World Electric Vehicle Journal

21 pages, 2121 KB

Open AccessArticle

A Particle Swarm Optimized Multi-Model Framework for Remaining Useful Life Prediction of Lithium-Ion Batteries Using Domain-Driven Feature Engineering

by Farrukh Hafeez, Zeeshan Ahmad Arfeen, Gohar Ali, Muhammad I. Masud, Muhammad Hamid, Mohammed Aman, Muhammad Salman Saeed and Touqeer Ahmed Jumani

World Electr. Veh. J. 2025, 16(11), 639; https://doi.org/10.3390/wevj16110639 - 20 Nov 2025

Viewed by 409

Abstract

With respect to battery management and safe operation and maintenance scheduling of electric vehicles (EVs), it is very important to predict the remaining useful life (RUL) of lithium-ion batteries (LIBs). Accurate prediction of RUL can bring secure working conditions, avert internal and external [...] Read more.

With respect to battery management and safe operation and maintenance scheduling of electric vehicles (EVs), it is very important to predict the remaining useful life (RUL) of lithium-ion batteries (LIBs). Accurate prediction of RUL can bring secure working conditions, avert internal and external failure, and, last, avoid any undesirable consequences. However, achieving accurate prediction of RUL is complicated for EV applications due to various reasons such as the complex operational characteristics, dynamic changes in the model parameters during the aging process, extraction of battery parameters, data preparation, and hyper-parameter tuning of the predictive model. This research proposes a novel approach that integrates Particle Swarm Optimization (PSO) with a multi-model technique for RUL prediction. The framework integrates many machine learning (ML) models and deep learning (DL) models. Combining domain knowledge, advanced optimization techniques, and learning models to make high-accuracy RUL predictions reduces maintenance costs and improves battery management systems. This study uses domain-driven feature engineering to extract battery-specific indicators, including voltage drops, charging time, and temperature fluctuations, to increase model accuracy. Among the evaluated models, LSTM demonstrates superior performance, achieving a mean absolute error (MAE) of 0.34, a root mean square error (RMSE) of 0.76, and an R² of 0.93, providing the best results in RUL prediction. The proposed research uniquely integrates PSO-based optimization with domain-driven feature engineering across multiple machine learning and deep learning models, demonstrating a unified and novel approach that significantly improves the prediction accuracy of RUL in LIBs. Full article

(This article belongs to the Special Issue Material Synthesis, Manufacturing and Electrochemical Modelling for Lithium-Ion Batteries in Electric Vehicle)

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30 pages, 1386 KB

Open AccessReview

AI-Enhanced Circular Economy and Sustainability in the Indian Electric Two-Wheeler Industry: A Review

by Dilip K. Achal and Gangoor S. Vijaya

World Electr. Veh. J. 2025, 16(11), 638; https://doi.org/10.3390/wevj16110638 - 20 Nov 2025

Viewed by 686

Abstract

Drastically cutting carbon footprints to reduce global warming is now a universal norm, in keeping with the United Nations’ Convention on Climate Change 2015. The global proliferation of electric vehicles (EVs) is, hence, appropriate. India (Niti Aayog) has given a determined call for [...] Read more.

Drastically cutting carbon footprints to reduce global warming is now a universal norm, in keeping with the United Nations’ Convention on Climate Change 2015. The global proliferation of electric vehicles (EVs) is, hence, appropriate. India (Niti Aayog) has given a determined call for ‘only EV’ on road by 2030, a transition which will be led by electric two-wheelers (E2Ws) with 80% of the market. The Indian E2W (IE2W) industry needs to adopt green manufacturing and sustainable supply chain management (SSCM), addressing environmental, economic, and social issues. The battery supply chain (an environmental gray area) must also follow circularity and sustainability principles. With artificial intelligence (AI) having come into play in industry and manufacturing, it will undoubtedly influence the circular economy (CE) and sustainability concerns in the IE2W space. This review aims to critically study the available literature on AI’s contribution to CE and sustainability in the IE2W sector. The study has revealed a lack of sufficient research, specifically in the IE2W sector, including AI’s effect on waste management, government policies, etc. For the government, the study recommends a higher outlay for R&D, bridging skill gaps, and strengthening regulatory frameworks and ethics; and, for the IE2W industry, this study recommends increased focus on CE, public awareness, compliance with ethical norms for AI deployment, and prioritizing a fleet-first model. The study is expected to enhance value for the IE2W sector, the government, the public, and the environment. Full article

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25 pages, 3399 KB

Open AccessArticle

Enhancing Intelligent Transportation Safety with Explainable AI: A Framework for Uncovering Crash Severity Factors at Highway–Rail Grade Crossings

by Dongming Wang, Qin He, Jinwen Peng and Gen Li

World Electr. Veh. J. 2025, 16(11), 637; https://doi.org/10.3390/wevj16110637 - 20 Nov 2025

Viewed by 399

Abstract

Improving road safety is a fundamental goal of Intelligent Transportation Systems (ITS). However, the complex interplay of factors in accident-prone scenarios, such as highway–rail grade crossings, poses significant challenges for conventional analysis. This paper addresses this gap by proposing and validating a novel [...] Read more.

Improving road safety is a fundamental goal of Intelligent Transportation Systems (ITS). However, the complex interplay of factors in accident-prone scenarios, such as highway–rail grade crossings, poses significant challenges for conventional analysis. This paper addresses this gap by proposing and validating a novel explainable artificial intelligence (XAI) framework, which integrates Extreme Gradient Boosting (XGBoost) with Shapley Additive Explanations (SHAP), to enhance safety analysis within ITS. Applying this framework to a comprehensive dataset of highway–rail grade crossing collisions, our research moves beyond simple correlation to uncover the nonlinear relationships and interaction effects governing injury severity. The model identifies speed-related factors, driver age, and traffic exposure as primary predictors. More critically, the SHAP analysis quantitatively reveals significant synergistic risks, demonstrating that the combination of non-dry road surfaces and poor lighting conditions drastically amplifies injury severity. These findings offer granular insights for the “smart management” and development of “resilient infrastructures,” enabling targeted interventions like adaptive lighting systems and dynamic risk warnings. This study not only provides critical safety solutions for grade crossings but also showcases the power of XAI as a robust tool for “advanced analysis” across various complex transportation safety problems, ultimately contributing to the creation of safer and more reliable ITS. Full article

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

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22 pages, 3660 KB

Open AccessArticle

Enabling Grid Services with Bidirectional EV Chargers: A Comparative Analysis of CCS2 and CHAdeMO Response Dynamics

by Kristoffer Laust Pedersen, Rasmus Meier Knudsen, Mattia Marinelli, Mattia Secchi and Kristian Sevdari

World Electr. Veh. J. 2025, 16(11), 636; https://doi.org/10.3390/wevj16110636 - 20 Nov 2025

Viewed by 553

Abstract

Bidirectional electric vehicle (EV) charging represents an opportunity to leverage EVs as flexible energy assets within the power system. By enabling controlled power flow in both directions, bidirectional charging unlocks a wide range of grid services, thereby enhancing grid stability as the energy [...] Read more.

Bidirectional electric vehicle (EV) charging represents an opportunity to leverage EVs as flexible energy assets within the power system. By enabling controlled power flow in both directions, bidirectional charging unlocks a wide range of grid services, thereby enhancing grid stability as the energy sector decarbonizes. This paper presents a comprehensive experimental evaluation of bidirectional charging systems (EVCS), focusing on response dynamics and controllability delays critical for grid services. A real ISO 15118–20–enabled EV and an EV emulator were used to conduct tests across configurations, utilizing the Watt & Well 22 kW bidirectional charging bay. The study compares CCS2 and CHAdeMO protocols under varying configuration conditions. Results show that modern chargers achieve sub-second responsiveness, with local communication delays typically below 0.4 s and ramping times around 0.5 s. However, power flow reversals introduce an additional delay of approximately 1 s. These updated controllability metrics are essential for validating bidirectional charging in time-critical applications such as primary frequency regulation. The findings highlight the influence of voltage level and modular configuration on dynamic performance, underscoring the need to integrate external control path delays for full-stack validation. This work provides a foundation for modeling and deploying bidirectional EVCS in fast-response grid services. Full article

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

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16 pages, 511 KB

Open AccessArticle

Analysis of Technological Innovation Efficiency in Listed New Energy Vehicle Enterprises Under the Carbon Neutrality Framework Based on Two-Stage Dynamic Network DEA and a GRA Model

by Zhihua Ruan and Zhikun Liu

World Electr. Veh. J. 2025, 16(11), 635; https://doi.org/10.3390/wevj16110635 - 20 Nov 2025

Viewed by 357

Abstract

Technological innovation and the efficiency of resource allocation in Chinese new energy vehicle enterprises represent critical factors influencing the sustainable development of the industry. By applying a two-stage dynamic network DEA model to analyze the comprehensive and stage-specific technological innovation efficiency of 13 [...] Read more.

Technological innovation and the efficiency of resource allocation in Chinese new energy vehicle enterprises represent critical factors influencing the sustainable development of the industry. By applying a two-stage dynamic network DEA model to analyze the comprehensive and stage-specific technological innovation efficiency of 13 A-share-listed new energy vehicle enterprises between 2017 and 2024, this study reveals that both overall and phase-specific innovation efficiencies remain below optimal levels. Moreover, the average technological R&D efficiency across these firms is found to be lower than their average achievement transformation efficiency, highlighting the urgent need to improve innovation performance in this sector. Grey relational analysis of influencing factors identifies six key determinants of technological innovation efficiency: the shareholding ratio of the largest shareholder, R&D investment intensity, the proportion of employees holding bachelor’s degrees or higher, management capability, return on equity, and total asset turnover. In comparison, government subsidies and total assets exhibit relatively limited influence on technological innovation efficiency. Full article

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23 pages, 4771 KB

Open AccessArticle

Validating DVS Application in Autonomous Driving with Various AEB Scenarios in CARLA Simulator

by Jingxiang Feng, Peiran Zhao, Jessada Konpang, Adisorn Sirikham, Haoran Zheng, Phuri Kalnaowakul and Jia Wang

World Electr. Veh. J. 2025, 16(11), 634; https://doi.org/10.3390/wevj16110634 - 20 Nov 2025

Viewed by 410

Abstract

Predicting potential collisions with leading vehicles is a fundamental capability of autonomous and assisted driving systems. In particular, automatic emergency braking (AEB) demands reaction times on the order of microseconds. A key limitation of existing approaches lies in their update rate, which is [...] Read more.

Predicting potential collisions with leading vehicles is a fundamental capability of autonomous and assisted driving systems. In particular, automatic emergency braking (AEB) demands reaction times on the order of microseconds. A key limitation of existing approaches lies in their update rate, which is constrained by the sampling speed of conventional sensors. Event-based Dynamic Vision Sensors (DVSs), with their microsecond temporal resolution and high dynamic range, offer a promising alternative to frame-based cameras in challenging driving environments. In this work, we investigate the integration of DVS into autonomous driving pipelines, focusing specifically on AEB scenarios. Building on our earlier work, where a YOLO-based detection model was trained on real-world DVS data, we extend the approach to CARLA’s simulated DVS environment. We publish a CARLA-compatible 2-channel DVS dataset aligned with our detection model, bridging the gap between real-world recordings and simulation. Through a series of simulated AEB scenarios, we demonstrate how DVS enables earlier and more reliable detection compared to RGB cameras, resulting in improved braking performance. Full article

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28 pages, 3911 KB

Open AccessReview

Traction Synchronous Motors with Rotor Field Winding: A Literature Review

by Vladimir Prakht, Vladimir Dmitrievskii, Vadim Kazakbaev, Eduard Valeev and Victor Goman

World Electr. Veh. J. 2025, 16(11), 633; https://doi.org/10.3390/wevj16110633 - 20 Nov 2025

Viewed by 556

Abstract

Synchronous motors with a field winding in the rotor, known as wound-rotor synchronous motors (WRSMs) or electrically excited synchronous motors (EESMs), are claimed to be a good alternative to induction motors and even permanent-magnet synchronous motors (PMSMs) in electric traction applications. WRSMs do [...] Read more.

Synchronous motors with a field winding in the rotor, known as wound-rotor synchronous motors (WRSMs) or electrically excited synchronous motors (EESMs), are claimed to be a good alternative to induction motors and even permanent-magnet synchronous motors (PMSMs) in electric traction applications. WRSMs do not require expensive rare-earth magnets and potentially have high power and torque density, and lower inverter power and cost, especially in applications demanding a wide constant-power speed range. Designing WRSMs for electric traction imposes some challenges and requires careful analysis. This paper provides an overview of commercial WRSMs for ground electric transport over the past 40 years, a comparison of WRSMs with other types of electric motors suitable for electric traction, and an overview of optimization methods and brushless excitation technologies for such machines. The goals of this paper are to present and discuss design approaches for traction WRSMs, to benchmark WRSMs against other motor types used in ground electric transport, and to highlight the most promising WRSM topologies and design techniques. Full article

(This article belongs to the Special Issue New Journey of Energy and Electric Vehicle Revolutions-Infinities Possibilities in the Science World: In Honor of Prof. Dr. C.C. Chan’s 90th Birthday)

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27 pages, 5940 KB

Open AccessArticle

Manufacturability-Constrained Multi-Objective Optimization of an EV Battery Pack Enclosure for Side-Pole Impact

by Desheng Zhang, Zhenxin Sun, Han Zhang and Jieguo Liao

World Electr. Veh. J. 2025, 16(11), 632; https://doi.org/10.3390/wevj16110632 - 19 Nov 2025

Viewed by 280

Abstract

This work minimizes battery pack enclosure mass (kg) and peak deformation (mm) under a side-pole impact condition and validates the results by finite-element reruns complemented by coupon-level material tests. A 64-run optimal Latin hypercube dataset trained ARD Matérn-5/2 Gaussian-process surrogates, and NSGA-II performed [...] Read more.

This work minimizes battery pack enclosure mass (kg) and peak deformation (mm) under a side-pole impact condition and validates the results by finite-element reruns complemented by coupon-level material tests. A 64-run optimal Latin hypercube dataset trained ARD Matérn-5/2 Gaussian-process surrogates, and NSGA-II performed a multi-objective search on a manufacturability grid (

Δ_{t}

= 0.5 mm). Decision-making processes used knee-region filtering and TOPSIS in the normalized objective space with robustness checks (uncertainty inflation, weight perturbation, and cross-kernel audit). The representative optimum reduced mass from 149.40 kg to 115.20 kg (−22.89%) while keeping peak deformation essentially unchanged (66.17 → 66.25 mm) in independent reruns. To examine material dependence, an orthotropic CFRP cross-check was performed by substituting the upper cover and side walls: the iso-thickness mapping yields 90.40 kg with 68.67 mm (+3.65% vs. aluminum

x^{⋆}

), whereas a constrained iso-mass setting (

H_{1}

= 7.0 mm,

H_{2}

= 7.0 mm) gives 111.70 kg with 80.85 mm (+22.04%). The observed trends are consistent with the laminate’s lower transverse-shear moduli and shear-sensitive load paths; damage evolution and lay-up optimization are outside the present scope. The workflow provides a reproducible route to balance lightweighting and deformation control for battery pack enclosures. Full article

(This article belongs to the Section Storage Systems)

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38 pages, 981 KB

Open AccessArticle

Optimal Incentive Mechanism: Balancing the Complex Risk Preferences of Shared Battery Swapping Station Enterprises Under Dual Asymmetric Information

by Lei He, Yanfei Lan, Mingmao Hu and Aihong Gong

World Electr. Veh. J. 2025, 16(11), 631; https://doi.org/10.3390/wevj16110631 - 19 Nov 2025

Viewed by 258

Abstract

This study investigates how the government can design optimal incentive mechanisms for Shared Battery Swapping Station (SBSS) construction enterprises under the conditions of dual information asymmetry (effort and operational efficiency) and heterogeneous corporate risk preferences. Employing game theory and principal–agent theory, this research [...] Read more.

This study investigates how the government can design optimal incentive mechanisms for Shared Battery Swapping Station (SBSS) construction enterprises under the conditions of dual information asymmetry (effort and operational efficiency) and heterogeneous corporate risk preferences. Employing game theory and principal–agent theory, this research constructs a mathematical model between the government and the enterprises to derive the optimal incentive coefficient and fixed subsidy, with theoretical results verified through numerical simulations. The findings reveal that risk-averse enterprises require higher incentives and subsidies. Compared to a single asymmetry scenario, the incentive coefficient is lower under dual information asymmetry. The government’s utility increases with an enterprise’s risk aversion beyond a critical threshold, while the enterprise’s utility remains at its reservation level. Our findings reveal a critical trade-off: while risk-averse enterprises require higher fixed subsidies and incentive coefficients, the presence of dual information asymmetry forces the government to paradoxically lower the incentive coefficient to prevent information rent extraction by less efficient firms. Furthermore, we identify a critical threshold where it becomes more beneficial for the government to contract with highly risk-averse firms due to their predictable behavior, even as the enterprise’s utility is held at its reservation level. These results provide a quantitative basis for policymakers to design a ’menu of contracts’—offering stable, high-subsidy options for risk-averse players and performance-based incentives for risk-neutral ones—rather than a one-size-fits-all policy. Full article

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6 pages, 170 KB

Open AccessEditorial

Integration of Innovative Paths for Permanent Magnet Motors in Electric Vehicles: Intelligent Control, Proactive Diagnosis, and Collaborative Design

by Yan Wang, Ming Yao and Shuchao Cao

World Electr. Veh. J. 2025, 16(11), 630; https://doi.org/10.3390/wevj16110630 - 19 Nov 2025

Viewed by 423

Abstract

Today, in the face of the urgent need to decarbonize global transportation, the development of electric vehicles (EVs) has become one of the core strategies to address energy crises and environmental challenges [...] Full article

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

18 pages, 3026 KB

Open AccessArticle

Enhanced Sliding-Mode Observer for Mechanical Parameter Estimation and Load Compensation in PMSM Drives

by Chuanyu Sun, Zhihao Wang, Chunmei Wang, Xingling Xiao, Shanshan Gong and Junjie Wan

World Electr. Veh. J. 2025, 16(11), 629; https://doi.org/10.3390/wevj16110629 - 18 Nov 2025

Viewed by 398

Abstract

This paper presents an improved sliding-mode observer (SMO) for estimating mechanical parameters and compensating load torque in permanent magnet synchronous motor (PMSM) drives. Traditional SMOs have limited robustness when the motor model is inaccurate. To solve this, an enhanced sliding-mode observer (ESMO) is [...] Read more.

This paper presents an improved sliding-mode observer (SMO) for estimating mechanical parameters and compensating load torque in permanent magnet synchronous motor (PMSM) drives. Traditional SMOs have limited robustness when the motor model is inaccurate. To solve this, an enhanced sliding-mode observer (ESMO) is proposed. It can estimate both the total inertia and the load torque at the same time. The method is verified using Lyapunov stability analysis and convergence time calculation. Experimental results show that, when combined with a single-vector Model Predictive Current Control (MPCC), the proposed ESMO achieves zero overshoot during no-load startup and keeps the steady-state error below 0.1% under load changes. It also reduces q-axis current ripple and improves harmonic suppression. This control method is suitable for applications that require high precision and strong robustness, such as robots, electric vehicles, and smart manufacturing. Full article

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

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26 pages, 4898 KB

Open AccessArticle

Expanding Electric and Vehicle-Based Solar Transit Options with Breakthrough Vehicular Efficiencies

by Adam B. Suppes and Galen Suppes

World Electr. Veh. J. 2025, 16(11), 628; https://doi.org/10.3390/wevj16110628 - 18 Nov 2025

Viewed by 321

Abstract

Traditional approaches to overcoming energy loss from resistances of vehicular transit velocities have focused primarily on reducing aerodynamic drag through streamlining air flow. These approaches have overlooked significant reductions in resistance on highways by reducing rolling losses and the aerodynamic drag associated with [...] Read more.

Traditional approaches to overcoming energy loss from resistances of vehicular transit velocities have focused primarily on reducing aerodynamic drag through streamlining air flow. These approaches have overlooked significant reductions in resistance on highways by reducing rolling losses and the aerodynamic drag associated with boundary layer separation and leading-edge stagnation regions. Ground effect vehicles are able to make significant strides towards reducing these two resistances. These vehicles can approach an 80% reduction in resistance compared to conventional frame streamlining alone. These substantial reductions to resistance enable a more effective and broader range of electric vehicles, including electric trucks and railcars. Lower resistance enables higher speeds at the same power consumption. Examples of digital prototype performances include up to 50% and 30% reductions in resistance through mitigating rolling/drivetrain and boundary layer separation losses, respectively. Digital prototypes are able to reach a lift-to-drag efficiency of 25 while maintaining a 0.2 aspect ratio. A cascade of additional advantages arises from aerodynamic lift-enabling rubber tires on steel rails for multimodal and widespread service. This paper details the mechanisms of how to achieve substantial reductions in energy consumption and enable transit transformations. The technology enables open-ended evolution with far greater possibilities than current transit options. The technological evolution includes electric automobiles, delivery trucks, semi-trucks, and railcars using batteries and solar sheets with significant competitive advantages over fossil fuels. Full article

(This article belongs to the Special Issue The Contribution of Electric Vehicles to Realization of Dual Carbon Goal)

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16 pages, 3856 KB

Open AccessArticle

Electric Bus Depot Charging in South Africa: Lessons for Grid Integration

by Praise George-Kayode, Halloran Stratford and Marthinus Johannes Booysen

World Electr. Veh. J. 2025, 16(11), 627; https://doi.org/10.3390/wevj16110627 - 18 Nov 2025

Viewed by 368

Abstract

Uncontrolled charging of large electric bus fleets can strain constrained power grids, such as South Africa’s. This study develops and evaluates a demand-oriented charging strategy for Golden Arrow Bus Services using a Mixed-Integer Linear Programming (MILP) model calibrated with real operating data. The [...] Read more.

Uncontrolled charging of large electric bus fleets can strain constrained power grids, such as South Africa’s. This study develops and evaluates a demand-oriented charging strategy for Golden Arrow Bus Services using a Mixed-Integer Linear Programming (MILP) model calibrated with real operating data. The model schedules fleet charging over an off-peak window to minimise the highest total demand charge (Notified Maximum Demand, NMD) while respecting arrival state of charge (SOC), Time-of-Use (ToU) tariffs, and ensuring all vehicles are fully charged before dispatch. Compared to the unmanaged baseline, the optimised schedules reduce the peak demand charge by 17%, keeping total depot demand below 1 MW and ensuring full fleet readiness. The strategy also eliminates all energy consumption during expensive peak-tariff windows in both winter and summer. Further analysis shows that raising the minimum arrival SOC reduces the required optimum per-bus demand approximately linearly (≈1.5 kW per +5% SOC), whereas widening the SOC arrival range increases demand variability. This MILP framework demonstrates that exploiting SOC diversity and modest charge capacity capping can significantly lower peak demand and operational costs, offering a validated model for depots in other capacity-constrained power systems. Full article

(This article belongs to the Special Issue Smart Charging Strategies for Plug-In Electric Vehicles)

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18 pages, 2759 KB

Open AccessArticle

Research on Real-Time Operational Risk Prediction for New Energy Vehicles Based on Multi-Source Feature Fusion

by Yilong Shi, Shubing Huang, Beichen Zhao, Liang Peng and Chongming Wang

World Electr. Veh. J. 2025, 16(11), 626; https://doi.org/10.3390/wevj16110626 - 18 Nov 2025

Viewed by 259

Abstract

With the rapid growth of new energy vehicles (NEVs), the number of NEV-related traffic accidents has risen sharply. To address the challenge of low accuracy in real-time risk assessment caused by the coupling of multi-source heterogeneous data, this paper proposes a real-time risk [...] Read more.

With the rapid growth of new energy vehicles (NEVs), the number of NEV-related traffic accidents has risen sharply. To address the challenge of low accuracy in real-time risk assessment caused by the coupling of multi-source heterogeneous data, this paper proposes a real-time risk prediction method for NEV operations based on multi-source feature fusion. First, considering issues such as signal loss and bias in NEV operation data and accident records, a fused accident operation dataset is constructed through data matching, imputation, and Kalman smoothing. Then, this study analyzes the influence of external factors (e.g., weather, road type, and lighting) and internal factors (e.g., speed, acceleration, and driving duration) on accident risk and develops a normalized representation method for NEV accident risk features. Based on the coupling of internal and external parameters, a real-time accident risk prediction model is established based on the XGBoost algorithm, enabling accurate prediction of NEV accidents. Vehicle data tests show that the proposed method achieves an average accident risk prediction accuracy of 69.60%, outperforming the traditional Analytic Hierarchy Process and Support Vector Machine models. Finally, application effect demonstrates that the method reduces the NEV accident rate to 0.83%, effectively assisting traffic management departments in identifying and warning high-risk vehicles, thereby improving road traffic safety. Full article

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

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20 pages, 2783 KB

Open AccessArticle

Research on the Recycling Strategy of End-of-Life Power Battery for Electric Vehicles Based on Evolutionary Game

by Fangfang Zhao, Yiqi Geng, Wenhui Shi and Yingxue Ren

World Electr. Veh. J. 2025, 16(11), 625; https://doi.org/10.3390/wevj16110625 - 17 Nov 2025

Viewed by 340

Abstract

The rapid growth of China’s electric vehicle (EV) market has led to a peak in end-of-life (EOL) power batteries, yet the recycling sector remains dominated by informal operations. This paper incorporates the formal and informal recycling participation behaviours of EV owners into the [...] Read more.

The rapid growth of China’s electric vehicle (EV) market has led to a peak in end-of-life (EOL) power batteries, yet the recycling sector remains dominated by informal operations. This paper incorporates the formal and informal recycling participation behaviours of EV owners into the framework of evolutionary games, systematically examines the mechanism by which governmental incentive and disincentive mechanisms influence the evolutionary stability of each party, and constructs a tripartite evolutionary game model involving the government, recycling enterprises, and EV owners. Numerical simulation experiments conducted using PyCharm 2.3 provide an in-depth exploration of the strategic evolutionary trajectories of each participating agent. The findings indicate that (1) the stable strategy for the game-theoretic system of EOL power battery recycling is government non-regulation, recycling enterprises adopting formal recycling practices, and EV owners participating in formal recycling; (2) strengthening penalties against recycling enterprises will accelerate their transition towards formal recycling strategies, while increasing incentive levels can significantly enhance the steady-state probability of firms opting for formal recycling; (3) government subsidies for EV owners encourage both EV owners and recycling enterprises to adopt formal recycling, with recycling enterprises shifting first. This study enriches the application of evolutionary game theory in the field of EOL power battery recycling and further provides guidance for the healthy development of the recycling industry. Full article

(This article belongs to the Section Energy Supply and Sustainability)

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53 pages, 5248 KB

Open AccessArticle

Emission/Reliability-Aware Stochastic Optimization of Electric Bus Parking Lots and Renewable Energy Sources in Distribution Network: A Fuzzy Multi-Objective Framework Considering Forecasted Data

by Masood ur Rehman, Ujwal Ramesh Shirode, Aarti Suryakant Pawar, Tze Jin Wong, Egambergan Khudaynazarov and Saber Arabi Nowdeh

World Electr. Veh. J. 2025, 16(11), 624; https://doi.org/10.3390/wevj16110624 - 17 Nov 2025

Viewed by 371

Abstract

In this paper, an emission- and reliability-aware stochastic optimization model is proposed for the economic planning of electric bus parking lots (EBPLs) with photovoltaic (PV) and wind-turbine (WT) resources in an 85-bus radial distribution network. The model simultaneously minimizes operating, emission, and energy-loss [...] Read more.

In this paper, an emission- and reliability-aware stochastic optimization model is proposed for the economic planning of electric bus parking lots (EBPLs) with photovoltaic (PV) and wind-turbine (WT) resources in an 85-bus radial distribution network. The model simultaneously minimizes operating, emission, and energy-loss costs while increasing system reliability, measured by energy not supplied (ENS), and uses a fuzzy decision-making approach to determine the final solution. To address optimization challenges, a new multi-objective entropy-guided Sinh–Cosh Optimizer (MO-ESCHO) is proposed to efficiently mitigate premature convergence and produce a well-distributed Pareto front. Also, a hybrid forecasting architecture that combines MO-ESCHO and artificial neural networks (ANN) is proposed for accurate prediction of PV and WT power and network loading. The framework is tested across five cases, progressively incorporating EBPL, demand response (DR), forecast information, and stochastic simulation of uncertainties using a new hybrid Unscented Transformation–Cubature Quadrature Rule (UT-CQR) method. Comparative analyses against conventional methods confirm superior performance in achieving better objective values and ensuring computational efficiency. The outcomes indicate that the combination of EBPL with RES reduces operating costs by 5.23%, emission costs by 27.39%, and ENS by 11.48% compared with the base case with RES alone. Moreover, incorporating the stochastic model increases operating costs by 6.03%, emission costs by 5.05%, and ENS by 7.94% over the deterministic forecast case, reflecting the added complexity of uncertainty. The main contributions lie in coupling EBPLs and RES under uncertainty and proposing UT-CQR, which exhibits robust system performance with reduced variance and lower computational effort compared with Monte Carlo and cloud-model approaches. Full article

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26 pages, 4637 KB

Open AccessArticle

Evaluating Unplug Incentives to Improve User Experience and Increase DC Fast Charger Utilization

by Nathaniel Pearre, Niranjan Jayanath and Lukas Swan

World Electr. Veh. J. 2025, 16(11), 623; https://doi.org/10.3390/wevj16110623 - 14 Nov 2025

Viewed by 404

Abstract

Direct current fast charging is a necessary element of the transition to electric vehicles (EVs). Regulatory complexity, capital requirements, and challenging business models hinder charging infrastructure deployment, so focusing on the efficient use of such infrastructure is of paramount importance. A tool to [...] Read more.

Direct current fast charging is a necessary element of the transition to electric vehicles (EVs). Regulatory complexity, capital requirements, and challenging business models hinder charging infrastructure deployment, so focusing on the efficient use of such infrastructure is of paramount importance. A tool to improve this efficiency is an incentive to terminate charging events when charging power drops, the vehicle state of charge rises above some value, or time plugged in exceeds a threshold. A timeseries charging demand model was built based on observed EV population and charging behavior. This was used to explore these three incentive trigger metrics across a range of plausible values, to find their relative impacts on the vehicles charging, those waiting in line to access a cordset, and charging site operators. Results indicate that basing such a trigger on charging power would have little impact if the threshold power is low enough to accommodate older, slower-charging vehicles, but that more restrictive limits based on state of charge or charging duration can decrease wait times, increase vehicle throughput, and increase total energy sales for cordsets serving more than 1000 EVs per year. Full article

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

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17 pages, 3335 KB

Open AccessArticle

CLAMT Shifting Strategy with Dog Clutch and Active Synchronization for Electrified Tractors

by Bertug Bingol, Ece Olcay Gunes and Murat Gundogdu

World Electr. Veh. J. 2025, 16(11), 622; https://doi.org/10.3390/wevj16110622 - 14 Nov 2025

Viewed by 390

Abstract

This study focuses on the development and optimization of a Clutchless Automated-Manual Transmission (CLAMT) system for tractors, aiming to enhance performance and efficiency across diverse operating conditions. It explores the use of a dog clutch mechanism as a simpler, robust alternative to traditional [...] Read more.

This study focuses on the development and optimization of a Clutchless Automated-Manual Transmission (CLAMT) system for tractors, aiming to enhance performance and efficiency across diverse operating conditions. It explores the use of a dog clutch mechanism as a simpler, robust alternative to traditional synchronizers. The main objective is to replace complex transmission setups—often requiring up to 32 gear ratios—with a system that operates efficiently using only two gears, without sacrificing versatility. Smooth gear engagement, even under varying loads and terrains, is a key challenge addressed. To ensure this, a Vehicle Management Unit (VMU) manages gear shifts and actively synchronizes speeds. The system leverages steady torque delivery through control algorithms and modern hybrid/electric powertrain capabilities. Two algorithmic approaches are implemented, and their performance is evaluated through empirical testing. Results show improvements in system simplicity, transmission reliability, and overall operational efficiency. The proposed approach offers valuable insights for future agricultural drivetrains, highlighting the potential of dog clutch-based architectures in reducing mechanical complexity while maintaining functional performance. Full article

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26 pages, 28958 KB

Open AccessArticle

Impact Assessment of Electric Bus Charging on a Real-Life Distribution Feeder Using GIS-Integrated Power Utility Data: A Case Study in Brazil

by Camila dos Anjos Fantin, Fillipe Matos de Vasconcelos, Carolina Gonçalves Pardini, Felipe Proença de Albuquerque, Marco Esteban Rivera Abarca and Jakson Paulo Bonaldo

World Electr. Veh. J. 2025, 16(11), 621; https://doi.org/10.3390/wevj16110621 - 14 Nov 2025

Viewed by 483

Abstract

The electrification of public transport with battery electric buses (BEBs) poses technical, regulatory, and environmental challenges. This paper analyzes the impact of BEB charging on a Brazilian urban medium-voltage (MV) feeder using a novel methodology to convert utility GIS data into OpenDSS simulation [...] Read more.

The electrification of public transport with battery electric buses (BEBs) poses technical, regulatory, and environmental challenges. This paper analyzes the impact of BEB charging on a Brazilian urban medium-voltage (MV) feeder using a novel methodology to convert utility GIS data into OpenDSS simulation models. The study utilizes Geographic Database of the Distribution Company (BDGD) data from the Brazilian Electricity Regulatory Agency (ANEEL) and OpenDSS simulations. Motivated by Cuiabá’s proposal to electrify its public bus fleet, four realistic scenarios were simulated, incorporating distributed photovoltaic (PV) generation and vehicle-to-grid (V2G) operation. Results show that up to 118 BEBs can be charged simultaneously without voltage violations. However, thermal overload occurs beyond 56 units, requiring conductor upgrades or load redistribution. PV systems can supply up to 64% of the daily energy demand but introduce reverse power flows and overvoltages, indicating the need for dynamic control. V2G operation enables peak shaving but also leads to overvoltages when more than 33 buses inject power concurrently. The findings suggest that while the current infrastructure partially supports fleet electrification, future scalability depends on integrating smart grid features and reinforcing the system. Although focused on Cuiabá, the methodology offers a replicable approach for low-carbon urban mobility planning in similar developing regions. Full article

(This article belongs to the Special Issue Electric Vehicles in Smart Grids: Integration, Optimization, and Sustainability)

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19 pages, 596 KB

Open AccessArticle

An Efficient Drowsiness Detection Framework for Improving Driver Safety Through Supervised Learning Models

by Hassan Harb

World Electr. Veh. J. 2025, 16(11), 620; https://doi.org/10.3390/wevj16110620 - 13 Nov 2025

Viewed by 409

Abstract

Nowadays, we live in the smart mobility era in which vehicles are equipped with small sensing devices to collect various road information. With such sensors, we are able to provide an overview of what is happening on the road and offer an efficient [...] Read more.

Nowadays, we live in the smart mobility era in which vehicles are equipped with small sensing devices to collect various road information. With such sensors, we are able to provide an overview of what is happening on the road and offer an efficient solution for transport problems such as congestion, accidents, avoiding traffic lights, fuel consumption, etc. Particularly, driver drowsiness is one of the most important problems that transportation systems face and mostly leads to severe accidents, injuries, and deaths. In order to overcome such a problem, a set of sensor devices has been integrated into vehicles to monitor driver and driving behaviors, and then to evaluate the driver’s situation, e.g., drowsy or awake. Unfortunately, most of the proposed drowsiness detection techniques are dedicated to analyzing one behavior type, but not both, which may affect the accuracy rate of the detection. In this paper, we propose an efficient drowsiness detection framework (RDDF) that may analyze one behavior or be adapted to both of them in order to increase the accuracy of drowsiness detection. Mainly, RDDF periodically monitors the driver and driving behaviors, extracts important patterns, and then uses and compares a set of supervised learning models to detect drowsy drivers. After that, RDDF proposes a modified version of the K-nearest neighbors (KNN) model called Jaccard-KNN (JKNN) that increases drowsiness detection accuracy and overcomes several challenges imposed by traditional models. The proposed framework has been preliminarily validated through real sensor data, and we show the effectiveness of our framework in detecting real-time drowsy drivers with an accuracy rate of up to

99 %

. Full article

(This article belongs to the Special Issue Recent Advances in Intelligent Vehicle)

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30 pages, 3727 KB

Open AccessArticle

A Novel Model Chain for Analysing the Performance of Vehicle Integrated Photovoltaic (VIPV) Systems

by Hamid Samadi, Guido Ala, Miguel Centeno Brito, Marzia Traverso, Silvia Licciardi, Pietro Romano and Fabio Viola

World Electr. Veh. J. 2025, 16(11), 619; https://doi.org/10.3390/wevj16110619 - 13 Nov 2025

Viewed by 404

Abstract

This study proposes a novel framework for analyzing Vehicle-Integrated Photovoltaic (VIPV) systems, integrating optical, thermal, and electrical models. The model modifies existing fixed PV methodologies for VIPV applications to assess received irradiance, PV module temperature, and energy production, and is available as an [...] Read more.

This study proposes a novel framework for analyzing Vehicle-Integrated Photovoltaic (VIPV) systems, integrating optical, thermal, and electrical models. The model modifies existing fixed PV methodologies for VIPV applications to assess received irradiance, PV module temperature, and energy production, and is available as an open-source MATLAB tool (VIPVLIB) enabling simulations via a smartphone. A key innovation is the integration of meteorological data and real-time driving, dynamically updating vehicle position and orientation every second. Different time resolutions were explored to balance accuracy and computational efficiency for optical model, while the thermal model, enhanced by vehicle speed, wind effects, and thermal inertia, improved temperature and power predictions. Validation on a minibus operating within the University of Palermo campus confirmed the applicability of the proposed framework. The roof received 45–47% of total annual irradiation, and the total yearly energy yield reached about 4.3 MWh/Year for crystalline-silicon, 3.7 MWh/Year for CdTe, and 3.1 MWh/Year for CIGS, with the roof alone producing up to 2.1 MWh/Year (c-Si). Under hourly operation, the generated solar energy was sufficient to fully meet daily demand from April to August, while during continuous operation it supplied up to 60% of total consumption. The corresponding CO₂-emission reduction ranged from about 3.5 ton/Year for internal-combustion vehicles to around 2 ton/Year for electric ones. The framework provides a structured, data-driven approach for VIPV analysis, capable of simulating dynamic optical, thermal, and electrical behaviors under actual driving conditions. Its modular architecture ensures both immediate applicability and long-term adaptability, serving as a solid foundation for advanced VIPV design, fleet-scale optimization, and sustainability-oriented policy assessment. Full article

(This article belongs to the Section Energy Supply and Sustainability)

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34 pages, 595 KB

Open AccessArticle

Comprehensive Analysis of Stakeholder Dynamics for Strategic Electric Bus Adoption in Public Transit Networks

by Thisaiveerasingam Thilakshan, Thusitha Sugathapala, Saman Bandara and Dilum Dissanayake

World Electr. Veh. J. 2025, 16(11), 618; https://doi.org/10.3390/wevj16110618 - 12 Nov 2025

Viewed by 423

Abstract

Cities are increasingly using electric buses as a viable alternative to diesel buses. This is a crucial undertaking to achieve sustainability in the transport sector. However, integrating them in transport systems in developing countries such as Sri Lanka, which is characterized by environmental [...] Read more.

Cities are increasingly using electric buses as a viable alternative to diesel buses. This is a crucial undertaking to achieve sustainability in the transport sector. However, integrating them in transport systems in developing countries such as Sri Lanka, which is characterized by environmental and economic challenges, is complex. This work examines the factors that influence the shift from diesel to electric buses with particular attention to the stakeholders, their motivations, and how they seek to achieve their objectives regarding each other, both conflicting and cooperative angles. This study adopts a comprehensive stakeholder-centric methodology to analyze electric bus adoption in the public transit system in Sri Lanka. The research employs a mixed-methods approach that combines qualitative stakeholder analysis with quantitative barrier prioritization, following established project management principles. Based on the case study of Sri Lanka, the research investigates how the electric bus transition can be expedited by leveraging such alliances while considering local challenges like infrastructural deficits, policy gaps, and funding limitations. Lessons learned and best practices from international case studies are considered to provide strategic recommendations to policymakers and other stakeholders to promote the electric bus. By mapping out the interactions between various stakeholders and outlining where key leverage exists, the research provides a roadmap for introducing electric buses. This will be aligned with the sustainability targets and the vision to deliver sustainability goals for the long term. Full article

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

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14 pages, 3038 KB

Open AccessArticle

Fault Diagnosis Method of Four-Level Converter Based on Improved Dual-Kernel Extreme Learning Machine

by Ning Xie, Duotong Yang, Xiaohui Cao and Zhenglei Wang

World Electr. Veh. J. 2025, 16(11), 617; https://doi.org/10.3390/wevj16110617 - 12 Nov 2025

Viewed by 298

Abstract

To ensure the reliable operation of power converters and prevent catastrophic failures, this paper proposes a novel online fault diagnosis strategy for a four-level converter. The core of this strategy is an optimized multi-kernel extreme learning machine model. Specifically, the model extracts multi-scale [...] Read more.

To ensure the reliable operation of power converters and prevent catastrophic failures, this paper proposes a novel online fault diagnosis strategy for a four-level converter. The core of this strategy is an optimized multi-kernel extreme learning machine model. Specifically, the model extracts multi-scale features from three-phase output currents by combining Gaussian and polynomial kernels and employs particle swarm optimization to determine the optimal kernel fusion scheme. Experimental validation was performed on an online diagnosis platform for a four-level converter. The results show that the proposed method achieves a high diagnostic accuracy of 99.35% for open-circuit faults. Compared to conventional methods, this strategy significantly enhances diagnostic speed and accuracy through its optimized multi-kernel mechanism. Full article

(This article belongs to the Section Power Electronics Components)

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25 pages, 636 KB

Open AccessSystematic Review

Consensus on the Internet of Vehicles: A Systematic Literature Review

by Hilda Jemutai Bitok, Mingzhong Wang and Dennis Desmond

World Electr. Veh. J. 2025, 16(11), 616; https://doi.org/10.3390/wevj16110616 - 11 Nov 2025

Viewed by 453

Abstract

The Internet of Vehicles (IoV) revolutionizes transportation by enabling real-time communication and data exchange among vehicles (V2V), infrastructure (V2I), and other entities (V2X). These capabilities are crucial for improving road safety and traffic efficiency. However, achieving reliable and secure consensus across network nodes [...] Read more.

The Internet of Vehicles (IoV) revolutionizes transportation by enabling real-time communication and data exchange among vehicles (V2V), infrastructure (V2I), and other entities (V2X). These capabilities are crucial for improving road safety and traffic efficiency. However, achieving reliable and secure consensus across network nodes remains a significant challenge. Consensus mechanisms are essential in IoV for ensuring agreement on the network’s state, enabling applications such as autonomous driving, traffic management, and emergency response. This paper presents a systematic review of IoV consensus mechanisms, examining 78 peer-reviewed publications from 2010 to June 2025 using the PRISMA framework. Our analysis highlights challenges, including scalability, latency, and energy efficiency and identifies trends such as the adoption of lightweight algorithms, edge computing, and AI-assisted techniques. Unlike previous reviews, this work introduces a structured comparative framework specifically designed for IoV environments, enabling a detailed evaluation of consensus mechanisms across key features such as latency, fault tolerance, communication overhead and scalability to identify their relative strengths and limitations. Full article

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26 pages, 429 KB

Open AccessArticle

Dynamic Horizon-Based Energy Management for PEVs Considering Battery Degradation in Grid-Connected Microgrid Applications

by Junyi Zheng, Qian Tao, Qinran Hu and Muhammad Humayun

World Electr. Veh. J. 2025, 16(11), 615; https://doi.org/10.3390/wevj16110615 - 11 Nov 2025

Viewed by 323

Abstract

The growing integration of plug-in electric vehicles (PEVs) into microgrids presents both challenges and opportunities, particularly through vehicle-to-grid (V2G) services. This paper proposes a dynamic horizon optimization (DHO) framework with adaptive pricing for real-time scheduling of PEVs in a renewable-powered microgrid. The system [...] Read more.

The growing integration of plug-in electric vehicles (PEVs) into microgrids presents both challenges and opportunities, particularly through vehicle-to-grid (V2G) services. This paper proposes a dynamic horizon optimization (DHO) framework with adaptive pricing for real-time scheduling of PEVs in a renewable-powered microgrid. The system integrates solar and wind energy, V2G capabilities, and time-of-use (ToU) tariffs. The DHO strategy dynamically adjusts control horizons based on forecasted load, generation, and electricity prices, while considering battery health. A PEV-specific pricing scheme couples ToU tariffs with system marginal prices. Case studies on a microgrid with four heterogeneous EV charging stations show that the proposed method reduces peak load by 23.5%, lowers charging cost by 12.6%, and increases average final SoC by 12.5%. Additionally, it achieves a 6.2% reduction in carbon emissions and enables V2G revenue while considering battery longevity. Full article

(This article belongs to the Special Issue Smart Charging Strategies for Plug-In Electric Vehicles)

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24 pages, 3932 KB

Open AccessArticle

Advanced Fault Classification in Induction Motors for Electric Vehicles Using A Stacking Ensemble Learning Approach

by Said Benkaihoul, Saad Khadar, Yildirim Özüpak, Emrah Aslan, Mishari Metab Almalki and Mahmoud A. Mossa

World Electr. Veh. J. 2025, 16(11), 614; https://doi.org/10.3390/wevj16110614 - 9 Nov 2025

Cited by 2 | Viewed by 517

Abstract

This study proposes an innovative stacking ensemble learning framework for classifying faults in induction motors utilized in Electric Vehicles (EVs). Employing a comprehensive dataset comprising motor data, such as speed, torque, current, and voltage, the analysis encompasses six distinct conditions: normal operating mode, [...] Read more.

This study proposes an innovative stacking ensemble learning framework for classifying faults in induction motors utilized in Electric Vehicles (EVs). Employing a comprehensive dataset comprising motor data, such as speed, torque, current, and voltage, the analysis encompasses six distinct conditions: normal operating mode, over-voltage fault, under-voltage fault, overloading fault, phase-to-phase fault, and phase-to-ground fault. The proposed model integrates Gradient Boosting (GB), K-Nearest Neighbors (KNN), Gradient Boosting (XGBoost), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF) algorithms in a synergistic manner. The findings reveal that the RF–GB–DT–XGBoost combination achieves a remarkable accuracy of 98.53%, significantly surpassing other methods reported in the literature. Performance is evaluated through metrics including accuracy, precision, sensitivity, and F1-score, with results analyzed in comparison to practical applications and existing studies. Validated with real-world data, this study demonstrates that the proposed model offers a groundbreaking solution for predictive maintenance systems in the EV industry, exhibiting high generalization capacity despite complex operating conditions. This approach holds transformative potential for both academic research and industrial applications. The dataset used in this study was generated using a MATLAB 2018/Simulink-based Variable Frequency Drive (VFD) model that emulates real-world EV operating conditions rather than relying solely on laboratory data. This ensures that the developed model accurately reflects practical electric vehicle environments. Full article

(This article belongs to the Special Issue Dynamic Modeling, Identification, and Advanced Control of Intelligent Electric Vehicles)

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28 pages, 784 KB

Open AccessArticle

Comprehensive DEA-Based Evaluation of Charging Station Operational Efficiency

by Jinyu Wang, Houzhi Li, Yang Hu, Jiejin Yan, Chunhua Jin, Zhuowen Zhang and Zhen Yang

World Electr. Veh. J. 2025, 16(11), 613; https://doi.org/10.3390/wevj16110613 - 9 Nov 2025

Viewed by 434

Abstract

This study aims to evaluate the operational efficiency of electric vehicle (EV) charging stations and explore optimization strategies to enhance resource utilization and service performance. A systematic review approach was first applied to identify the main evaluation indicators and influencing factors from existing [...] Read more.

This study aims to evaluate the operational efficiency of electric vehicle (EV) charging stations and explore optimization strategies to enhance resource utilization and service performance. A systematic review approach was first applied to identify the main evaluation indicators and influencing factors from existing studies. Subsequently, a super-efficiency Data Envelopment Analysis (DEA) model was used to assess the efficiency of six EV charging stations in a certain City, China. The robustness analysis was carried out, and the output variables were replaced, and the evaluation results did not change. The results show substantial disparities in efficiency across stations: C1 exhibits the highest operational efficiency, while C3 performs the lowest. The inefficiencies primarily result from supply–demand mismatches and redundant capacity investment. Based on these findings, the study proposes both overall and localized optimization strategies to improve operational performance. The results provide valuable insights for urban energy infrastructure planning and contribute to the enhancement of high-quality, low-carbon transportation development in China. Full article

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27 pages, 1549 KB

Open AccessArticle

Adaptive Large Neighborhood Search for a Flexible Truck–Drone Routing Problem with Multi-Visit and Cost Trade-Offs

by Jiang Bian, Rui Zhou and Qingbin Meng

World Electr. Veh. J. 2025, 16(11), 612; https://doi.org/10.3390/wevj16110612 - 7 Nov 2025

Viewed by 683

Abstract

Extending the truck–drone mothership system, this paper addresses a flexible multi-visit truck and drone joint routing problem (FMTDJRP). A mixed integer linear program is proposed to minimize total travel cost. An adaptive large neighborhood search with a knowledge-based acceleration strategy yields near-optimal solutions. [...] Read more.

Extending the truck–drone mothership system, this paper addresses a flexible multi-visit truck and drone joint routing problem (FMTDJRP). A mixed integer linear program is proposed to minimize total travel cost. An adaptive large neighborhood search with a knowledge-based acceleration strategy yields near-optimal solutions. Experiments across varied customer distributions, drone specs, and truck-–drone cost ratios confirm flexibility, adaptability, and cost efficiency. Full article

(This article belongs to the Special Issue Changes in Travel Behavior When Autonomous Vehicles Are Integrated into the Existing Transport System in Urban Cities)

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31 pages, 870 KB

Open AccessReview

Application of Psychoacoustic Metrics in the Noise Assessment of Geared Drives

by Krisztian Horvath

World Electr. Veh. J. 2025, 16(11), 611; https://doi.org/10.3390/wevj16110611 - 6 Nov 2025

Viewed by 1037

Abstract

Psychoacoustic metrics offer a valuable complement to traditional noise evaluation methods for gear transmissions, as they account for the human perception of sound quality rather than relying solely on physical measurements. While parameters such as overall sound pressure level (SPL) and spectral content [...] Read more.

Psychoacoustic metrics offer a valuable complement to traditional noise evaluation methods for gear transmissions, as they account for the human perception of sound quality rather than relying solely on physical measurements. While parameters such as overall sound pressure level (SPL) and spectral content quantify noise intensity and frequency distribution, they often fail to reflect subjective annoyance caused by tonal or high-frequency components common in gear systems. This review provides a structured overview of how psychoacoustic metrics—including loudness, sharpness, roughness, fluctuation strength, and tonality—are applied in the analysis of gear transmission noise. Relevant studies were identified through a comprehensive search across multiple scientific databases, with 54 meeting the inclusion criteria. The findings highlight both the benefits and limitations of these metrics, and present examples of their industrial application in automotive and mechanical engineering contexts. The review also identifies gaps in current research, particularly in integrating psychoacoustic evaluation with predictive modelling and machine learning, and suggests directions for future work. Full article

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18 pages, 2055 KB

Open AccessArticle

Feasibility Analysis and Optimisation of Vehicle-Integrated Photovoltaic (VIPV) Systems for Sustainable Transportation

by Mark Smitheram and Ehsan Gatavi

World Electr. Veh. J. 2025, 16(11), 610; https://doi.org/10.3390/wevj16110610 - 6 Nov 2025

Viewed by 521

Abstract

This paper investigates the feasibility of vehicle-integrated photovoltaic (VIPV) systems for light vehicles by developing and simulating an intelligent solar integration design based on the Tesla Model 3. The proposed system incorporates roof and bonnet-mounted photovoltaic modules, each managed by independent buck converters [...] Read more.

This paper investigates the feasibility of vehicle-integrated photovoltaic (VIPV) systems for light vehicles by developing and simulating an intelligent solar integration design based on the Tesla Model 3. The proposed system incorporates roof and bonnet-mounted photovoltaic modules, each managed by independent buck converters employing maximum power point tracking (MPPT) for optimal energy extraction. A novel fuzzy logic controller was designed to dynamically allocate auxiliary battery charging between the traction battery and the solar subsystem, using real-time irradiance and state-of-charge (SOC) inputs. The system was implemented in MATLAB/Simulink with location-specific data for Melbourne, Australia. Simulation results demonstrate high converter efficiencies of 94–95%, stable MPPT convergence within 0.5 s and an estimated annual solar contribution of 930 kWh, confirming effective control and energy management under varying conditions. This work highlights the innovative application of adaptive fuzzy control and dual MPPT coordination within VIPV systems and provides a validated basis for future optimisation and real-world integration. Full article

(This article belongs to the Section Energy Supply and Sustainability)

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World Electr. Veh. J., Volume 16, Issue 11 (November 2025) – 51 articles

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