. This study presents a vibration analysis to highlight the significance of vibration control in improving electronic reliability, reducing mechanical fatigue, and enhancing user experience. A functional prototype of the RAVRS was implemented and evaluated using real-world data collected from experimental trips. The simulation results demonstrate that the proposed system achieves effective control of speed and acceleration, with success rates of 83.97% and 99.79%, respectively, outperforming existing control strategies. In addition, the proposed RAVRS significantly enhances the riding experience by improving both comfort and safety.
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Open AccessArticle
Stepwise Segmented Skewed Pole Modulation Vibration Reduction Design for Integer-Slot Motors
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Huawei Wu, Shaokang Lu, Xiaoyuan Zhu, Weiye Li and Jianping Peng
World Electr. Veh. J. 2025, 16(5), 275; https://doi.org/10.3390/wevj16050275 - 16 May 2025
Abstract
To optimize the modulated vibration generated by the integer-slot interior permanent magnet synchronous motor (IPMSM), a stepwise segmented skewed pole method was proposed, using an 8-pole 48-slot IPMSM as an example. First, the vibration characteristics of the motor were studied, and the theoretical
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To optimize the modulated vibration generated by the integer-slot interior permanent magnet synchronous motor (IPMSM), a stepwise segmented skewed pole method was proposed, using an 8-pole 48-slot IPMSM as an example. First, the vibration characteristics of the motor were studied, and the theoretical mechanisms of the magnetic field modulation effect and radial force modulation effect were explained. The study showed that high-order radial forces can excite larger low-order vibrations under the influence of radial force modulation. Then, in response to the axial spacing in the linear skewed pole structure when canceling the 48th-order radial force, a stepwise skewed pole structure was proposed. The suppression mechanism of this skewed pole structure on the motor’s modulated vibration was analyzed, and the optimization effect of different segment numbers on the motor’s vibration acceleration at was discussed. Finally, models for the motor’s magnetic field, structural field, and acoustic field before and after skewing were established, and simulations were conducted to compare the magnitudes of the radial forces at each order and their vibration noise performance. The results showed that after stepwise skewed pole optimization, the radial force that excites the modulated vibration was reduced by 68%, the maximum vibration acceleration on the casing surface was reduced by 84%, and the overall noise was reduced by 7.491 dB, effectively suppressing electromagnetic vibration noise.
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(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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Open AccessArticle
Research on Multi-Target Point Path Planning Based on APF and Improved Bidirectional RRT* Fusion Algorithm
by
Zijian Bian, Gang Li and Xizheng Wang
World Electr. Veh. J. 2025, 16(5), 274; https://doi.org/10.3390/wevj16050274 - 16 May 2025
Abstract
In order to solve the problems of traditional RRT algorithms that are too random in planning, have low planning efficiency, and have insufficient security, this paper proposes an algorithm that fuses APF and the improved bidirectional RRT* algorithm and proposes a heuristic planning
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In order to solve the problems of traditional RRT algorithms that are too random in planning, have low planning efficiency, and have insufficient security, this paper proposes an algorithm that fuses APF and the improved bidirectional RRT* algorithm and proposes a heuristic planning strategy to sort multiple target points so that the fusion improvement algorithm can traverse multiple target points with a short path length. This study also aims to improve the RRT* algorithm by using optimization strategies such as bidirectional sampling and adding an adaptive target bias strategy to improve its efficiency in obtaining global paths. The obstacle expansion strategy is used in the APF algorithm to expand the repulsion effect, and the APF algorithm after the obstacle expansion is fused with the improved bidirectional RRT* algorithm, adding gravitational potential field-guided sampling at random points, avoiding local optimum solution, and improving the sampling efficiency while accelerating the acquisition of global paths. The redundant node deletion strategy is introduced to simplify the path, and the repulsive potential field is used to improve the Bezier smoothing method, avoiding collisions caused by path distortion due to smoothing. A multi-target point heuristic planning strategy is proposed to achieve shorter global paths while maintaining shorter local paths, taking into account both local solutions and optimal solutions, so that the fusion algorithm can be applied to the path planning of multi-target points.
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(This article belongs to the Special Issue Research on Intelligent Vehicle Path Planning Algorithm)
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Open AccessArticle
Dynamic Closed-Loop Validation of a Hardware-in-the-Loop Testbench for Parallel Hybrid Electric Vehicles
by
Marc Timur Düzgün, Christian Heusch, Sascha Krysmon, Christian Dönitz, Sung-Yong Lee, Jakob Andert and Stefan Pischinger
World Electr. Veh. J. 2025, 16(5), 273; https://doi.org/10.3390/wevj16050273 - 14 May 2025
Abstract
The complexity and shortening of development cycles in the automotive industry, particularly with the rise in hybrid electric vehicle sales, increases the need for efficient calibration and testing methods. Virtualization using hardware-in-the-loop testbenches has the potential to counteract these trends, specifically for the
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The complexity and shortening of development cycles in the automotive industry, particularly with the rise in hybrid electric vehicle sales, increases the need for efficient calibration and testing methods. Virtualization using hardware-in-the-loop testbenches has the potential to counteract these trends, specifically for the calibration of hybrid operating strategies. This paper presents a dynamic closed-loop validation of a hardware-in-the-loop testbench designed for the virtual calibration of hybrid operating strategies for a plug-in hybrid electric vehicle. Requirements regarding the hardware-in-the-loop testbench accuracy are defined based on the investigated use case. From this, a dedicated hardware-in-the-loop testbench setup is derived, including an electrical setup as well as a plant simulation model. The model is then operated in a closed loop with a series production hybrid control unit. The closed-loop validation results demonstrate that the chassis simulation reproduces driving resistance closely aligning with the reference data. The driver model follows target speed profiles within acceptable limits, achieving an R2 = 0.9993, comparable to the R2 reached by trained human drivers. The transmission model replicates the gear ratios, maintaining rotational speed deviations below 30 min−1. Furthermore, the shift strategy is implemented in a virtual control unit, resulting in a gear selection comparable to reference measurements. The energy flow simulation in the complete powertrain achieves high accuracy. Deviations in the high-voltage battery state of charge remain below 50 Wh in a WLTC charge-sustaining drive cycle and are thus within the acceptable error margin. The net energy change criterion is satisfied with the hardware-in-the-loop testbench, achieving a net energy change of 0.202%, closely matching the reference measurement of 0.159%. Maximum deviations in cumulative high-voltage battery energy are proven to be below 10% in both the charging and discharging directions. Fuel consumption and CO2 emissions are modeled with deviations below 3%, validating the simulation’s representation of vehicle efficiency. Real-time capability is achieved under all investigated operating conditions and test scenarios. The testbench achieves a real-time factor of at least 1.104, ensuring execution within the hard real-time criterion. In conclusion, the closed-loop validation confirms that the developed hardware-in-the-loop testbench satisfies all predefined requirements, accurately simulating the behavior of the reference vehicle.
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(This article belongs to the Special Issue Advanced Vehicle Dynamics Identification, Control and Observer Methods for Autonomous, Electrified Vehicles)
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