World Electric Vehicle Journal

0 pages, 7034 KiB

Open AccessArticle

Dynamic Simulation of Ground Braking Force Control Based on Fuzzy Adaptive PID for Integrated ABS-RBS System with Slip Ratio Consideration

by Pinjia Shi, Yongjun Min, Hui Wang and Liya Lv

World Electr. Veh. J. 2025, 16(7), 372; https://doi.org/10.3390/wevj16070372 - 3 Jul 2025

Abstract

This study resolves a critical challenge in electromechanical brake system validation: conventional ABS/RBS integrated platforms’ inability to dynamically simulate tire-road adhesion characteristics during braking. We propose a fuzzy adaptive PID-controlled magnetic powder clutch (MPC) system that achieves ground braking force simulation synchronized with [...] Read more.

This study resolves a critical challenge in electromechanical brake system validation: conventional ABS/RBS integrated platforms’ inability to dynamically simulate tire-road adhesion characteristics during braking. We propose a fuzzy adaptive PID-controlled magnetic powder clutch (MPC) system that achieves ground braking force simulation synchronized with slip ratio variations. The innovation encompasses: (1) Dynamic torque calculation model incorporating the curve characteristics of longitudinal friction coefficient (

φ

) versus slip ratio (

s

), (2) Nonlinear compensation through fuzzy self-tuning PID control, and (3) Multi-scenario validation platform. Experimental validation confirms superior tracking performance across multiple scenarios: (1) Determination coefficients R² of 0.942 (asphalt), 0.926 (sand), and 0.918 (snow) for uniform surfaces, (2) R² = 0.912/0.908 for asphalt-snow/snow-asphalt transitions, demonstrating effective adhesion characteristic simulation. The proposed control strategy achieves remarkable precision improvements, reducing integral time absolute error (ITAE) by 8.3–52.8% compared to conventional methods. Particularly noteworthy is the substantial ITAE reduction in snow conditions (236.47 vs. 500.969), validating enhanced simulation fidelity under extreme road surfaces. The system demonstrates consistently rapid response times. These improvements allow for highly accurate replication of dynamic slip ratio variations, establishing a refined laboratory-grade solution for EV regenerative braking coordination validation that greatly enhances strategy optimization efficiency. Full article

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0 pages, 1344 KiB

Open AccessReview

An Overview of Lithium-Ion Battery Recycling: A Comparison of Brazilian and International Scenarios

by Jean Furlanetto, Marcus V. C. de Lara, Murilo Simionato, Vagner do Nascimento and Giovani Dambros Telli

World Electr. Veh. J. 2025, 16(7), 371; https://doi.org/10.3390/wevj16070371 - 3 Jul 2025

Abstract

Purely electric and hybrid vehicles are emerging as the transport sector’s response to meet climate goals, aiming to mitigate global warming. As the adoption of transport electrification increases, the importance of recycling components of the electric propulsion system at the end of their [...] Read more.

Purely electric and hybrid vehicles are emerging as the transport sector’s response to meet climate goals, aiming to mitigate global warming. As the adoption of transport electrification increases, the importance of recycling components of the electric propulsion system at the end of their life grows, particularly the battery pack, which significantly contributes to the vehicle’s final cost and generates environmental impacts and CO₂ during production. This work presents an overview of the recycling processes for lithium-ion automotive batteries, emphasizing the developing Brazilian scenario and more established international scenarios. In Brazil, companies and research centers are investing in recycling and using reused cathode material to manufacture new batteries through the hydrometallurgical process. On the international front, pyrometallurgy and physical recycling are being applied, and other methods, such as direct processes and biohydrometallurgy, are also under study. Regardless of the recycling method, the main challenge is scaling prototype processes to meet current and future battery demand, driven by the growth of electric and hybrid vehicles, pursuing both environmental gains through reduced mining and CO₂ emissions and economic viability to make recycling profitable and support global electrification. Full article

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16 pages, 3186 KiB

Open AccessArticle

AI-Driven Framework for Secure and Efficient Load Management in Multi-Station EV Charging Networks

by Md Sabbir Hossen, Md Tanjil Sarker, Marran Al Qwaid, Gobbi Ramasamy and Ngu Eng Eng

World Electr. Veh. J. 2025, 16(7), 370; https://doi.org/10.3390/wevj16070370 - 2 Jul 2025

Abstract

This research introduces a comprehensive AI-driven framework for secure and efficient load management in multi-station electric vehicle (EV) charging networks, responding to the increasing demand and operational difficulties associated with widespread EV adoption. The suggested architecture has three main parts: a Smart Load [...] Read more.

This research introduces a comprehensive AI-driven framework for secure and efficient load management in multi-station electric vehicle (EV) charging networks, responding to the increasing demand and operational difficulties associated with widespread EV adoption. The suggested architecture has three main parts: a Smart Load Balancer (SLB), an AI-driven intrusion detection system (AIDS), and a Real-Time Analytics Engine (RAE). These parts use advanced machine learning methods like Support Vector Machines (SVMs), autoencoders, and reinforcement learning (RL) to make the system more flexible, secure, and efficient. The framework uses federated learning (FL) to protect data privacy and make decisions in a decentralized way, which lowers the risks that come with centralizing data. The framework makes load distribution 23.5% more efficient, cuts average wait time by 17.8%, and predicts station-level demand with 94.2% accuracy, according to simulation results. The AI-based intrusion detection component has precision, recall, and F1-scores that are all over 97%, which is better than standard methods. The study also finds important gaps in the current literature and suggests new areas for research, such as using graph neural networks (GNNs) and quantum machine learning to make EV charging infrastructures even more scalable, resilient, and intelligent. Full article

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26 pages, 2565 KiB

Open AccessArticle

The Impact of New Energy Vehicle Industry Agglomeration on High-Quality Green Development—Evidence from China

by Wenxin Liu and Tao Xie

World Electr. Veh. J. 2025, 16(7), 369; https://doi.org/10.3390/wevj16070369 - 2 Jul 2025

Abstract

In light of increasing environmental issues, green and environmentally friendly growth has emerged as a global consensus., making the progression of a low-carbon and eco-friendly new energy vehicle (NEV) industry essential for countries globally. This study focuses on the 26 provinces of China, [...] Read more.

In light of increasing environmental issues, green and environmentally friendly growth has emerged as a global consensus., making the progression of a low-carbon and eco-friendly new energy vehicle (NEV) industry essential for countries globally. This study focuses on the 26 provinces of China, employing benchmark regression, mediation analysis, spatial econometrics, and difference-in-differences models to comprehensively investigate the impact and underlying mechanisms of NEV industry agglomeration on high-quality green development, using a unified framework to measure both agglomeration and development standards, which enhances the accuracy of previous measurements using a single indicator. The findings show that NEV industry agglomeration directly promotes high-quality green development, mediated significantly by green technological innovation and public environmental awareness. Analysis reveals significant regional heterogeneity, with stronger NEV industry agglomeration in midwestern regions, areas prioritizing sustainable and low-carbon policies, and regions with advanced economic and financial systems, leading to a greater positive impact on high-quality green development. NEV industry agglomeration influences high-quality green development in neighboring regions through spatial spillover effects. The results remain robust when using instrumental variables and treating NEV-related policy formulation as a quasi-natural experiment. This study provides theoretical guidance and policy recommendations to encourage high-quality green development through NEV industry agglomeration. Full article

(This article belongs to the Special Issue Electric Vehicle Technology Development, Energy and Environmental Implications, and Decarbonization: 2nd Edition)

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34 pages, 4495 KiB

Open AccessArticle

Charging Ahead: Perceptions and Adoption of Electric Vehicles Among Full- and Part-Time Ridehailing Drivers in California

by Mengying Ju, Elliot Martin and Susan Shaheen

World Electr. Veh. J. 2025, 16(7), 368; https://doi.org/10.3390/wevj16070368 - 2 Jul 2025

Abstract

California’s SB 1014 (Clean Miles Standard) mandates ridehailing fleet electrification to reduce emissions from vehicle miles traveled, posing financial and infrastructure challenges for drivers. This study employs a mixed-methods approach, including expert interviews (n = 10), group discussions (n = 8), [...] Read more.

California’s SB 1014 (Clean Miles Standard) mandates ridehailing fleet electrification to reduce emissions from vehicle miles traveled, posing financial and infrastructure challenges for drivers. This study employs a mixed-methods approach, including expert interviews (n = 10), group discussions (n = 8), and a survey of full- and part-time drivers (n = 436), to examine electric vehicle (EV) adoption attitudes and policy preferences. Access to home charging and prior EV experience emerged as the most statistically significant predictors of EV acquisition. Socio-demographic variables, particularly income and age, could also influence the EV choice and sensitivity to policy design. Full-time drivers, though confident in the EV range, were concerned about income loss from the charging downtime and access to urban fast chargers. They showed a greater interest in EVs than part-time drivers and favored an income-based instant rebate at the point of sale. In contrast, part-time drivers showed greater hesitancy and were more responsive to vehicle purchase discounts (price reductions or instant rebates at the point of sale available to all customers) and charging credits (monetary incentive or prepaid allowance to offset the cost of EV charging equipment). Policymakers might target low-income full-time drivers with greater price reductions and offer charging credits (USD 500 to USD 1500) to part-time drivers needing operational and infrastructure support. Full article

(This article belongs to the Special Issue Exploring Beyond Electric Cars: Comprehensive Charging Solutions and Innovations in Land, Sea, and Air Mobility)

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15 pages, 4855 KiB

Open AccessArticle

A Semi-Active Control Method for Trains Based on Fuzzy Rules of Non-Stationary Wind Fields

by Gaoyang Meng, Jianjun Meng, Defang Lv, Yanni Shen and Zhicheng Wang

World Electr. Veh. J. 2025, 16(7), 367; https://doi.org/10.3390/wevj16070367 - 2 Jul 2025

Abstract

The stochastic fluctuation characteristics of wind speed can significantly affect the control performance of train suspension systems. To enhance the running quality of trains in non-stationary wind fields, this paper proposes a semi-active control method for trains based on fuzzy rules of non-stationary [...] Read more.

The stochastic fluctuation characteristics of wind speed can significantly affect the control performance of train suspension systems. To enhance the running quality of trains in non-stationary wind fields, this paper proposes a semi-active control method for trains based on fuzzy rules of non-stationary wind fields. Firstly, a dynamic model of the train and suspension system was established based on the CRH2 (China Railway High-Speed 2) high-speed train and magnetorheological dampers. Then, using frequency–time transformation technology, the non-stationary wind load excitation and train response patterns under 36 common operating conditions were calculated. Finally, by analyzing the response patterns of the train under different operating conditions, a comprehensive control rule table for the semi-active suspension system of the train under non-stationary wind fields was established, and a fuzzy controller suitable for non-stationary wind fields was designed. To verify the effectiveness of the proposed method, the running smoothness of the train was analyzed using a train-semi-active suspension system co-simulation model based on real wind speed data from the Lanzhou–Xinjiang railway line. The results demonstrate that the proposed method significantly improves the running quality of the train. Specifically, when the wind speed reaches 20 m/s and the train speed reaches 200 km/h, the lateral Sperling index is increased by 46.4% compared to the optimal standard index, and the vertical Sperling index is increased by 71.6% compared to the optimal standard index. Full article

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Graphical abstract

18 pages, 3139 KiB

Open AccessArticle

Sliding Mode Thrust Control Strategy for Electromagnetic Energy-Feeding Shock Absorbers Based on an Improved Gray Wolf Optimizer

by Wenqiang Zhang, Jiayu Lu, Wenqing Ge, Xiaoxuan Xie, Cao Tan and Huichao Zhang

World Electr. Veh. J. 2025, 16(7), 366; https://doi.org/10.3390/wevj16070366 - 2 Jul 2025

Abstract

Owing to its high energy efficiency, regenerative capability, and fast dynamic response, the Electromagnetic Energy-Feeding Shock Absorber has found widespread application in automotive suspension control systems. To further improve thrust control precision, this study presents a sliding mode thrust controller designed using an [...] Read more.

Owing to its high energy efficiency, regenerative capability, and fast dynamic response, the Electromagnetic Energy-Feeding Shock Absorber has found widespread application in automotive suspension control systems. To further improve thrust control precision, this study presents a sliding mode thrust controller designed using an improved Gray Wolf Optimization algorithm. Firstly, an improved exponential reaching law is adopted, where a saturation function replaces the traditional sign function to enhance system tracking accuracy and stability. Meanwhile, a position update strategy from the particle swarm optimization (PSO) algorithm is integrated into the gray wolf optimizer (GWO) to improve the global search ability and the balance of local exploitation. Secondly, the improved GWO is combined with sliding mode control to achieve online optimization of controller parameters, ensuring system robustness while suppressing chattering. Finally, comparative analyses and simulation validations are conducted to verify the effectiveness of the proposed controller. Simulation results show that, under step input conditions, the improved GWO reduces the rise time from 0.0034 s to 0.002 s and the steady-state error from 0.4 N to 0.12 N. Under sinusoidal input, the average error is reduced from 0.26 N to 0.12 N. Under noise disturbance, the average deviation is reduced from 2.77 N to 2.14 N. These results demonstrate that the improved GWO not only provides excellent trajectory tracking and control accuracy but also exhibits strong robustness under varying operating conditions and random white noise disturbances. Full article

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18 pages, 265 KiB

Open AccessReview

Battery Electric Vehicle Safety Issues and Policy: A Review

by Sanjeev M. Naiek, Sorawich Aungsuthar, Corey Harper and Chris Hendrickson

World Electr. Veh. J. 2025, 16(7), 365; https://doi.org/10.3390/wevj16070365 - 1 Jul 2025

Abstract

Battery electric vehicles (BEVs) are seeing widespread adoption globally due to technological improvements, lower manufacturing costs, and supportive policies aimed at reducing greenhouse gas emissions. Governments have introduced incentives such as purchase subsidies and investments in charging infrastructure, while automakers continue to broaden [...] Read more.

Battery electric vehicles (BEVs) are seeing widespread adoption globally due to technological improvements, lower manufacturing costs, and supportive policies aimed at reducing greenhouse gas emissions. Governments have introduced incentives such as purchase subsidies and investments in charging infrastructure, while automakers continue to broaden their electric vehicle portfolios. Although BEVs show high overall safety performance comparable to internal combustion engine vehicles (ICEVs), they also raise distinct safety challenges that merit policy attention. This review synthesizes the current literature on safety concerns associated with BEVs, with particular attention to fire risks, vehicle weight, low-speed noise levels, and unique driving characteristics. Fire safety remains a significant issue, as lithium-ion battery fires, although less frequent than those in ICEVs, tend to be more severe and difficult to manage. Strategies such as improved thermal management, fire enclosures, and standardized response protocols are essential. BEVs are typically heavier than ICEVs, affecting crash outcomes and braking performance. These risks are especially important for interactions with pedestrians and smaller vehicles. Quiet operation at low speeds can also reduce pedestrian awareness, prompting regulations for vehicle sound alerts. Together, these issues highlight the need for policies that address both emerging safety risks and the evolving nature of BEV technology. Full article

29 pages, 2891 KiB

Open AccessArticle

Cybersecurity Risks in EV Mobile Applications: A Comparative Assessment of OEM and Third-Party Solutions

by Bilal Saleem, Alishba Rehman, Muhammad Ali Hassan and Zia Muhammad

World Electr. Veh. J. 2025, 16(7), 364; https://doi.org/10.3390/wevj16070364 - 30 Jun 2025

Abstract

As the world accelerates toward a sustainable future with electric vehicles (EVs), smartphone applications have become an indispensable tool for drivers. These applications, developed by both EV manufacturers and third-party developers, offer functionalities such as remote vehicle control, charging station location, and route [...] Read more.

As the world accelerates toward a sustainable future with electric vehicles (EVs), smartphone applications have become an indispensable tool for drivers. These applications, developed by both EV manufacturers and third-party developers, offer functionalities such as remote vehicle control, charging station location, and route planning. However, they also have access to sensitive information, making them potential targets for cyber threats. This paper presents a comprehensive survey of the cybersecurity vulnerabilities, weaknesses, and permissions in these applications. We categorize 20 applications into two groups: those developed by EV manufacturers and those by third parties, and conduct a comparative analysis of their functionalities by performing static and dynamic analysis. Our findings reveal major security flaws such as poor authentication, broken encryption, and insecure communication, among others. The paper also discusses the implications of these vulnerabilities and the risks they pose to users. Furthermore, we analyze 10 permissions and 12 functionalities that are not present in official EV applications and mostly present in third-party apps, leading users to rely on poorly built third-party applications, thereby increasing their attack surface. To address these issues, we propose defensive measures which include 10 CWE AND OWASP top 10 defenses to enhance the security of these applications, ensuring a safe and secure transition to EVs. Full article

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27 pages, 10823 KiB

Open AccessArticle

Adaptive and Collaborative Hierarchical Optimization Strategies for a Multi-Microgrid System Considering EV and Storage

by Yifeng He, Tong Liu, Zilong Wang, Qiqi Ren and Alian Chen

World Electr. Veh. J. 2025, 16(7), 363; https://doi.org/10.3390/wevj16070363 - 30 Jun 2025

Abstract

The disordered nature of electric vehicle (EV) charging and user electricity consumption behaviors has intensified the strain on the grid. Meanwhile, energy storage technologies and microgrid interconnections still lack effective supply–consumption regulations and cost–benefit optimization mechanisms. Therefore, the system’s operational efficiency holds significant [...] Read more.

The disordered nature of electric vehicle (EV) charging and user electricity consumption behaviors has intensified the strain on the grid. Meanwhile, energy storage technologies and microgrid interconnections still lack effective supply–consumption regulations and cost–benefit optimization mechanisms. Therefore, the system’s operational efficiency holds significant potential for improvement. This paper proposes hierarchical optimization strategies for the multi-microgrid system to address these issues. In the lower layer, for the charging states of EVs in a single microgrid, an improved simulation method to enhance accuracy and a recursion mechanism of an energy storage margin band to facilitate intelligent EV-to-grid interaction are proposed. Additionally, in conjunction with demand management, an adaptive optimization method and a Pareto decision method are proposed to achieve optimal peak shaving and valley filling for both the EVs and load, yielding a 38.5% reduction in the total electricity procurement costs. The upper layer is built upon the EV–load management strategies of microgrids in the lower layers and evolves into a distributed interconnection structure. Furthermore, a dynamic optimization mechanism based on state mapping and a collaborative optimization method are proposed to improve storage benefits and energy synergies, achieving a 22.1% reduction in the total operating cost. The results provided demonstrate that the proposed strategy optimizes the operation of the multi-microgrid system, effectively enhancing the overall operational efficiency and economic performance. Full article

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18 pages, 3380 KiB

Open AccessArticle

Assessment of the Impact of Vehicle Electrification on the Increase in Total Electrical Energy Consumption in Bosnia and Herzegovina

by Mirsad Trobradović, Jasmin Šehović, Nedim Pervan, Almir Blažević, Haris Lulić, Vahidin Hadžiabdić and Adis J. Muminović

World Electr. Veh. J. 2025, 16(7), 362; https://doi.org/10.3390/wevj16070362 - 29 Jun 2025

Abstract

In this paper, an assessment of the impact of the electrification of the vehicle fleet in Bosnia and Herzegovina on the total electrical energy consumption is made, for different scenarios of increasing the number of electric vehicles. Based on a statistical analysis of [...] Read more.

In this paper, an assessment of the impact of the electrification of the vehicle fleet in Bosnia and Herzegovina on the total electrical energy consumption is made, for different scenarios of increasing the number of electric vehicles. Based on a statistical analysis of the structure and number of vehicles in Bosnia and Herzegovina in the period from 2010 to 2024, an estimate of the total number of passenger cars, as well as the number of electric vehicles for the period up to 2050, is made. It is estimated that in 2050 the number of electric passenger cars will be around 300,000. For one representative electric passenger car, averaged annual electrical energy consumption is calculated. Based on the calculation and for the estimated number of electric vehicles in use, the total annual consumption of electrical energy for the segment of passenger cars is defined, for different scenarios of increasing the number of electric vehicles. Following the estimated increase in the number of passenger electric cars, an exponential increase in electrical energy consumption is estimated, reaching the annual amount of 635 GWh in 2050, which is 10 times higher than the total electrical energy consumption of the transport sector in 2024. In this way, for the period up to 2050, the additional amount of electrical energy that the electrical power grid should provide, due to the electrification of the vehicle fleet, is estimated. Full article

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22 pages, 11249 KiB

Open AccessArticle

A Correlational Study on Architectural Design and Thermal Distribution Patterns Using a Novel Multi-Terminal Approach in Cylindrical Li-Ion Cell-Integrated Battery Packs

by Sagar D, Raja Ramar and Shama Ravichandran

World Electr. Veh. J. 2025, 16(7), 361; https://doi.org/10.3390/wevj16070361 - 28 Jun 2025

Abstract

A novel architectural design is proposed to mitigate uneven thermal distribution, peak temperature, and heat spot generation, which are common issues that are observed in conventional battery packs. This approach features a multi-terminal configuration, incorporating a modified battery pack structure along with a [...] Read more.

A novel architectural design is proposed to mitigate uneven thermal distribution, peak temperature, and heat spot generation, which are common issues that are observed in conventional battery packs. This approach features a multi-terminal configuration, incorporating a modified battery pack structure along with a multi-terminal switching algorithm that identifies the optimal terminal for current flow to the load. In the proposed design, the first and second terminals are placed at the first and fourth series string while the battery pack is divided into four regions, each corresponding to one series string. Additionally, terminal points represent the four thermal zones at the pack level. Experiments were conducted to evaluate the performance of the dual-terminal switching mechanism in three configurations—1S, 2S, and 3S. The 1S setup outperformed the single-terminal design, achieving a 6.23% improvement in reducing the zone temperature difference (ΔP_z). The 2S configuration demonstrated an 11.11% improvement, while the 3S setup achieved an improvement in peak region difference (ΔP_r) of >50%, without a cooling system. Finally, while forced air cooling effectively lowers peak temperature, it is insufficient in addressing thermal distribution and heat spot formation. However, integrating the proposed multi-terminal approach enables the effective control and management of all three critical thermal parameters—peak temperature, thermal distribution, and heat spot generation. Full article

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14 pages, 3334 KiB

Open AccessArticle

Quantitative Assessment of EV Energy Consumption: Applying Coast Down Testing to WLTP and EPA Protocols

by Teeraphon Phophongviwat, Piyawong Poopanya and Kanchana Sivalertporn

World Electr. Veh. J. 2025, 16(7), 360; https://doi.org/10.3390/wevj16070360 - 27 Jun 2025

Abstract

This study presents a comprehensive methodology for evaluating electric vehicle (EV) energy consumption by integrating coast down testing with standardized chassis dynamometer protocols under WLTP Class 3b and EPA driving cycles. Coast down tests were conducted to determine road load coefficients—critical for replicating [...] Read more.

This study presents a comprehensive methodology for evaluating electric vehicle (EV) energy consumption by integrating coast down testing with standardized chassis dynamometer protocols under WLTP Class 3b and EPA driving cycles. Coast down tests were conducted to determine road load coefficients—critical for replicating real-world resistance profiles on a dynamometer. Energy usage data were measured using On-Board Diagnostics II (OBD-II) and dynamometer measurements to assess power flow from the battery to the wheels. The results reveal that OBD-II consistently recorded higher cumulative energy usage, particularly under urban driving conditions, highlighting limitations in dynamometer responsiveness to transient loads and regenerative events. Notably, the WLTP low-speed cycle exhibited a significantly lower efficiency of 62.42%, with nearly half of the battery energy consumed by non-propulsion systems. In contrast, the EPA cycle demonstrated consistently higher efficiencies of 84.52% (low-speed) and 93.00% (high-speed). Interestingly, high-speed efficiencies between WLTP and EPA were nearly identical, despite differences in total energy consumption. These findings underscore the importance of aligning test protocols with actual driving conditions and demonstrate the effectiveness of combining coast down data with real-time diagnostics for robust EV performance assessments. Full article

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22 pages, 3431 KiB

Open AccessArticle

Safety–Efficiency Balanced Navigation for Unmanned Tracked Vehicles in Uneven Terrain Using Prior-Based Ensemble Deep Reinforcement Learning

by Yiming Xu, Songhai Zhu, Dianhao Zhang, Yinda Fang and Mien Van

World Electr. Veh. J. 2025, 16(7), 359; https://doi.org/10.3390/wevj16070359 - 27 Jun 2025

Abstract

This paper proposes a novel navigation approach for Unmanned Tracked Vehicles (UTVs) using prior-based ensemble deep reinforcement learning, which fuses the policy of the ensemble Deep Reinforcement Learning (DRL) and Dynamic Window Approach (DWA) to enhance both exploration efficiency and deployment safety in [...] Read more.

This paper proposes a novel navigation approach for Unmanned Tracked Vehicles (UTVs) using prior-based ensemble deep reinforcement learning, which fuses the policy of the ensemble Deep Reinforcement Learning (DRL) and Dynamic Window Approach (DWA) to enhance both exploration efficiency and deployment safety in unstructured off-road environments. First, by integrating kinematic analysis, we introduce a novel state and an action space that account for rugged terrain features and track–ground interactions. Local elevation information and vehicle pose changes over consecutive time steps are used as inputs to the DRL model, enabling the UTVs to implicitly learn policies for safe navigation in complex terrains while minimizing the impact of slipping disturbances. Then, we introduce an ensemble Soft Actor–Critic (SAC) learning framework, which introduces the DWA as a behavioral prior, referred to as the SAC-based Hybrid Policy (SAC-HP). Ensemble SAC uses multiple policy networks to effectively reduce the variance of DRL outputs. We combine the DRL actions with the DWA method by reconstructing the hybrid Gaussian distribution of both. Experimental results indicate that the proposed SAC-HP converges faster than traditional SAC models, which enables efficient large-scale navigation tasks. Additionally, a penalty term in the reward function about energy optimization is proposed to reduce velocity oscillations, ensuring fast convergence and smooth robot movement. Scenarios with obstacles and rugged terrain have been considered to prove the SAC-HP’s efficiency, robustness, and smoothness when compared with the state of the art. Full article

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25 pages, 9001 KiB

Open AccessArticle

Analysis of the Impact of Electromobility on the Distribution Grid

by Tomislav Kovačević, Ružica Kljajić, Hrvoje Glavaš and Milan Kljajin

World Electr. Veh. J. 2025, 16(7), 358; https://doi.org/10.3390/wevj16070358 - 27 Jun 2025

Abstract

This paper analyzes the impact of electromobility on distribution grids and voltage stability. In line with current legislation and the European Commission’s plans for the future of electromobility, the aim is to increase the share of electric vehicles to 50% by 2050. However, [...] Read more.

This paper analyzes the impact of electromobility on distribution grids and voltage stability. In line with current legislation and the European Commission’s plans for the future of electromobility, the aim is to increase the share of electric vehicles to 50% by 2050. However, achieving this target can be challenging due to the characteristics and features of the electric vehicle charging stations and the associated charging methods, which can lead to constraints within the network. The analysis includes the integration of single-phase and three-phase chargers on a radial feeder, as well as the determination of the maximum number of vehicles that can be accommodated on a given feeder without compromising voltage stability. Five scenarios are evaluated using the DigSilent software package to gain a better understanding of the impact of electromobility on the distribution grid. Full article

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

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26 pages, 3661 KiB

Open AccessArticle

Mathematical Model for the Study of Energy Storage Cycling in Electric Rail Transport

by Boris V. Malozyomov, Nikita V. Martyushev, Vladimir Yu. Konyukhov, Olga I. Matienko, Vladislav V. Kukartsev, Oleslav A. Antamoshkin and Yulia I. Karlina

World Electr. Veh. J. 2025, 16(7), 357; https://doi.org/10.3390/wevj16070357 - 27 Jun 2025

Abstract

The rapid development of electric transport necessitates efficient energy storage and redistribution in traction systems. A key challenge is the utilization of regenerative braking energy, which is often dissipated in resistors due to network saturation and limited consumption capacity. The paper addresses the [...] Read more.

The rapid development of electric transport necessitates efficient energy storage and redistribution in traction systems. A key challenge is the utilization of regenerative braking energy, which is often dissipated in resistors due to network saturation and limited consumption capacity. The paper addresses the problem of inefficient energy utilization in electric rail vehicles due to the absence of effective energy recovery mechanisms. A specific challenge arises when managing energy recuperated during regenerative braking, which is typically lost if not immediately reused. This study proposes the integration of on-board energy storage systems (ESS) based on supercapacitor technology to temporarily store excess braking energy. A mathematical model of a traction drive with a DC motor and supercapacitor-based ESS is developed, accounting for variable load profiles and typical urban driving cycles. Simulation results demonstrate potential energy savings of up to 30%, validating the feasibility of the proposed solution. The model also enables system-level analysis for optimal ESS sizing and placement in electric rail vehicles. Full article

(This article belongs to the Special Issue Battery Management System in Electric and Hybrid Vehicles)

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23 pages, 5955 KiB

Open AccessArticle

Remaining Useful Life Interval Prediction for Lithium-Ion Batteries via Periodic Time Series and Trend Filtering Segmentation-Based Fuzzy Information Granulation

by Chunsheng Cui, Guangshu Xia, Chenyu Jia and Jie Wen

World Electr. Veh. J. 2025, 16(7), 356; https://doi.org/10.3390/wevj16070356 - 26 Jun 2025

Abstract

The accurate prediction of remaining useful life (RUL) is crucial in order to reasonably and efficiently utilize lithium-ion batteries (LiBs). In this paper, a construction method of periodic time series is applied to the degradation data of LiBs to address the issues of [...] Read more.

The accurate prediction of remaining useful life (RUL) is crucial in order to reasonably and efficiently utilize lithium-ion batteries (LiBs). In this paper, a construction method of periodic time series is applied to the degradation data of LiBs to address the issues of insufficient training data and smooth degradation in the RUL interval prediction method based on trend filtering segmentation and fuzzy information granulation. The construction method for periodic time series is used to form a new dataset from the original data, based on which the fusion model, by combining the variational mode decomposition (VMD) and gated recurrent unit (GRU), is used as the RUL interval prediction model of LiBs. Moreover, the effectiveness and advantage of the RUL interval prediction method proposed in this paper was verified and analyzed by utilizing the CALCE battery dataset and NCA battery dataset. Full article

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12 pages, 1522 KiB

Open AccessArticle

Reduction of Current Harmonics in BLDC Motors Using the Proposed Sigmoid Trapezoidal Current Hysteresis Control

by Anuradha Thangavelu, Jebarani Evangeline Stephen, Srithar Samidurai, Ranganayaki Velusamy, Selligoundanur Subramaniyam Sivaraju, Subramaniam Usha and Sivakumar Palaniswamy

World Electr. Veh. J. 2025, 16(7), 355; https://doi.org/10.3390/wevj16070355 - 25 Jun 2025

Abstract

Brushless DC (BLDC) motors are widely used in applications such as Electric Vehicles (EVs) due to their high efficiency, low maintenance, and favorable torque-to-mass ratio. However, one major challenge in BLDC motors is the presence of current harmonics, which can lead to increased [...] Read more.

Brushless DC (BLDC) motors are widely used in applications such as Electric Vehicles (EVs) due to their high efficiency, low maintenance, and favorable torque-to-mass ratio. However, one major challenge in BLDC motors is the presence of current harmonics, which can lead to increased noise, vibration, and reduced efficiency, particularly at low speeds or light loads. These harmonics primarily arise from abrupt current transitions during phase commutation. To address this, thispaper presents an innovative approach that combines the Proposed Sigmoid Trapezoidal Current Model with hysteresis control to reduce current harmonics. The model facilitates smooth current changes by applying a sigmoid function, replacing sharp transitions with gradual ones, thus significantly minimizing harmonic distortion. Additionally, hysteresis PWM control enhances the system by precisely regulating the current and dynamically adjusting the switching frequency to maintain the current within a defined range. Simulation results confirm the effectiveness of this method, showing substantial reductions in current harmonics, speed ripple, and torque ripple. Specifically, the proposed method reduces torque ripple by 81% compared to traditional Electronic Commutation Control and improves torque ripple by 30% compared to the conventional method. Full article

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19 pages, 3238 KiB

Open AccessArticle

Optimal Location for Electric Vehicle Fast Charging Station as a Dynamic Load for Frequency Control Using Particle Swarm Optimization Method

by Yassir A. Alhazmi and Ibrahim A. Altarjami

World Electr. Veh. J. 2025, 16(7), 354; https://doi.org/10.3390/wevj16070354 - 25 Jun 2025

Abstract

There are significant emissions of greenhouse gases into the atmosphere from the transportation industry. As a result, the idea that electric vehicles (EVs) offer a revolutionary way to reduce greenhouse gas emissions and our reliance on rapidly depleting petroleum supplies has been put [...] Read more.

There are significant emissions of greenhouse gases into the atmosphere from the transportation industry. As a result, the idea that electric vehicles (EVs) offer a revolutionary way to reduce greenhouse gas emissions and our reliance on rapidly depleting petroleum supplies has been put forward. EVs are becoming more common in many nations worldwide, and the rapid uptake of this technology is heavily reliant on the growth of charging stations. This is leading to a significant increase in their number on the road. This rise has created an opportunity for EVs to be integrated with the power system as a Demand Response (DR) resource in the form of an EV fast charging station (EVFCS). To allocate electric vehicle fast charging stations as a dynamic load for frequency control and on specific buses, this study included the optimal location for the EVFCS and the best controller selection to obtain the best outcomes as DR for various network disruptions. The optimal location for the EVFCS is determined by applying transient voltage drop and frequency nadir parameters to the Particle Swarm Optimization (PSO) location model as the first stage of this study. The second stage is to explore the optimal regulation of the dynamic EVFCS load using the PSO approach for the PID controller. PID controller settings are acquired to efficiently support power system stability in the event of disruptions. The suggested model addresses various types of system disturbances—generation reduction, load reduction, and line faults—when it comes to the Kundur Power System and the IEEE 39 bus system. The results show that Bus 1 then Bus 4 of the Kundur System and Bus 39 then Bus 1 in the IEEE 39 bus system are the best locations for dynamic EVFCS. Full article

(This article belongs to the Special Issue Active Voltage and Frequency Support Control by the EV, New Energy and Energy Storages, 2nd Edition)

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