World Electric Vehicle Journal

21 pages, 13843 KiB

Open AccessArticle

Improved Quintic Polynomial Autonomous Vehicle Lane-Change Trajectory Planning Based on Hybrid Algorithm Optimization

by Yuelou Zhang, Lingshan Chen and Ning Li

World Electr. Veh. J. 2025, 16(5), 244; https://doi.org/10.3390/wevj16050244 - 23 Apr 2025

A trajectory planning method is proposed to address the lane-changing problem in intelligent vehicles. The method is based on quintic polynomial improvement. The transit position is determined according to the position and state of motion of the vehicle and the obstacle vehicle; the [...] Read more.

A trajectory planning method is proposed to address the lane-changing problem in intelligent vehicles. The method is based on quintic polynomial improvement. The transit position is determined according to the position and state of motion of the vehicle and the obstacle vehicle; the lane-changing process is divided into two segments. The quintic polynomials are commonly applied in trajectory planning, respectively, in the two segments. According to the different characteristics of the lane-changing paths in the front and rear segments, a multi-objective optimization function with different weight coefficients is established. A safe and comfortable lane-changing trajectory is achieved through the improved particle swarm optimization algorithm. Real-time simulation tests of lane-changing method are conducted on the hardware-in-the-loop platform. The method can be used in different scenarios to plan safe and comfortable trajectories. Full article

(This article belongs to the Special Issue Motion Planning and Control of Autonomous Vehicles)

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24 pages, 1591 KiB

Open AccessArticle

A Privacy-Preserving Scheme for Charging Reservations and Subsequent Deviation Settlements for Electric Vehicles Based on a Consortium Blockchain

by Beibei Wang, Yikun Yang, Wenjie Liu and Lun Xu

World Electr. Veh. J. 2025, 16(5), 243; https://doi.org/10.3390/wevj16050243 - 22 Apr 2025

Abstract

Electric vehicles have garnered substantial attention as an environmentally sustainable transportation alternative amid escalating global concerns regarding ecological preservation and energy resource management. While the proliferation of electric vehicles necessitates the development of efficient and secure charging infrastructure, the inherent communication-intensive nature of [...] Read more.

Electric vehicles have garnered substantial attention as an environmentally sustainable transportation alternative amid escalating global concerns regarding ecological preservation and energy resource management. While the proliferation of electric vehicles necessitates the development of efficient and secure charging infrastructure, the inherent communication-intensive nature of the charging processes has raised concerns regarding potential privacy vulnerabilities. Our paper introduces a privacy protection scheme specifically designed for electric vehicle charging reservations to address this issue. The primary goal of this scheme is to protect user privacy while maintaining operational efficiency and economic viability for charging providers. Our proposed solution ensures a secure and private environment for charging reservation transactions and subsequent deviation settlements by incorporating advanced technologies, including zero-knowledge proof, a consortium blockchain, and homomorphic encryption. The scheme encrypts charging reservation information and securely transmits it via a consortium blockchain, effectively shielding the sensitive data of all participating parties. Notably, the experimental findings establish the robustness of our scheme in terms of its security and privacy protection, aligning with the stringent demands of electric vehicle charging operations. Full article

15 pages, 242 KiB

Open AccessCommunication

Enhancing Sustainable Last-Mile Delivery: The Impact of Electric Vehicles and AI Optimization on Urban Logistics

by Joao C. Ferreira and Marco Esperança

World Electr. Veh. J. 2025, 16(5), 242; https://doi.org/10.3390/wevj16050242 - 22 Apr 2025

Abstract

The rapid growth of e-commerce has intensified the need for efficient and sustainable last-mile delivery solutions in urban environments. This paper explores the integration of electric vehicles (EVs) and artificial intelligence (AI) into a combined framework to enhance the environmental, operational, and economic [...] Read more.

The rapid growth of e-commerce has intensified the need for efficient and sustainable last-mile delivery solutions in urban environments. This paper explores the integration of electric vehicles (EVs) and artificial intelligence (AI) into a combined framework to enhance the environmental, operational, and economic performance of urban logistics. Through a comprehensive literature review, we examine current trends, technological developments, and implementation challenges at the intersection of smart mobility, green logistics, and digital transformation. We propose an operational framework that leverages AI for route optimization, fleet coordination, and energy management in EV-based delivery networks. This framework is validated through a real-world case study conducted in Lisbon, Portugal, where a logistics provider implemented a city consolidation center model supported by AI-driven optimization tools. Using key performance indicators—including delivery time, energy consumption, fleet utilization, customer satisfaction, and CO₂ emissions—we measure the pre- and post-AI deployment impacts. The results demonstrate significant improvements across all metrics, including a 15–20% reduction in delivery time, a 10–25% gain in energy efficiency, and up to a 40% decrease in emissions. The findings confirm that the synergy between EVs and AI provides a robust and scalable model for achieving sustainable last-mile logistics, supporting broader urban mobility and climate objectives. Full article

(This article belongs to the Special Issue Theory, Method and Application of New Energy and Intelligent Transportation)

19 pages, 5405 KiB

Open AccessArticle

Research on Trajectory Prediction Based on Front Vehicle Sideslip Recognition

by Jian Ou, Xiaolong Cheng and Pengju Zhang

World Electr. Veh. J. 2025, 16(4), 241; https://doi.org/10.3390/wevj16040241 - 21 Apr 2025

Abstract

In order to solve the problem of emergency collision avoidance of autonomous vehicles when the front vehicle is unstable and sliding under high-speed conditions, a research method for the state recognition of the front side-skid vehicle and the trajectory prediction of the front [...] Read more.

In order to solve the problem of emergency collision avoidance of autonomous vehicles when the front vehicle is unstable and sliding under high-speed conditions, a research method for the state recognition of the front side-skid vehicle and the trajectory prediction of the front side-skid vehicle was proposed. By extracting the key features of the vehicle in front of the vehicle in danger of sliding to build a skidding recognition model of the vehicle in front, a skidding recognition strategy of the vehicle in front was designed based on the extracted skidding feature indexes to judge the skidding state of the vehicle in front. The state quantity of the sliding vehicle in front is selected, and the constant rotation rate and acceleration model (CTRA) is established to predict the trajectory of the sliding vehicle in front in a short time. Considering the simplified assumptions of the model and the noise in the process of sensor perception information, the Unscented Kalman Filter (UKF) is used to deal with the uncertainty in the trajectory prediction process, the possible position and covariance of the front sideslipping vehicle are calculated, and the possible future area of the front sideslipping vehicle is estimated under the condition of a probability of 0.9. Through the established Carsim and Simulink co-simulation platform, the effectiveness of the front vehicle skidding state recognition strategy and the accuracy of the trajectory prediction of the sliding vehicle are verified under the condition of high speed and low attachment. Full article

(This article belongs to the Special Issue Motion Planning and Control of Autonomous Vehicles)

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26 pages, 8624 KiB

Open AccessArticle

Analysis of the Correlation Between Electric Bus Charging Strategies and Carbon Emissions from Electricity Production

by Szabolcs Kocsis Szürke, Roland Pál and Gábor Saly

World Electr. Veh. J. 2025, 16(4), 240; https://doi.org/10.3390/wevj16040240 - 20 Apr 2025

Abstract

Reducing carbon dioxide emissions in transportation has become a priority for achieving emission targets. Transitioning to electric vehicles significantly decreases global CO₂ emissions and reduces urban noise and air pollution. The selection of efficient charging strategies for electric bus fleets substantially influences [...] Read more.

Reducing carbon dioxide emissions in transportation has become a priority for achieving emission targets. Transitioning to electric vehicles significantly decreases global CO₂ emissions and reduces urban noise and air pollution. The selection of efficient charging strategies for electric bus fleets substantially influences their environmental impact. This study analyzes the charging strategy for electric bus fleets based on real operational data from Győr, Hungary. It evaluates the impact of different charging times and strategies on CO₂ emissions, considering the energy mixes of Hungary, Poland, Germany, and Sweden. A methodology has been developed for defining sustainable and environmentally friendly charging strategies by incorporating operational conditions as well as daily, monthly, and seasonal fluctuations in emission factors. Results indicate substantial potential for emission reduction through the recommended alternative charging strategies, although further studies regarding battery lifespan and economic feasibility of infrastructure investments are recommended. The novelty of this work lies in integrating real charging data with hourly country-specific emission intensity values to assess environmental impacts dynamically. A comparative framework of four charging strategies provides quantifiable insights into emission reduction potential under diverse national energy mixes. Full article

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13 pages, 2215 KiB

Open AccessArticle

Estimation Algorithm for Vehicle Motion Parameters Based on Innovation Covariance in AC Chassis Dynamometer

by Xiaorui Zhang, Xingyuan Xu and Hengliang Shi

World Electr. Veh. J. 2025, 16(4), 239; https://doi.org/10.3390/wevj16040239 - 20 Apr 2025

Abstract

When the alternating current (AC) chassis dynamometer system measures the motion parameters of a test vehicle, it is subject to interference from measurement noise, leading to an increase in testing errors. An innovative adaptive Kalman Filtering (KF) algorithm based on innovation covariance is [...] Read more.

When the alternating current (AC) chassis dynamometer system measures the motion parameters of a test vehicle, it is subject to interference from measurement noise, leading to an increase in testing errors. An innovative adaptive Kalman Filtering (KF) algorithm based on innovation covariance is proposed. This algorithm facilitates the optimal estimation of vehicle motion parameters without necessitating prior error statistics. The loading model of the measurement and control system is optimized, enabling the precise loading of the dynamometer. The test results indicate that the testing error of the optimized algorithm for the loading model decreases from 6.4% to 1.8%. This improvement establishes a foundation for achieving accurate control of the chassis dynamometer and minimizing testing errors. Full article

(This article belongs to the Special Issue Intelligent Control and Energy Systems for Modern Mobility and Industry)

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19 pages, 4643 KiB

Open AccessArticle

Fault Diagnosis of Permanent Magnet Synchronous Motor Based on Wavelet Packet Transform and Genetic Algorithm-Optimized Back Propagation Neural Network

by Ming Ye, Run Gong, Wanjun Wu, Zhiyuan Peng and Kelin Jia

World Electr. Veh. J. 2025, 16(4), 238; https://doi.org/10.3390/wevj16040238 - 18 Apr 2025

Abstract

In this paper, a fault diagnosis method for permanent magnet synchronous motors is proposed, combining wavelet packet transform (WPT) energy feature extraction and a genetic algorithm (GA)-optimized back propagation (BP) neural network. Firstly, for the common types of motor faults (turn-to-turn short-circuit, phase-to-phase [...] Read more.

In this paper, a fault diagnosis method for permanent magnet synchronous motors is proposed, combining wavelet packet transform (WPT) energy feature extraction and a genetic algorithm (GA)-optimized back propagation (BP) neural network. Firstly, for the common types of motor faults (turn-to-turn short-circuit, phase-to-phase short-circuit, loss of magnetism, inverter open-circuit, rotor eccentricity), a corresponding motor fault model is established. The stator current signals during motor operation are analyzed using wavelet packet transform, and energy features are extracted from them as feature vectors for fault diagnosis. Then, a BP neural network is constructed, and a genetic algorithm is used to optimize its initial weights and thresholds, thereby improving the network’s classification accuracy. The results show that the GA-BP model outperforms the SSA-PNN diagnostic model in terms of fault classification accuracy. In particular, for the diagnosis of normal operation, inverter open-circuit, and demagnetization faults, the accuracy rate reaches 100%. This method demonstrates high diagnostic accuracy and practical application value. Full article

(This article belongs to the Special Issue Permanent Magnet Motors and Driving Control for Electric Vehicles)

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19 pages, 6428 KiB

Open AccessArticle

Design, Modeling, and Experimental Validation of a Hybrid Piezoelectric–Magnetoelectric Energy-Harvesting System for Vehicle Suspensions

by Hicham Mastouri, Amine Ennawaoui, Mohammed Remaidi, Erroumayssae Sabani, Meryiem Derraz, Hicham El Hadraoui and Chouaib Ennawaoui

World Electr. Veh. J. 2025, 16(4), 237; https://doi.org/10.3390/wevj16040237 - 18 Apr 2025

Abstract

The growing demand for sustainable and self-powered technologies in automotive applications has led to increased interest in energy harvesting from vehicle suspensions. Recovering mechanical energy from road-induced vibrations offers a viable solution for powering wireless sensors and autonomous electronic systems, reducing dependence on [...] Read more.

The growing demand for sustainable and self-powered technologies in automotive applications has led to increased interest in energy harvesting from vehicle suspensions. Recovering mechanical energy from road-induced vibrations offers a viable solution for powering wireless sensors and autonomous electronic systems, reducing dependence on external power sources. This study presents the design, modeling, and experimental validation of a hybrid energy-harvesting system that integrates piezoelectric and magnetoelectric effects to efficiently convert mechanical vibrations into electrical energy. A model-based systems engineering (MBSE) approach was used to optimize the system architecture, ensuring high energy conversion efficiency, durability, and seamless integration into suspension systems. The theoretical modeling of both piezoelectric and magnetoelectric energy harvesting mechanisms was developed, providing analytical expressions for the harvested power as a function of system parameters. The designed system was then fabricated and tested under controlled mechanical excitations to validate the theoretical models. Experimental results demonstrate that the hybrid system achieves a maximum power output of 16 µW/cm² from the piezoelectric effect and 3.5 µW/cm² from the magnetoelectric effect. The strong correlation between theoretical predictions and experimental measurements confirms the feasibility of this hybrid approach for self-powered automotive applications. Full article

(This article belongs to the Special Issue Design, Modelling and Control Strategies for Hybrid and Electric Vehicles)

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36 pages, 2524 KiB

Open AccessArticle

Compensating PI Controller’s Transients with Tiny Neural Network for Vector Control of Permanent Magnet Synchronous Motors

by Martin Joel Mouk Elele, Danilo Pau, Shixin Zhuang and Tullio Facchinetti

World Electr. Veh. J. 2025, 16(4), 236; https://doi.org/10.3390/wevj16040236 - 18 Apr 2025

Abstract

Recent advancements in neural networks (NNs) have underscored their potential for deployment in domains that demand computationally intensive operations, including applications on resource-constrained edge devices. This study investigates the integration of a compact neural network, TinyFC, within the Field-Oriented Control (FOC) framework of [...] Read more.

Recent advancements in neural networks (NNs) have underscored their potential for deployment in domains that demand computationally intensive operations, including applications on resource-constrained edge devices. This study investigates the integration of a compact neural network, TinyFC, within the Field-Oriented Control (FOC) framework of a Permanent Magnet Synchronous Motor (PMSM). While proportional–integral (PI) controllers remain a widely adopted choice for FOC due to their simplicity, their performance can degrade significantly under high-frequency speed transitions, where nonlinear dynamics introduce notable inaccuracies. The TinyFC model complements the PI controller by learning the intrinsic dependencies within the control loops and generating corrective signals to alleviate these inaccuracies. To ensure practical implementation, TinyFC underwent extensive optimization procedures, incorporating advanced techniques such as hyperparameter tuning, pruning, and 8-bit quantization. These measures successfully reduced the model’s computational overhead while preserving predictive accuracy. Simulation results demonstrated that embedding TinyFC within the FOC framework substantially reduced overshoot, with the pruned TinyFC entirely eliminating overshoot when integrated into the speed control unit. These findings highlight the feasibility of employing lightweight neural networks for real-time motor control applications, establishing a foundation for more efficient and precise control strategies in edge automotive and industrial systems. Full article

(This article belongs to the Special Issue Permanent Magnet Motors and Driving Control for Electric Vehicles)

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Graphical abstract

23 pages, 12309 KiB

Open AccessArticle

An Improved sRGB Optical Algorithm Considering Thermal Effects and Adaptability for Low-Cost Automotive-Grade Dedicated LED Chips

by Lingling Hong and Miao Liu

World Electr. Veh. J. 2025, 16(4), 235; https://doi.org/10.3390/wevj16040235 - 17 Apr 2025

Abstract

Achieving a stable color output across wide temperature ranges in automotive LED applications is challenging, especially when using cost-sensitive chips with limited computational resources. This study proposes an improved temperature model that integrates Fourier heat conduction and thermal resistance concepts to more accurately [...] Read more.

Achieving a stable color output across wide temperature ranges in automotive LED applications is challenging, especially when using cost-sensitive chips with limited computational resources. This study proposes an improved temperature model that integrates Fourier heat conduction and thermal resistance concepts to more accurately capture self-heating and power dissipation effects. To accommodate the constraints of low-cost automotive-grade microcontrollers (MCUs), the associated optical algorithm is converted from floating-point to a 16.16 fixed-point format, reducing both memory usage and computational overhead. Experimental results conducted from −40 °C to 120 °C show that the improved model predicts LED temperatures within 5 °C of measured values, reducing errors by up to 30% compared to conventional PN-junction-based methods. Furthermore, by comparing the chromaticity points generated under the new and traditional models—and implementing an additional three-duty-cycle offset at 1% brightness—the improved approach reduces chromaticity drift by approximately 0.0052 in the CIE 1931 xy color space. These findings confirm the superior stability and accuracy of the new model for both thermal management and chromaticity compensation, offering a cost-effective solution for automotive LED systems requiring precise color control under constrained MCU resources. Full article

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27 pages, 9692 KiB

Open AccessArticle

Advanced Battery Management for Lithium-Ion EVs: Integrating Extended Kalman Filter and Modified Multi-Layer Perceptron for Enhanced State Monitoring

by Mohana Devi Sureshbabu and Veeramani Bagyaveereswaran

World Electr. Veh. J. 2025, 16(4), 234; https://doi.org/10.3390/wevj16040234 - 15 Apr 2025

Abstract

An efficient Battery Management System (BMS) specifically for Electric Vehicles is crucial for improving battery run time performance. A primary function of an effective BMS is accurately determining the State of Charge (SOC) and State of Health (SOH) of lithium-ion batteries in Electric [...] Read more.

An efficient Battery Management System (BMS) specifically for Electric Vehicles is crucial for improving battery run time performance. A primary function of an effective BMS is accurately determining the State of Charge (SOC) and State of Health (SOH) of lithium-ion batteries in Electric Vehicles (EVs). However, many existing studies have concentrated on examining sensor malfunctions in batteries to avert problems such as overcharging and overheating and are lacking in terms of effective handling of non-linear behaviors. To overcome these limitations, the proposed work introduces a hybrid approach for estimating the state of lithium-ion batteries. It employs an Extended Kalman Filter (EKF) for SOC estimation and modified Multi-Layer Perceptron (MLP) for SOH estimation in batteries. It can handle the non-linear characteristics often exhibited by sensor readings and fault behaviors. The EKF algorithm involves initialization, prediction, and correction phases, allowing for accurate state estimation based on measurements. For SOH estimation, the NASA battery dataset, which includes various battery conditions across different temperatures, is analyzed using a modified MLP regression process. This modified MLP employs a gradient shift bias adjustment technique to minimize error rates by refining the gradients and biases introduced during the training process. It also effectively adjusts the model’s weights for better SOH estimation. The results demonstrate improved accuracy in battery performance, as indicated by lower RMSE, MSE, MAE and R² values. Furthermore, the study highlights the effectiveness of this hybrid method for significant battery management at different temperatures, which emphasizes the potential of this model, with enhanced state estimation for EV applications. Full article

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25 pages, 1689 KiB

Open AccessArticle

Multidimensional Analysis of Technological Innovation Efficiency in New Energy Vehicles: Industrial Chain Heterogeneity and Key Drivers

by Yawei Xue, Yuchen Lu and Zhongshuai Wang

World Electr. Veh. J. 2025, 16(4), 233; https://doi.org/10.3390/wevj16040233 - 15 Apr 2025

Abstract

As the world accelerates efforts to combat climate change and transition toward a green, low-carbon economy, the new energy vehicle (NEV) industry has become a key driver of carbon reduction. Its ability to innovate efficiently is critical to long-term sustainable development. This study [...] Read more.

As the world accelerates efforts to combat climate change and transition toward a green, low-carbon economy, the new energy vehicle (NEV) industry has become a key driver of carbon reduction. Its ability to innovate efficiently is critical to long-term sustainable development. This study builds on the innovation value chain theory and introduces an evaluation framework that accounts for undesirable outputs such as energy consumption and pollutant emissions. Using a super-efficiency network SBM–Malmquist model and Tobit regression, we analyze the technological innovation efficiency of 272 A-share listed NEV enterprises in China from 2016 to 2023. Expanding beyond traditional overall assessments, we examine efficiency at different stages of the industry chain and find that: (a) overall technological innovation efficiency has declined, mainly due to weak pure technical efficiency, underscoring the need for better R&D management and resource allocation; (b) efficiency varies across the industry chain, with midstream firms performing better than those upstream and downstream, reflecting differences in technological accumulation and market conditions; (c) R&D tax deductions and market competition significantly boost innovation efficiency by creating pressure-driven incentives, while mismatched labor skills, the “welfare dependence” effect of tax incentives and financing constraints hinder progress. By introducing a two-stage innovation efficiency evaluation framework, this study not only pinpoints where efficiency losses occur along the industry chain but also provides empirical insights to guide targeted policy decisions, offering valuable implications for the sustainable growth of the global NEV industry. Full article

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17 pages, 10236 KiB

Open AccessArticle

Research on Active Suspension Control Based on Vehicle Speed Control Under Transient Pavement Excitation

by Xiangpeng Meng, Linghui Kong, Renkai Ding, Wei Liu and Ruochen Wang

World Electr. Veh. J. 2025, 16(4), 232; https://doi.org/10.3390/wevj16040232 - 15 Apr 2025

Abstract

Transient road excitation can cause high-frequency impacts to the vehicle, leading to deterioration of smoothness and operational stability, and seriously impairing system life and performance. To address this problem, the vehicle model and the road model containing transient road excitation is first established, [...] Read more.

Transient road excitation can cause high-frequency impacts to the vehicle, leading to deterioration of smoothness and operational stability, and seriously impairing system life and performance. To address this problem, the vehicle model and the road model containing transient road excitation is first established, and the impact mechanism of transient excitation is simulated and analyzed, from which the fuzzy control of vehicle speed and the model predictive control of the suspension system are designed respectively. The suspension control method based on the speed control is further proposed, which sets the strategy to use fuzzy control to regulate the vehicle speed after the on-board sensors identify the bumpy road excitation, and at the same time to implement the model predictive control for the active suspension system, and dynamically adjusts the control weight parameters to be compatible with the vehicle speed control. The simulation results show that compared with the single suspension control, the control strategy improves in all stages, and the root mean square values of body acceleration, pitch angle acceleration, and front and rear tire dynamic loads are reduced by 9.52%, 4.55%, 29.5% and 17.8%, respectively, and the peaks are reduced by 23.8%, 39.9%, 44.7% and 33.2%, respectively, which further enhances the safety and ride comfort of the vehicle. Finally, the effectiveness and correctness of the strategy are verified by a hardware-in-the-loop test. Full article

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11 pages, 2823 KiB

Open AccessArticle

Model Predictive Control Using an Artificial Neural Network for Fast-Charging Lithium-Ion Batteries

by Joris Jaguemont, Ali Darwiche and Fanny Bardé

World Electr. Veh. J. 2025, 16(4), 231; https://doi.org/10.3390/wevj16040231 - 15 Apr 2025

Abstract

The increasing computational complexity of Model Predictive Control (MPC) in battery systems limits its practical adoption, despite its potential for optimizing performance under dynamic operating conditions. To address this challenge, this study introduces an Artificial Neural Network-based MPC framework (MPCANN) tailored for VTC6 [...] Read more.

The increasing computational complexity of Model Predictive Control (MPC) in battery systems limits its practical adoption, despite its potential for optimizing performance under dynamic operating conditions. To address this challenge, this study introduces an Artificial Neural Network-based MPC framework (MPCANN) tailored for VTC6 3Ah lithium-ion cells, aiming to reduce computational burdens while retaining predictive accuracy. The framework synergizes MPC’s predictive capabilities with the daptive learning of Artificial Neural Network (ANN) by training the ANN offline using MPC-derived input–output data. Validation against prior MPC results demonstrates MPCANN’s ability to replicate MPC behavior across temperatures, achieving strong alignment in current and temperature predictions. While state of charge (SoC) estimation accuracy requires refinement at elevated temperatures, the framework reduces computation time by 94% compared to traditional MPC, highlighting its efficiency. These results underscore MPCANN’s potential to enable real-time implementation of advanced battery control strategies, offering a pathway to balance computational efficiency with performance in adaptive energy systems. 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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26 pages, 8584 KiB

Open AccessArticle

Congestion Relief and Economic Optimization of Integrated Power Stations with Charging and Swapping Functions

by Zhaoyi Wang, Xiaohong Zhang, Qingyuan Yan, Xiaokang Zhang and Yanxue Li

World Electr. Veh. J. 2025, 16(4), 230; https://doi.org/10.3390/wevj16040230 - 14 Apr 2025

Abstract

To effectively address the challenges of imbalanced equipment utilization, frequent congestion, and poor economic benefits faced by charging and swapping stations (ICSSs), this paper innovatively proposes a comprehensive scheduling strategy that combines user behavior regulation and battery management. In terms of user regulation, [...] Read more.

To effectively address the challenges of imbalanced equipment utilization, frequent congestion, and poor economic benefits faced by charging and swapping stations (ICSSs), this paper innovatively proposes a comprehensive scheduling strategy that combines user behavior regulation and battery management. In terms of user regulation, an intention-reshaping model for changing user cognition is proposed to equalize the use of charging and swapping (CAS) equipment, easing ICSS congestion. Moreover, an off-station scheduling model for electric vehicles (EVs) is developed to enhance overall ICSS revenue. Within the battery management terms, the suggested inventory battery threshold adjustment method and charging strategy by charging time segmentation are employed to ensure consistent inventory battery supply and cost-effective battery charging. Finally, a two-stage scheduling strategy of in-station and off-station scheduling is suggested for the ICSS, and an improved northern goshawk optimization algorithm (INGO) is used to solve it. The results showed that this strategy reduced the overall congestion of ICSSs by 34% and increased the average annual net revenue by 64%. The goal of alleviating congestion and improving the economic efficiency of ICSSs has been achieved. Full article

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22 pages, 664 KiB

Open AccessArticle

Beyond the Battery: The Impact of Cultural Factor on Electric Vehicle Consumers’ Service Quality Expectations in Dealerships

by Yang Zhou, Wanwen Dai and Miao Xiao

World Electr. Veh. J. 2025, 16(4), 229; https://doi.org/10.3390/wevj16040229 - 14 Apr 2025

Abstract

This research investigates the impact of cultural dimensions on service quality expectations in electric vehicle (EV) dealerships. Grounded in Hofstede’s cultural theory and employing a data-driven approach, the study utilizes a series of robust analytical techniques, including the SVM algorithm, factor analysis, and [...] Read more.

This research investigates the impact of cultural dimensions on service quality expectations in electric vehicle (EV) dealerships. Grounded in Hofstede’s cultural theory and employing a data-driven approach, the study utilizes a series of robust analytical techniques, including the SVM algorithm, factor analysis, and ANOVA. Through a comprehensive analysis of EV customers’ expectations for expertise, empathy, and responsiveness, the findings reveal that cultural dimensions significantly shape service quality expectations, regardless of the service provider’s gender. Notably, consumers with a stronger masculine orientation have lower expectations for expertise but higher expectations for empathy than those with a stronger feminine orientation. These findings challenge the traditional emphasis on gender as a key factor in service quality expectations and underscore the need to incorporate cultural values in service strategy design and quality improvement in the EV industry. Full article

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20 pages, 4600 KiB

Open AccessArticle

Comparative Analysis of MacPherson and Double Wishbone Suspensions for an Electric Off-Road Vehicle Retrofit

by Pablo Tapia, Eugenio Tramacere, David Sebastian Puma-Benavides, Renato Galluzzi, Victor Danilo Zambrano-Leon, Juan Carlos Jima-Matailo and Edilberto Antonio Llanes-Cedeño

World Electr. Veh. J. 2025, 16(4), 228; https://doi.org/10.3390/wevj16040228 - 14 Apr 2025

Abstract

The suspension system in plays a pivotal role, especially in off-road vehicles, in ensuring optimal comfort, road holding and ride safety. This study explores the transition from a MacPherson strut to a double wishbone suspension system, emphasizing its impact on relevant suspension features, [...] Read more.

The suspension system in plays a pivotal role, especially in off-road vehicles, in ensuring optimal comfort, road holding and ride safety. This study explores the transition from a MacPherson strut to a double wishbone suspension system, emphasizing its impact on relevant suspension features, such as camber and caster angles, motion ratio and vertical dynamics. Through this study, an off-road vehicle was retrofitted with the proposed suspension architecture and tested both numerically and experimentally. Test results reproduce simulation outcomes, thus confirming the effectiveness of the redesigned suspension system for the target vehicle, especially for demanding off-road applications. Full article

(This article belongs to the Special Issue Vehicle Control and Drive Systems for Electric Vehicles)

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24 pages, 4171 KiB

Open AccessArticle

Energy Management of a 1 MW Photovoltaic Power-to-Electricity and Power-to-Gas for Green Hydrogen Storage Station

by Dalila Hidouri, Ines Ben Omrane, Kassmi Khalil and Adnen Cherif

World Electr. Veh. J. 2025, 16(4), 227; https://doi.org/10.3390/wevj16040227 - 11 Apr 2025

Abstract

Green hydrogen is increasingly recognized as a sustainable energy vector, offering significant potential for the industrial sector, buildings, and sustainable transport. As countries work to establish infrastructure for hydrogen production, transport, and energy storage, they face several challenges, including high costs, infrastructure complexity, [...] Read more.

Green hydrogen is increasingly recognized as a sustainable energy vector, offering significant potential for the industrial sector, buildings, and sustainable transport. As countries work to establish infrastructure for hydrogen production, transport, and energy storage, they face several challenges, including high costs, infrastructure complexity, security concerns, maintenance requirements, and the need for public acceptance. To explore these challenges and their environmental impact, this study proposes a hybrid sustainable infrastructure that integrates photovoltaic solar energy for the production and storage of green hydrogen, with PEMFC fuel cells and a hybrid Power-to-Electricity (PtE) and Power-to-Gas (PtG) configurations. The proposed system architecture is governed by an innovative energy optimization and management (EMS) algorithm, allowing forecasting, control, and supervision of various PV–hydrogen–Grid transfer scenarios. Additionally, comprehensive daily and seasonal simulations were performed to evaluate power sharing, energy transfer, hydrogen production, and storage capabilities. Dynamic performance assessments were conducted under different conditions of solar radiation, temperature, and load, demonstrating the system’s adaptability. The results indicate an overall efficiency of 62%, with greenhouse gas emissions reduced to 1% and a daily production of hydrogen of around 250 kg equivalent to 8350 KWh/day. Full article

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17 pages, 492 KiB

Open AccessArticle

Blockchain-Based Secure Firmware Updates for Electric Vehicle Charging Stations in Web of Things Environments

by Amjad Aldweesh

World Electr. Veh. J. 2025, 16(4), 226; https://doi.org/10.3390/wevj16040226 - 10 Apr 2025

Abstract

The integration of electric vehicles into modern mobility ecosystems relies heavily on reliable charging station infrastructures that support real-time communications and data-driven functionalities. Existing solutions often face security vulnerabilities in their firmware update mechanisms, compromising safety, user trust, and the broader deployment of [...] Read more.

The integration of electric vehicles into modern mobility ecosystems relies heavily on reliable charging station infrastructures that support real-time communications and data-driven functionalities. Existing solutions often face security vulnerabilities in their firmware update mechanisms, compromising safety, user trust, and the broader deployment of these stations in emerging digital and connected environments. This paper aims to address these gaps by proposing a blockchain-based framework designed to provide secure, tamper-proof firmware updates for charging stations in a Web of Things environment. The approach uses decentralized ledger technologies to validate firmware integrity, authenticate update sources, and mitigate the risk of malicious or fraudulent content. In a comprehensive experimental setup, the proposed method demonstrates enhanced resilience against unauthorized firmware modifications and improved traceability of update transactions through immutable records. Results highlight a reduction in firmware compromise events, as well as improved detection and notification efficiencies in real-time networked systems. These findings suggest that integrating blockchain technology into firmware update workflows strengthens security in electric vehicle charging infrastructures. Consequently, the adoption of decentralized verification approaches can drive broader trust in connected mobility services, supporting safer and more efficient charging station networks while fostering future innovation in sustainable transport. Full article

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

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