World Electric Vehicle Journal

3 pages, 196 KB

Open AccessCorrection

Correction: Zhang et al. Design of Coordinated EV Traffic Control Strategies for Expressway System with Wireless Charging Lanes. World Electr. Veh. J. 2025, 16, 496

by Yingying Zhang, Yifeng Hong and Zhen Tan

World Electr. Veh. J. 2025, 16(10), 588; https://doi.org/10.3390/wevj16100588 - 21 Oct 2025

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Abstract

In the original publication [...] Full article

14 pages, 2289 KB

Open AccessArticle

FPGA Implementation of Battery State-of-Charge Estimation Using Extended Kalman Filter and Dynamic Sampling

by Seungjae Yun, Jeongju Jeon, Eunseong Lee, Taeyeon Jeong and Sunhee Kim

World Electr. Veh. J. 2025, 16(10), 587; https://doi.org/10.3390/wevj16100587 - 20 Oct 2025

Viewed by 549

Abstract

The rapid increase in the adoption of electric vehicles (EVs) has highlighted issues related to the safety and efficiency of lithium-ion batteries. This study implemented a hardware module to effectively estimate the state of charge (SOC), which is a core element of the [...] Read more.

The rapid increase in the adoption of electric vehicles (EVs) has highlighted issues related to the safety and efficiency of lithium-ion batteries. This study implemented a hardware module to effectively estimate the state of charge (SOC), which is a core element of the battery management system (BMS), using an extended Kalman filter (EKF)-based approach. A method to reduce the power consumption during hardware design through adjustments to the sampling period according to the SOC range was proposed. The root mean square error was obtained as below 0.75, with only 2455 samples out of the 700,000 measurements, achieving a reduction of 99.65%. Following the evaluation of the accuracy of the software model, the results were compared through hardware implementation. Consequently, the performance was verified via synthesis using a DE2-115 FPGA board from Terasic in Taiwan. Full article

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

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14 pages, 1554 KB

Open AccessArticle

Analysis and Improvement of the Dynamic Characteristics of an Electro-Hydrostatic Actuator Based on a Vehicle’s Active Suspension

by Peng Chen and Xing Chen

World Electr. Veh. J. 2025, 16(10), 586; https://doi.org/10.3390/wevj16100586 - 20 Oct 2025

Viewed by 324

Abstract

This study investigates the dynamic characteristics of electro-hydrostatic actuators (EHA), which serve as the core actuating element in vehicle active suspension systems, with the aim of enhancing overall system performance. The purpose of this research is to identify and address the factors limiting [...] Read more.

This study investigates the dynamic characteristics of electro-hydrostatic actuators (EHA), which serve as the core actuating element in vehicle active suspension systems, with the aim of enhancing overall system performance. The purpose of this research is to identify and address the factors limiting EHA dynamic response. Through theoretical analysis from the perspectives of natural frequency properties and power demand, the study reveals that the natural frequency of the motor-pump assembly acts as the primary bottleneck, while insufficient motor output torque represents another major constraint. To overcome these limitations, a method is proposed involving increased maximum motor output torque and reduced rotational inertia of the motor-pump assembly. The feasibility of this approach is validated via frequency domain simulation analysis. Comparative simulations demonstrate that the enhanced EHA system exhibits significantly improved dynamic performance under both step and sinusoidal position commands compared to the baseline system. These findings provide important theoretical insights and practical directions for overcoming actuator performance limitations in vehicle active suspension systems. Full article

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22 pages, 2464 KB

Open AccessArticle

Fuzzy Control with Modified Fireworks Algorithm for Fuel Cell Commercial Vehicle Seat Suspension

by Nannan Jiang and Xiaoliang Chen

World Electr. Veh. J. 2025, 16(10), 585; https://doi.org/10.3390/wevj16100585 - 17 Oct 2025

Viewed by 406

Abstract

Enhancing ride comfort and vibration control performance is a critical requirement for fuel cell commercial vehicles (FCCVs). This study develops a semi-active seat suspension control strategy that integrates a fuzzy logic controller with a Modified Fireworks Algorithm (MFWA) to systematically optimize fuzzy parameters. [...] Read more.

Enhancing ride comfort and vibration control performance is a critical requirement for fuel cell commercial vehicles (FCCVs). This study develops a semi-active seat suspension control strategy that integrates a fuzzy logic controller with a Modified Fireworks Algorithm (MFWA) to systematically optimize fuzzy parameters. A seven-degree-of-freedom (7-DOF) half-vehicle model, including the magnetorheological damper (MRD)-based seat suspension system, is established in MATLAB/Simulink to evaluate the methodology under both random and bump road excitations. In addition, a hardware-in-the-loop (HIL) experimental validation was conducted, confirming the real-time feasibility and effectiveness of the proposed controller. Comparative simulations are conducted against passive suspension (comprising elastic and damping elements) and conventional PID control. Results show that the proposed MFWA-FL approach significantly improves ride comfort, reducing vertical acceleration of the human body by up to 49.29% and seat suspension dynamic deflection by 12.50% under C-Class road excitation compared with the passive system. Under bump excitations, vertical acceleration is reduced by 43.03% and suspension deflection by 11.76%. These improvements effectively suppress vertical vibrations, minimize the risk of suspension bottoming, and highlight the potential of intelligent optimization-based control for enhancing FCCV reliability and passenger comfort. Full article

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

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32 pages, 7717 KB

Open AccessArticle

Trigger-Based PDCA Framework for Sustainable Grid Integration of Second-Life EV Batteries

by Ganna Kostenko and Artur Zaporozhets

World Electr. Veh. J. 2025, 16(10), 584; https://doi.org/10.3390/wevj16100584 - 17 Oct 2025

Viewed by 555

Abstract

Second-life electric vehicle batteries (SLBs) represent a promising asset for enhancing grid flexibility and advancing circular economy objectives in the power sector. This paper proposes a conceptual trigger-based PDCA (Plan–Do–Check–Act) framework for the sustainable grid integration of SLBs, enabling adaptive operational control across [...] Read more.

Second-life electric vehicle batteries (SLBs) represent a promising asset for enhancing grid flexibility and advancing circular economy objectives in the power sector. This paper proposes a conceptual trigger-based PDCA (Plan–Do–Check–Act) framework for the sustainable grid integration of SLBs, enabling adaptive operational control across diverse application scenarios. The framework combines lifecycle KPI monitoring, degradation and performance tracking, and economic feasibility assessment with trigger-driven dispatch logic. Technical, financial, and environmental indicators are systematically integrated into the four PDCA phases, providing a structured basis for adaptive management. To illustrate applicability, indicative KPI calculations are presented for three representative scenarios (HV Backup, RES Smoothing, and Frequency Regulation). These examples demonstrate how the framework supports scenario-based planning, performance evaluation, and decision-making under uncertainty. Compared with existing state-of-the-art approaches, which typically analyse technical or economic aspects in isolation, the proposed framework introduces a modular, multi-model architecture that aligns operational triggers with long-term sustainability goals. By embedding reuse-oriented strategies into an adaptive PDCA cycle, the study offers a clear and practical methodology for maximising SLB value while minimising degradation and environmental impacts. The framework provides a valuable reference framework for structured SLB deployment, supporting more resilient, cost-effective, and low-carbon energy systems. Full article

(This article belongs to the Special Issue Power and Energy Systems for E-Mobility, 2nd Edition)

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26 pages, 3121 KB

Open AccessArticle

Multidisciplinary Engineering Educational Programme Based on the Development of Photovoltaic Electric Vehicles

by Daniel Rosas-Cervantes and José Fernández-Ramos

World Electr. Veh. J. 2025, 16(10), 583; https://doi.org/10.3390/wevj16100583 - 17 Oct 2025

Viewed by 410

Abstract

This study compares two methodologies for organising the working groups of a multidisciplinary project-based learning programme aimed at strengthening students’ transversal skills. The subject of the project was the design and manufacture of prototypes of light electric vehicles powered exclusively by photovoltaic energy. [...] Read more.

This study compares two methodologies for organising the working groups of a multidisciplinary project-based learning programme aimed at strengthening students’ transversal skills. The subject of the project was the design and manufacture of prototypes of light electric vehicles powered exclusively by photovoltaic energy. The difference between the two methodologies was the way in which the tasks were distributed among the working groups. In the first method, each group of students specialised in one of the tasks and many of these tasks were carried out simultaneously. In the second method, the tasks were organised sequentially and all groups were involved in some part of them. The results have shown that the first method allows a higher net return on the students’ work and a greater reinforcement of the skills acquired in the project, while the second method requires a rather less complex organisation, enables a more balanced distribution of the students’ work, allows rapid progress in the acquisition of a greater number of practical skills and presents a greater opportunity for implementing multidisciplinary teaching. Full article

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

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22 pages, 6322 KB

Open AccessArticle

Position Sensorless Control of BLDCM Fed by FSTP Inverter with Capacitor Voltage Compensation

by Hanrui Wang, Lu Zhou, Qinghui Meng, Ying Xin, Xinmin Li and Chen Li

World Electr. Veh. J. 2025, 16(10), 582; https://doi.org/10.3390/wevj16100582 - 15 Oct 2025

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Abstract

Aiming at the commutation error in position sensorless control of brushless DC motors (BLDCMs) driven by four-switch three-phase (FSTP) inverters—caused by ignoring capacitor voltage fluctuations—this paper proposes a novel position sensorless control method based on voltage offset compensation. By independently performing PWM modulation [...] Read more.

Aiming at the commutation error in position sensorless control of brushless DC motors (BLDCMs) driven by four-switch three-phase (FSTP) inverters—caused by ignoring capacitor voltage fluctuations—this paper proposes a novel position sensorless control method based on voltage offset compensation. By independently performing PWM modulation on the switches of the non-capacitor-connected phases (Phase a and Phase b), the method suppresses three-phase current distortion. Meanwhile, it calculates the terminal voltages using switch signals and constructs a G(θ) function independent of the motor speed. Based on the voltage compensation amount derived in this paper, the influence of capacitor voltage fluctuations on this function is compensated. According to the relationship between the extreme value jump edges of the G(θ) function (after voltage compensation) and the commutation points, the accurate commutation signals required for motor operation are determined. The proposed strategy eliminates the need for filters, which not only avoids phase delay but also is suitable for motor rotor position estimation over a wider speed range. Experimental results show that compared with the uncompensated method, the average commutation error is reduced from approximately 18° to less than 3° electrical angle. Under different operating conditions, the proposed method can always obtain uniform commutation signals and exhibits strong robustness. Full article

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

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18 pages, 12732 KB

Open AccessArticle

Research on Deep Adaptive Clustering Method Based on Stacked Sparse Autoencoders for Concrete Truck Mixers Driving Conditions

by Ying Huang, Fachao Jiang and Haiming Xie

World Electr. Veh. J. 2025, 16(10), 581; https://doi.org/10.3390/wevj16100581 - 15 Oct 2025

Viewed by 289

Abstract

Existing standard driving conditions fail to accurately characterize the complex characteristics of heavy-duty commercial vehicles such as concrete truck mixers (CTMs), while traditional dimensionality reduction methods have strong empirical dependence and an insufficient ability to capture nonlinear relationships. To address these issues, a [...] Read more.

Existing standard driving conditions fail to accurately characterize the complex characteristics of heavy-duty commercial vehicles such as concrete truck mixers (CTMs), while traditional dimensionality reduction methods have strong empirical dependence and an insufficient ability to capture nonlinear relationships. To address these issues, a novel method for constructing typical composite driving conditions that integrates deep feature learning and adaptive clustering is proposed. Firstly, a vehicle data monitoring system is used to collect real-world driving data, and a data processing and filtering criterion specific to CTMs is designed to provide effective input for feature extraction. Then, stacked sparse autoencoders (SSAE) are employed to extract deep features from normalized driving data. Finally, the K-means++ algorithm is improved using a nearest neighbor effective index minimization strategy to construct an adaptive driving condition clustering model. Validation results based on a real-world dataset of 8779 driving condition segments demonstrate that this method enables the precise extraction of complex driving condition features and optimal cluster partitioning. It provides a reliable basis for subsequent research on typical composite driving conditions construction and energy management strategies for heavy-duty commercial vehicles. Full article

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

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24 pages, 5112 KB

Open AccessArticle

Power Management for V2G and V2H Operation Modes in Single-Phase PV/BES/EV Hybrid Energy System

by Chayakarn Saeseiw, Kosit Pongpri, Tanakorn Kaewchum, Sakda Somkun and Piyadanai Pachanapan

World Electr. Veh. J. 2025, 16(10), 580; https://doi.org/10.3390/wevj16100580 - 14 Oct 2025

Viewed by 476

Abstract

A multi-port conversion system that connects photovoltaic (PV) arrays, battery energy storage (BES), and an electric vehicle (EV) to a single-phase grid offers a flexible solution for smart homes. By integrating Vehicle-to-Grid (V2G) and Vehicle-to-Home (V2H) technologies, the system supports bidirectional energy flow, [...] Read more.

A multi-port conversion system that connects photovoltaic (PV) arrays, battery energy storage (BES), and an electric vehicle (EV) to a single-phase grid offers a flexible solution for smart homes. By integrating Vehicle-to-Grid (V2G) and Vehicle-to-Home (V2H) technologies, the system supports bidirectional energy flow, optimizing usage, improving grid stability, and supplying backup power. The proposed four-port converter consists of an interleaved bidirectional DC-DC converter for high-voltage BES, a bidirectional buck–boost DC-DC converter for EV charging and discharging, a DC-DC boost converter with MPPT for PV, and a grid-tied inverter. Its non-isolated structure ensures high efficiency, compact design, and fewer switches, making it suitable for residential applications. A state-of-charge (SoC)-based power management strategy coordinates operation among PV, BES, and EV in both on-grid and off-grid modes. It reduces reliance on EV energy when supporting V2G and V2H, while SoC balancing between BES and EV extends lifetime and lowers current stress. A 7.5 kVA system was simulated in MATLAB/Simulink to validate feasibility. Two scenarios were studied: PV, BES, and EV with V2G supporting the grid and PV, BES, and EV with V2H providing backup power in off-grid mode. Tests under PV fluctuations and load variations confirmed the effectiveness of the proposed design. The system exhibited a fast transient response of 0.05 s during grid-support operation and maintained stable voltage and frequency in off-grid mode despite PV and load fluctuations. Its protection scheme disconnected overloads within 0.01 s, while harmonic distortions in both cases remained modest and complied with EN50610 standards. Full article

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

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Graphical abstract

26 pages, 2887 KB

Open AccessArticle

Novel Method for Battery Design of Electric Vehicles Based on Longitudinal Dynamics, Range, and Charging Requirements

by Ralph Biller, Erik Ketzmerick, Stefan Mayr and Günther Prokop

World Electr. Veh. J. 2025, 16(10), 579; https://doi.org/10.3390/wevj16100579 - 14 Oct 2025

Viewed by 371

Abstract

VDI/VDE 2206 introduces the “V-Model”, a standard in the field of automotive development that uses systems engineering to derive requirements for (sub-)systems and components based on vehicle characteristics. These characteristics, which are directly experienced by drivers, are crucial in the concept phase, where [...] Read more.

VDI/VDE 2206 introduces the “V-Model”, a standard in the field of automotive development that uses systems engineering to derive requirements for (sub-)systems and components based on vehicle characteristics. These characteristics, which are directly experienced by drivers, are crucial in the concept phase, where virtual methods are increasingly applied. Regarding the battery electric vehicle’s energy storage, commonly a lithium-ion battery, vehicle metrics, especially for charging, range, and longitudinal dynamics, are of particular relevance. This publication will demonstrate a method to derive the requirements for the battery system based on those metrics. The core of the method is a static battery model, which considers the needed effects and dependencies in order to adequately represent the defined vehicle metrics, e.g., the battery’s open-circuit voltage and internal resistance. This paper also discusses the necessity of the relevant effects and dependencies and also why some of them can be ignored at this particular vehicle development stage. The result is a consistent method for requirement definition, from vehicle level to battery system level, applicable in the concept phase of the vehicle development process. Full article

(This article belongs to the Section Manufacturing)

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17 pages, 2150 KB

Open AccessReview

Circular Economy and Sustainability in Lithium-Ion Battery Development in China and the USA

by Daniel Yousefi and Azita Soleymani

World Electr. Veh. J. 2025, 16(10), 578; https://doi.org/10.3390/wevj16100578 - 14 Oct 2025

Viewed by 877

Abstract

The surge in electric vehicles (EVs) and renewable energy has made lithium-ion batteries (LIBs) critical to the global energy transition. This review examines how LIBs contribute to a circular economy, focusing on China and the United States as key actors shaping the battery [...] Read more.

The surge in electric vehicles (EVs) and renewable energy has made lithium-ion batteries (LIBs) critical to the global energy transition. This review examines how LIBs contribute to a circular economy, focusing on China and the United States as key actors shaping the battery value chain. We analyze technological advancements, market growth, supply chain dynamics, ESG risks, and strategies for recycling, reuse, and next-generation chemistries. China’s approach centers on vertical integration and scale, while the U.S. emphasizes innovation, policy incentives, and diversification. Despite progress, gaps remain in closed-loop systems, ethical sourcing, and supply chain resilience. Realizing sustainable battery growth will require coordinated efforts in technology, governance, and international collaboration to align resource efficiency with long-term environmental and economic goals. Full article

(This article belongs to the Special Issue Electric Vehicle Battery Pack and Electric Motor Sizing Methods)

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48 pages, 5345 KB

Open AccessSystematic Review

Optimizing Energy Consumption in Electric Vehicles: A Systematic and Bibliometric Review of Recent Advances

by Hind Tarout, Hanane Zaki, Amine Chahbouni, Elmehdi Ennajih and El Mustapha Louragli

World Electr. Veh. J. 2025, 16(10), 577; https://doi.org/10.3390/wevj16100577 - 13 Oct 2025

Cited by 1 | Viewed by 799

Abstract

Electric vehicles are key to sustainable mobility, but their limited range remains a major obstacle to widespread adoption. Extending driving distance requires optimizing energy use across subsystems. This study combines bibliometric mapping (2017–2024, Scopus) with a focused qualitative review to structure recent research. [...] Read more.

Electric vehicles are key to sustainable mobility, but their limited range remains a major obstacle to widespread adoption. Extending driving distance requires optimizing energy use across subsystems. This study combines bibliometric mapping (2017–2024, Scopus) with a focused qualitative review to structure recent research. Results highlight a strong emphasis on energy efficiency, with China leading due to its market size, industrial base, and supportive policies. Major research directions tied to range extension include energy storage, motion control, thermal regulation, cooperative driving, and grid interaction. Among these, hybrid energy storage systems and motor control stand out for their measurable impact and industrial relevance, while thermal management, regenerative braking, and systemic approaches (V2V and V2G) remain underexplored. Beyond mapping contributions, the study identifies ongoing gaps and calls for integrated strategies that combine electrical, thermal, and mechanical aspects. As EV adoption accelerates and battery demand increases, the findings emphasize the need for battery-aware, multi-objective energy management strategies. This synthesis provides a vital framework to guide future research and support the development of robust, integrated, and industry-ready solutions for optimizing EV energy use and extending driving range. Full article

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

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19 pages, 3065 KB

Open AccessArticle

Coordinated Control of Trajectory Tracking and Lateral Stability for Distributed Electric-Driven Buses

by Yuanjie Huang, Xian Zheng, Tongqun Han and Wenhao Tan

World Electr. Veh. J. 2025, 16(10), 576; https://doi.org/10.3390/wevj16100576 - 13 Oct 2025

Viewed by 344

Abstract

To resolve the inherent coupling conflict between trajectory tracking and lateral stability in distributed electric drive buses, this paper proposes a hierarchical cooperative control framework. A simplified two-degree-of-freedom (2-DOF) vehicle model is first established, and kinematically derived reference states for stable motion are [...] Read more.

To resolve the inherent coupling conflict between trajectory tracking and lateral stability in distributed electric drive buses, this paper proposes a hierarchical cooperative control framework. A simplified two-degree-of-freedom (2-DOF) vehicle model is first established, and kinematically derived reference states for stable motion are computed. At the upper level, a model predictive controller (MPC) generates real-time steering commands while explicitly minimizing lateral tracking error. At the lower level, a proportional integral derivative (PID)-based roll moment controller and a linear quadratic regulator (LQR)-based direct yaw moment controller are designed, with four-wheel torque distribution achieved via quadratic programming subject to friction circle and vertical load constraints. Co-simulation results using TruckSim and MATLAB/Simulink demonstrate that, during high-speed single-lane-change maneuvers, peak lateral error is reduced by 11.59–18.09%, and root-mean-square (RMS) error by 8.67–14.77%. Under medium-speed double-lane-change conditions, corresponding reductions of 3.85–12.16% and 4.48–11.33% are achieved, respectively. These results fully validate the effectiveness of the proposed strategy. Compared with the existing MPC–direct yaw moment control (DYC) decoupled control framework, the coordinated control strategy proposed in this paper achieves the optimal trade-off between trajectory tracking and lateral stability while maintaining the quadratic programming solution delay below 0.5 milliseconds. Full article

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

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21 pages, 1219 KB

Open AccessArticle

Innovation Networks in the New Energy Vehicle Industry: A Dual Perspective of Collaboration Between Supply Chain and Executive Networks

by Lixiang Chen and Wenting Wang

World Electr. Veh. J. 2025, 16(10), 575; https://doi.org/10.3390/wevj16100575 - 11 Oct 2025

Viewed by 558

Abstract

Driven by the global energy transition and the pursuit of dual carbon goals (carbon peaking and carbon neutrality), the innovation network of the new energy vehicle (NEV) industry, composed of enterprises, universities, and research institutes, has become a key driver of sustainable industrial [...] Read more.

Driven by the global energy transition and the pursuit of dual carbon goals (carbon peaking and carbon neutrality), the innovation network of the new energy vehicle (NEV) industry, composed of enterprises, universities, and research institutes, has become a key driver of sustainable industrial development. The evolution of this network is jointly shaped by both supply chain networks (SCNs) and executive networks (ENs), representing formal and informal relational structures, respectively. To systematically explore these dynamics, this study analyzes panel data from Chinese A-share-listed NEV firms covering the period 2003–2024. Employing social network analysis (SNA) and Quadratic Assignment Procedure (QAP) regression, we investigate how SCNs and ENs influence the formation and structural evolution of innovation networks. The results reveal that although all three networks exhibit sparse connectivity, they differ substantially in their structural characteristics. Moreover, both SCNs and ENs have statistically significant positive effects on innovation network development. Building on these findings, we propose an integrative policy framework to strategically enhance the innovation ecosystem of China’s NEV industry. This study not only provides practical guidance for fostering collaborative innovation but also offers theoretical insights by integrating formal and informal network perspectives, thereby advancing the understanding of multi-network interactions in complex industrial systems. Full article

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32 pages, 1311 KB

Open AccessReview

Systemic Integration of EV and Autonomous Driving Technologies: A Study of China’s Intelligent Mobility Transition

by Jiyong Gao, Yi Qiu and Zejian Chen

World Electr. Veh. J. 2025, 16(10), 574; https://doi.org/10.3390/wevj16100574 - 11 Oct 2025

Viewed by 1065

Abstract

This paper presents a pioneering and novel analysis of the synergistic relationship between China’s leadership in electric vehicle (EV) adoption and the rapid advancement of autonomous driving (AD) technologies within the nation’s mobility ecosystem. Challenging the conventional view of electrification as a parallel [...] Read more.

This paper presents a pioneering and novel analysis of the synergistic relationship between China’s leadership in electric vehicle (EV) adoption and the rapid advancement of autonomous driving (AD) technologies within the nation’s mobility ecosystem. Challenging the conventional view of electrification as a parallel trend, this study introduces a new perspective by demonstrating how EV infrastructure serves as a fundamental enabler of autonomy, providing the necessary high-voltage architectures for critical AD functions like real-time sensor fusion and over-the-air updates. In doing so, it addresses the central research question: How does large-scale electrification influence the architecture, deployment, and safety development of autonomous driving vehicles, particularly in the context of China’s intelligent mobility ecosystem? Through technical analysis and industry examples, the paper offers original contributions by illustrating how EV-driven platforms overcome the inherent limitations of internal combustion engine systems, enhancing autonomous execution and system reliability. Furthermore, this research provides novel insights into China’s unique public–private innovation ecosystem, highlighting the role of vertically integrated startups and cross-sector coordination in driving AD development. By analyzing these previously overlooked systemic interactions, the paper posits that China’s EV dominance strategically amplifies its autonomous vehicle ambitions, positioning the nation to lead the next generation of intelligent transportation systems. Full article

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

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67 pages, 11384 KB

Open AccessReview

Powertrain in Battery Electric Vehicles (BEVs): Comprehensive Review of Current Technologies and Future Trends Among Automakers

by Ernest Ozoemela Ezugwu, Indranil Bhattacharya, Adeloye Ifeoluwa Ayomide, Mary Vinolisha Antony Dhason, Babatunde Damilare Soyoye and Trapa Banik

World Electr. Veh. J. 2025, 16(10), 573; https://doi.org/10.3390/wevj16100573 - 10 Oct 2025

Viewed by 1488

Abstract

Battery Electric Vehicles (BEVs) technology is rapidly emerging as the cornerstone of sustainable transportation, driven by advancements in battery technology, power electronics, and modern drivetrains. This paper presents a comprehensive review of current and next-generation BEV powertrain architectures, focusing on five key subsystems: [...] Read more.

Battery Electric Vehicles (BEVs) technology is rapidly emerging as the cornerstone of sustainable transportation, driven by advancements in battery technology, power electronics, and modern drivetrains. This paper presents a comprehensive review of current and next-generation BEV powertrain architectures, focusing on five key subsystems: battery energy storage system, electric propulsion motors, energy management systems, power electronic converters, and charging infrastructure. The review traces the evolution of battery technology from conventional lithium-ion to solid-state chemistries and highlights the critical role of battery management systems in ensuring optimal state of charge, health, and safety. Recent innovations by leading automakers are examined, showcasing advancements in cell formats, motor designs, and thermal management for enhanced range and performance. The role of power electronics and the integration of AI-driven strategies for vehicle control and vehicle-to-grid (V2G) are analyzed. Finally, the paper identifies ongoing research gaps in system integration, standardization, and advanced BMS solutions. This review provides a comprehensive roadmap for innovation, aiming to guide researchers and industry stakeholders in accelerating the adoption and sustainable advancement of BEV technologies. Full article

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

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14 pages, 1531 KB

Open AccessArticle

State of Charge (SoC) Accurate Estimation Using Different Models of LSTM

by Fehr Hassan, Mohamed El-Bably and Roaa I. Mubarak

World Electr. Veh. J. 2025, 16(10), 572; https://doi.org/10.3390/wevj16100572 - 10 Oct 2025

Viewed by 493

Abstract

Accurately estimating the State of Charge (SoC) is essential for optimal battery charge control and predicting the operational range of electric vehicles. The precision of SoC estimation directly influences these vehicles’ range and safety. However, achieving accurate SoC estimation is challenging due to [...] Read more.

Accurately estimating the State of Charge (SoC) is essential for optimal battery charge control and predicting the operational range of electric vehicles. The precision of SoC estimation directly influences these vehicles’ range and safety. However, achieving accurate SoC estimation is challenging due to environmental variations, temperature changes, and electromagnetic interference. Numerous technologies rely on Machine Learning (ML) and Artificial Neural Networks (ANN). The proposed model employs two or more cascaded Long Short-Term Memory (LSTM) networks, which have effectively reduced the Mean Square Error (MSE). Additionally, other models such as Nonlinear Auto Regressive models with exogenous input neural networks (NARX) combined with LSTM, and standard LSTM models have been simulated. In this research a model has been presented with reduced Root Mean Square Error (RMSE) compared to a LSTM by 78% and has reduced the RMSE compared to NARX with LSTM by 47%. Full article

(This article belongs to the Section Storage Systems)

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19 pages, 6362 KB

Open AccessArticle

Micro-Platform Verification for LiDAR SLAM-Based Navigation of Mecanum-Wheeled Robot in Warehouse Environment

by Yue Wang, Ying Yu Ye, Wei Zhong, Bo Lin Gao, Chong Zhang Mu and Ning Zhao

World Electr. Veh. J. 2025, 16(10), 571; https://doi.org/10.3390/wevj16100571 - 8 Oct 2025

Viewed by 502

Abstract

Path navigation for mobile robots critically determines the operational efficiency of warehouse logistics systems. However, the current QR (Quick Response) code path navigation for warehouses suffers from low operational efficiency and poor dynamic adaptability in complex dynamic environments. This paper introduces a deep [...] Read more.

Path navigation for mobile robots critically determines the operational efficiency of warehouse logistics systems. However, the current QR (Quick Response) code path navigation for warehouses suffers from low operational efficiency and poor dynamic adaptability in complex dynamic environments. This paper introduces a deep reinforcement learning and hybrid-algorithm SLAM (Simultaneous Localization and Mapping) path navigation method for Mecanum-wheeled robots, validated with an emphasis on dynamic adaptability and real-time performance. Based on the Gazebo warehouse simulation environment, the TD3 (Twin Deep Deterministic Policy Gradient) path planning method was established for offline training. Then, the Astar-Time Elastic Band (TEB) hybrid path planning algorithm was used to conduct experimental verification in static and dynamic real-world scenarios. Finally, experiments show that the TD3-based path planning for mobile robots makes effective decisions during offline training in the simulation environment, while Astar-TEB accurately completes path planning and navigates around both static and dynamic obstacles in real-world scenarios. Therefore, this verifies the feasibility and effectiveness of the proposed SLAM path navigation for Mecanum-wheeled mobile robots on a miniature warehouse platform. Full article

(This article belongs to the Special Issue Research on Intelligent Vehicle Path Planning Algorithm)

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26 pages, 3051 KB

Open AccessArticle

Impact of Massive Electric Vehicle Penetration on Quito’s 138 kV Distribution System: Probabilistic Analysis for a Sustainable Energy Transition

by Paul Andrés Masache, Washington Rodrigo Freire, Leandro Gabriel Corrales, Ana Lucia Mañay and Pablo Andrés Reyes

World Electr. Veh. J. 2025, 16(10), 570; https://doi.org/10.3390/wevj16100570 - 5 Oct 2025

Viewed by 734

Abstract

The study evaluates the impact of massive electric vehicle (EV) penetration on Quito’s 138 kV distribution system in Ecuador, employing a probabilistic approach to support a sustainable energy transition. The rapid adoption of EVs, as projected by Ecuador’s National Electromobility Strategy, poses significant [...] Read more.

The study evaluates the impact of massive electric vehicle (EV) penetration on Quito’s 138 kV distribution system in Ecuador, employing a probabilistic approach to support a sustainable energy transition. The rapid adoption of EVs, as projected by Ecuador’s National Electromobility Strategy, poses significant challenges to the capacity and reliability of the city’s electrical infrastructure. The objective is to analyze the system’s response to increased EV load and assess its readiness for this scenario. A methodology integrating dynamic battery modeling, Monte Carlo simulations, and power flow analysis was employed, evaluating two penetration levels: 800 and 25,000 EVs, under homogeneous and non-homogeneous distribution scenarios. The results indicate that while the system can handle moderate penetration, high penetration levels lead to overloads in critical lines, such as L10–15 and L11–5, compromising normal system operation. It is concluded that specific infrastructure upgrades and the implementation of smart charging strategies are necessary to mitigate operational risks. This approach provides a robust framework for effective planning of EV integration into the system, contributing key insights for a transition toward sustainable mobility. Full article

(This article belongs to the Special Issue Impact of Electric Vehicles on Power Systems and Society)

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21 pages, 3530 KB

Open AccessArticle

Discrete Element Method-Based Analysis of Tire-Soil Mechanics for Electric Vehicle Traction on Unstructured Sandy Terrains

by Chenyu Hu, Bo Li, Shaoyi Bei and Jingyi Gu

World Electr. Veh. J. 2025, 16(10), 569; https://doi.org/10.3390/wevj16100569 - 3 Oct 2025

Viewed by 458

Abstract

In order to tackle the issues of poor mobility and unstable traction of electric vehicles on sandy landscapes, this research develops a high-accuracy numerical model for wheel–sand interaction relying on the Discrete Element Method (DEM). An innovative parameter calibration procedure is proposed herein, [...] Read more.

In order to tackle the issues of poor mobility and unstable traction of electric vehicles on sandy landscapes, this research develops a high-accuracy numerical model for wheel–sand interaction relying on the Discrete Element Method (DEM). An innovative parameter calibration procedure is proposed herein, which optimizes the sand contact parameters. This reduces the error between the simulated and measured angles of repose to merely 1.2% and substantially improves the model’s reliability. The model was then used to systematically compare the performance of a 205/55 R16 slick tire with a treaded tire on sand. Simulations demonstrate that at a 30% slip ratio, the treaded tire exhibited significantly higher traction and greater sinkage than the slick tire. This indicates that tread patterns enhance traction mechanically by increasing the contact area and promoting shear deformation of the sand. The trends of traction with slip ratio and the corresponding sand flow patterns showed excellent agreement with experimental observations, which validated the simulation approach. This research provides an efficient and accurate tool for evaluating tire-sand interaction, providing critical support for the design and control of electric vehicles on complex terrains. Full article

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35 pages, 1513 KB

Open AccessArticle

Enhancing Thermal Comfort and Efficiency in Fuel Cell Trucks: A Predictive Control Approach for Cabin Heating

by Tarik Hadzovic, Achim Kampker, Heiner Hans Heimes, Julius Hausmann, Maximilian Bayerlein and Manuel Concha Cardiel

World Electr. Veh. J. 2025, 16(10), 568; https://doi.org/10.3390/wevj16100568 - 2 Oct 2025

Viewed by 463

Abstract

Fuel cell trucks are a promising solution to reduce the disproportionately high greenhouse gas emissions of heavy-duty long-haul transportation. However, unlike conventional diesel vehicles, they lack combustion engine waste heat for cabin heating. As a result, electric heaters are often employed, which increase [...] Read more.

Fuel cell trucks are a promising solution to reduce the disproportionately high greenhouse gas emissions of heavy-duty long-haul transportation. However, unlike conventional diesel vehicles, they lack combustion engine waste heat for cabin heating. As a result, electric heaters are often employed, which increase auxiliary energy consumption and reduce driving range. To address this challenge, advanced control strategies are needed to improve heating efficiency while maintaining passenger comfort. This study proposes and validates a methodology for implementing Model Predictive Control (MPC) in the cabin heating system of a fuel cell truck. Vehicle experiments were conducted to characterize dynamic heating behavior, passenger comfort indices, and to provide validation data for the mathematical models. Based on these models, an MPC strategy was developed in a Model-in-the-Loop simulation environment. The proposed approach achieves energy savings of up to 8.1% compared with conventional control using purely electric heating, and up to 21.7% when cabin heating is coupled with the medium-temperature cooling circuit. At the same time, passenger comfort is maintained within the desired range (PMV within ±0.5 under typical winter conditions). The results demonstrate the potential of MPC to enhance the energy efficiency of fuel cell trucks. The methodology presented provides a validated foundation for the further development of predictive thermal management strategies in heavy-duty zero-emission vehicles. Full article

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

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17 pages, 1318 KB

Open AccessArticle

Robust 3D Object Detection in Complex Traffic via Unified Feature Alignment in Bird’s Eye View

by Ajian Liu, Yandi Zhang, Huichao Shi and Juan Chen

World Electr. Veh. J. 2025, 16(10), 567; https://doi.org/10.3390/wevj16100567 - 2 Oct 2025

Viewed by 431

Abstract

Reliable three-dimensional (3D) object detection is critical for intelligent vehicles to ensure safety in complex traffic environments, and recent progress in multi-modal sensor fusion, particularly between LiDAR and camera, has advanced environment perception in urban driving. However, existing approaches remain vulnerable to occlusions [...] Read more.

Reliable three-dimensional (3D) object detection is critical for intelligent vehicles to ensure safety in complex traffic environments, and recent progress in multi-modal sensor fusion, particularly between LiDAR and camera, has advanced environment perception in urban driving. However, existing approaches remain vulnerable to occlusions and dense traffic, where depth estimation errors, calibration deviations, and cross-modal misalignment are often exacerbated. To overcome these limitations, we propose BEVAlign, a local–global feature alignment framework designed to generate unified BEV representations from heterogeneous sensor modalities. The framework incorporates a Local Alignment (LA) module that enhances camera-to-BEV view transformation through graph-based neighbor modeling and dual-depth encoding, mitigating local misalignment from depth estimation errors. To further address global misalignment in BEV representations, we present the Global Alignment (GA) module comprising a bidirectional deformable cross-attention (BDCA) mechanism and CBR blocks. BDCA employs dual queries from LiDAR and camera to jointly predict spatial sampling offsets and aggregate features, enabling bidirectional alignment within the BEV domain. The stacked CBR blocks then refine and integrate the aligned features into unified BEV representations. Experiment on the nuScenes benchmark highlights the effectiveness of BEVAlign, which achieves 71.7% mAP, outperforming BEVFusion by 1.5%. Notably, it achieves strong performance on small and occluded objects, particularly in dense traffic scenarios. These findings provide a basis for advancing cooperative environment perception in next-generation intelligent vehicle systems. Full article

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

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15 pages, 4024 KB

Open AccessArticle

Comparative Analysis of Efficiency and Harmonic Generation in Multiport Converters: Study of Two Operating Conditions

by Francisco J. Arizaga, Juan M. Ramírez, Janeth A. Alcalá, Julio C. Rosas-Caro and Armando G. Rojas-Hernández

World Electr. Veh. J. 2025, 16(10), 566; https://doi.org/10.3390/wevj16100566 - 2 Oct 2025

Viewed by 359

Abstract

This study presents a comparative analysis of efficiency and harmonic generation in Triple Active Bridge (TAB) converters under two operating configurations: Case I, with one input source and two loads, and Case II, with two input sources and one load. Two modulation strategies, [...] Read more.

This study presents a comparative analysis of efficiency and harmonic generation in Triple Active Bridge (TAB) converters under two operating configurations: Case I, with one input source and two loads, and Case II, with two input sources and one load. Two modulation strategies, Single-Phase Shift (SPS) and Dual-Phase Shift (DPS), are evaluated through frequency-domain modeling and simulations performed in MATLAB/Simulink. The analysis is complemented by experimental validation on a laboratory prototype. The results show that DPS reduces harmonic amplitudes, decreases conduction losses, and improves output waveform quality, leading to higher efficiency compared to SPS. Harmonic current spectra and total harmonic distortion (THD) are analyzed to quantify the impact of each modulation method. The findings highlight that DPS is more suitable for applications requiring stable power transfer and improved efficiency, such as renewable energy systems, electric vehicles, and multi-source DC microgrids. Full article

(This article belongs to the Section Power Electronics Components)

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26 pages, 6412 KB

Open AccessArticle

Optimized Charging Strategy for Lithium-Ion Battery Based on Improved MFO Algorithm and Multi-State Coupling Model

by Shuangming Duan and Linglong Chen

World Electr. Veh. J. 2025, 16(10), 565; https://doi.org/10.3390/wevj16100565 - 2 Oct 2025

Viewed by 481

Abstract

In lithium-ion battery charging, balancing charging speed with efficiency and state of health (SOH) is paramount. First, a multi-state electric-thermal-aging coupling model was developed to accurately reflect battery operating conditions. Second, a voltage-based multi-stage constant current-constant voltage (VMCC-CV) strategy was implemented, incorporating an [...] Read more.

In lithium-ion battery charging, balancing charging speed with efficiency and state of health (SOH) is paramount. First, a multi-state electric-thermal-aging coupling model was developed to accurately reflect battery operating conditions. Second, a voltage-based multi-stage constant current-constant voltage (VMCC-CV) strategy was implemented, incorporating an innovative V-SOC-R_int conversion mechanism—integrating voltage, state of charge (SOC), and internal resistance—to effectively mitigate thermal buildup during transitions. To optimize the VMCC-CV currents, an innovative enhancement was applied to the moth-flame optimization (MFO) algorithm, demonstrating superior performance over its traditional counterpart across diverse charging scenarios. Finally, three practical strategies were devised: rapid charging, multi-objective balanced charging, and enhanced safety performance charging. Relative to the manufacturer’s 0.75 C-CCCV protocol, the balanced strategy significantly accelerates charging, reducing time by 34.11%, while sustaining 93.54% efficiency and limiting SOH degradation to 0.006856%. Compared to conventional CCCV methods, the proposed approach offers greater versatility and applicability in varied real-world scenarios. Full article

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

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23 pages, 5971 KB

Open AccessArticle

Improved MNet-Atten Electric Vehicle Charging Load Forecasting Based on Composite Decomposition and Evolutionary Predator–Prey and Strategy

by Xiaobin Wei, Qi Jiang, Huaitang Xia and Xianbo Kong

World Electr. Veh. J. 2025, 16(10), 564; https://doi.org/10.3390/wevj16100564 - 2 Oct 2025

Viewed by 389

Abstract

In the context of low carbon, achieving accurate forecasting of electrical energy is critical for power management with the continuous development of power systems. For the sake of improving the performance of load forecasting, an improved MNet-Atten electric vehicle charging load forecasting based [...] Read more.

In the context of low carbon, achieving accurate forecasting of electrical energy is critical for power management with the continuous development of power systems. For the sake of improving the performance of load forecasting, an improved MNet-Atten electric vehicle charging load forecasting based on composite decomposition and the evolutionary predator–prey and strategy model is proposed. In this light, through the data decomposition theory, each subsequence is processed using complementary ensemble empirical mode decomposition and filters out high-frequency white noise by using singular value decomposition based on matrix operation, which improves the anti-interference ability and computational efficiency of the model. In the model construction stage, the MNet-Atten prediction model is developed and constructed. The convolution module is used to mine the local dependencies of the sequences, and the long term and short-term features of the data are extracted through the loop and loop skip modules to improve the predictability of the data itself. Furthermore, the evolutionary predator and prey strategy is used to iteratively optimize the learning rate of the MNet-Atten for improving the forecasting performance and convergence speed of the model. The autoregressive module is used to enhance the ability of the neural network to identify linear features and improve the prediction performance of the model. Increasing temporal attention to give more weight to important features for global and local linkage capture. Additionally, the electric vehicle charging load data in a certain region, as an example, is verified, and the average value of 30 running times of the combined model proposed is 117.3231 s, and the correlation coefficient PCC of the CEEMD-SVD-EPPS-MNet-Atten model is closer to 1. Furthermore, the CEEMD-SVD-EPPS-MNet-Atten model has the lowest MAPE, RMSE, and PCC. The results show that the model in this paper can better extract the characteristics of the data, improve the modeling efficiency, and have a high data prediction accuracy. Full article

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

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19 pages, 7379 KB

Open AccessArticle

Criterion Circle-Optimized Hybrid Finite Element–Statistical Energy Analysis Modeling with Point Connection Updating for Acoustic Package Design in Electric Vehicles

by Jiahui Li, Ti Wu and Jintao Su

World Electr. Veh. J. 2025, 16(10), 563; https://doi.org/10.3390/wevj16100563 - 2 Oct 2025

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Abstract

This research is based on the acoustic package design of new energy vehicles, investigating the application of the hybrid Finite Element–Statistical Energy Analysis (FE-SEA) model in predicting the high-frequency dynamic response of automotive structures, with a focus on the modeling and correction methods [...] Read more.

This research is based on the acoustic package design of new energy vehicles, investigating the application of the hybrid Finite Element–Statistical Energy Analysis (FE-SEA) model in predicting the high-frequency dynamic response of automotive structures, with a focus on the modeling and correction methods for hybrid point connections. New energy vehicles face unique acoustic challenges due to the special nature of their power systems and operating conditions, such as high-frequency noise from electric motors and electronic devices, wind noise, and road noise at low speeds, which directly affect the vehicle’s ride comfort. Therefore, optimizing the acoustic package design of new energy vehicles to reduce in-cabin noise and improve acoustic quality is an important issue in automotive engineering. In this context, this study proposes an improved point connection correction factor by optimizing the division range of the decision circle. The factor corrects the dynamic stiffness of point connections based on wave characteristics, aiming to improve the analysis accuracy of the hybrid FE-SEA model and enhance its ability to model boundary effects. Simulation results show that the proposed method can effectively improve the model’s analysis accuracy, reduce the degrees of freedom in analysis, and increase efficiency, providing important theoretical support and reference for the acoustic package design and NVH performance optimization of new energy vehicles. 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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27 pages, 4866 KB

Open AccessArticle

An Intelligent Control Framework for High-Power EV Fast Charging via Contrastive Learning and Manifold-Constrained Optimization

by Hao Tian, Tao Yan, Guangwu Dai, Min Wang and Xuejian Zhao

World Electr. Veh. J. 2025, 16(10), 562; https://doi.org/10.3390/wevj16100562 - 1 Oct 2025

Viewed by 279

Abstract

To address the complex trade-offs among charging efficiency, battery lifespan, energy efficiency, and safety in high-power electric vehicle (EV) fast charging, this paper presents an intelligent control framework based on contrastive learning and manifold-constrained multi-objective optimization. A multi-physics coupled electro-thermal-chemical model is formulated [...] Read more.

To address the complex trade-offs among charging efficiency, battery lifespan, energy efficiency, and safety in high-power electric vehicle (EV) fast charging, this paper presents an intelligent control framework based on contrastive learning and manifold-constrained multi-objective optimization. A multi-physics coupled electro-thermal-chemical model is formulated as a Mixed-Integer Nonlinear Programming (MINLP) problem, incorporating both continuous and discrete decision variables—such as charging power and cooling modes—into a unified optimization framework. An environment-adaptive optimization strategy is also developed. To enhance learning efficiency and policy safety, a contrastive learning–enhanced policy gradient (CLPG) algorithm is proposed to distinguish between high-quality and unsafe charging trajectories. A manifold-aware action generation network (MAN) is further introduced to enforce dynamic safety constraints under varying environmental and battery conditions. Simulation results demonstrate that the proposed framework reduces charging time to 18.3 min—47.7% faster than the conventional CC–CV method—while achieving 96.2% energy efficiency, 99.7% capacity retention, and zero safety violations. The framework also exhibits strong adaptability across wide temperature (−20 °C to 45 °C) and aging (SOH down to 70%) conditions, with real-time inference speed (6.76 ms) satisfying deployment requirements. This study provides a safe, efficient, and adaptive solution for intelligent high-power EV fast-charging. Full article

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

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19 pages, 3159 KB

Open AccessArticle

Optimizing Traffic Accident Severity Prediction with a Stacking Ensemble Framework

by Imad El Mallahi, Jamal Riffi, Hamid Tairi, Nikola S. Nikolov, Mostafa El Mallahi and Mohamed Adnane Mahraz

World Electr. Veh. J. 2025, 16(10), 561; https://doi.org/10.3390/wevj16100561 - 1 Oct 2025

Viewed by 485

Abstract

Road traffic crashes (RTCs) have emerged as a major global cause of fatalities, with the number of accident-related deaths rising rapidly each day. To mitigate this issue, it is essential to develop early prediction methods that help drivers and riders understand accident statistics [...] Read more.

Road traffic crashes (RTCs) have emerged as a major global cause of fatalities, with the number of accident-related deaths rising rapidly each day. To mitigate this issue, it is essential to develop early prediction methods that help drivers and riders understand accident statistics relevant to their region. These methods should consider key factors such as speed limits, compliance with traffic signs and signals, pedestrian crossings, right-of-way rules, weather conditions, driver negligence, fatigue, and the impact of excessive speed on RTC occurrences. Raising awareness of these factors enables individuals to exercise greater caution, thereby contributing to accident prevention. A promising approach to improving road traffic accident severity classification is the stacking ensemble method, which leverages multiple machine learning models. This technique addresses challenges such as imbalanced datasets and high-dimensional features by combining predictions from various base models into a meta-model, ultimately enhancing classification accuracy. The ensemble approach exploits the diverse strengths of different models, capturing multiple aspects of the data to improve predictive performance. The effectiveness of stacking depends on the careful selection of base models with complementary strengths, ensuring robust and reliable predictions. Additionally, advanced feature engineering and selection techniques can further optimize the model’s performance. Within the field of artificial intelligence, various machine learning (ML) techniques have been explored to support decision making in tackling RTC-related issues. These methods aim to generate precise reports and insights. However, the stacking method has demonstrated significantly superior performance compared to existing approaches, making it a valuable tool for improving road safety. Full article

(This article belongs to the Special Issue Advanced Design Theory and Methods of Intelligent Electric Vehicle Chassis)

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37 pages, 6543 KB

Open AccessArticle

Efficient Drone Data Collection in WSNs: ILP and mTSP Integration with Quality Assessment

by Gregory Gasteratos and Ioannis Karydis

World Electr. Veh. J. 2025, 16(10), 560; https://doi.org/10.3390/wevj16100560 - 1 Oct 2025

Viewed by 289

Abstract

The proliferation of wireless sensor networks in remote and inaccessible areas demands efficient data collection approaches that minimize energy consumption while ensuring comprehensive coverage. Traditional data retrieval methods face significant challenges when sensors are sparsely distributed across extensive areas, particularly in scenarios where [...] Read more.

The proliferation of wireless sensor networks in remote and inaccessible areas demands efficient data collection approaches that minimize energy consumption while ensuring comprehensive coverage. Traditional data retrieval methods face significant challenges when sensors are sparsely distributed across extensive areas, particularly in scenarios where direct sensor access is impractical due to terrain constraints or operational limitations. This research addresses these challenges through a novel hybrid optimization framework that combines integer linear programming (ILP) with multiple traveling salesperson problem (mTSP) algorithms for drone-based data collection in wireless sensor networks (WSNs). The methodology employs a two-phase approach, where ILP optimally determines strategic access point locations for sensor clustering based on communication capabilities, followed by mTSP optimization to generate efficient inter-AP flight trajectories rather than individual sensor visits. Comprehensive simulations across diverse network configurations and drone quantities demonstrate consistent performance improvements, with travel distance reductions reaching 32% compared to conventional mTSP implementations. Comparative evaluation against established clustering algorithms including Voronoi, DBSCAN, Constrained K-Means, Graph-Based clustering, and Greedy Circle Packing confirms that ILP consistently achieves optimal access point allocation while maintaining superior routing efficiency. Additionally, a novel quality assessment metric quantifies sensor grouping effectiveness, revealing that ILP-based clustering advantages become increasingly pronounced with higher sensor densities, providing substantial operational benefits for large-scale wireless sensor network deployments. Full article

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

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22 pages, 4496 KB

Open AccessArticle

Sliding Mode Controller Tuning Using Nature-Inspired Optimization for Induction Motor: EV Application

by Youssef Dhieb, Walid Ayadi, Farhan Hameed Malik, Soumya Ambramoli, Fawwaz Alkhatib and Moez Ghariani

World Electr. Veh. J. 2025, 16(10), 559; https://doi.org/10.3390/wevj16100559 - 1 Oct 2025

Viewed by 361

Abstract

The finite element model (FEM) for induction motors (IM) was developed and validated through experimental testing. The validated FEM provides a reliable basis for further optimization of the electric machine. A strong sliding mode technique, in conjunction with field-oriented control (FOC), is proposed [...] Read more.

The finite element model (FEM) for induction motors (IM) was developed and validated through experimental testing. The validated FEM provides a reliable basis for further optimization of the electric machine. A strong sliding mode technique, in conjunction with field-oriented control (FOC), is proposed for speed control of the IM. The sliding mode controller ensures steady functioning in the face of ambiguities and disruptions, while FOC enables precise control of the motor’s magnetic field. This combination enhances both the efficiency and accuracy of speed control in IM, making it a valuable tool for industrial applications. The proposed sliding mode control (SMC) was fine-tuned using the advantages produced by the ant colony optimization algorithm. This approach aids in resolving issues and delivers optimal speed and field responses. Simulation and experimental results demonstrate the effectiveness of the proposed approach. The optimized induction motor achieved a 28% reduction in rotor Joule losses, resulting in improved energy efficiency. Additionally, using Ant Colony Optimization to adjust the SMC parameters led to a 99.74% reduction in speed tracking error and a 99.59% reduction in flux error compared to traditional manual tuning. These substantial improvements confirm the superiority of the proposed method for high-performance and energy-efficient electric vehicle applications. Full article

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

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World Electr. Veh. J., Volume 16, Issue 10 (October 2025) – 44 articles

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