World Electric Vehicle Journal

49 pages, 1996 KB

Open AccessArticle

An Empirical Analysis of the Effectiveness of Local Industrial Policies for China’s New Energy Vehicle Sector

by Chunning Wang, Yingchong Xie, Yifen Yin, Jingwen Cai and Haoqian Hu

World Electr. Veh. J. 2025, 16(9), 519; https://doi.org/10.3390/wevj16090519 (registering DOI) - 12 Sep 2025

Despite China’s success in its new energy vehicle (NEV) transition, significant regional imbalances persist, raising the question of why provincial policy effectiveness is so context-dependent. To investigate this, this study develops a novel framework to measure policy “quality” and “style”, systematically quantifying 2455 [...] Read more.

Despite China’s success in its new energy vehicle (NEV) transition, significant regional imbalances persist, raising the question of why provincial policy effectiveness is so context-dependent. To investigate this, this study develops a novel framework to measure policy “quality” and “style”, systematically quantifying 2455 provincial policy documents from 2013 to 2023. Our empirical analysis reveals that policy quality—encompassing its authoritativeness, instrument strength, and resource commitment—is a far more decisive determinant of effectiveness than sheer policy quantity. We identify three primary policy styles with distinct impacts: substantive-driving policies are crucial for stimulating market demand, whereas coordinative-programmatic policies are more effective in guiding industrial supply, revealing a significant goal-mismatch. Conversely, high-level authoritative policies can unexpectedly inhibit infrastructure development. Crucially, the study finds that provincial policies act more as “catalysts” than “creators”, their effectiveness being highly contingent on local economic, fiscal, and industrial fundamentals. The findings of this research offer direct implications for policymaking: decision-makers should shift their focus from pursuing policy quantity to enhancing policy quality and design targeted, “precision-irrigation” policy instrument portfolios tailored to the specific contexts and development objectives (e.g., promoting sales or guiding production) of different regions. Full article

26 pages, 1905 KB

Open AccessArticle

Architecture and Pricing Strategies for Commercial EV Battery Swapping—Dual-Market Cournot Model and Degradation-Sensitive Regulated Framework

by Soham Ghosh

World Electr. Veh. J. 2025, 16(9), 518; https://doi.org/10.3390/wevj16090518 (registering DOI) - 12 Sep 2025

Abstract

The global electric vehicle (EV) market has experienced sustained growth over the last decade; however, adoption within the commercial EV segment remains comparatively sluggish. This disparity is driven by three primary factors: the intrinsic limitations of lithium-ion battery chemistry, which imposes constraints on [...] Read more.

The global electric vehicle (EV) market has experienced sustained growth over the last decade; however, adoption within the commercial EV segment remains comparatively sluggish. This disparity is driven by three primary factors: the intrinsic limitations of lithium-ion battery chemistry, which imposes constraints on charge–discharge cycling, excessive charging durations for large battery packs used in long-haul semi-trucks, and diminished charging effectiveness under cold weather conditions, which further extends downtime and increases grid demand. To address these operational and infrastructural challenges, this article proposes a novel battery swapping station layout with ‘design-integrated safety’ features, enabling rapid battery replacement while ensuring compliance with safety codes and standards. Two complementary pricing strategies are developed for deployment under differing market structures. The first is a Cournot competition, applicable to deregulated environments, where firms strategically allocate battery inventory between EV swapping services and participation in a secondary energy market. As an extension of the Cournot competition model, the profit functions are analytically derived for a duopoly in which one firm engages in dual markets, enabling assessment of equilibrium outcomes under competitive conditions. The second strategy is a degradation-sensitive pricing framework, intended for regulated markets, which dynamically adjusts swap prices based on state-of-charge depletion, duty cycle intensity, environmental exposure, and nonlinear battery degradation effects. This formulation is evaluated for six representative operational cases, demonstrating its ability to incentivize shallow cycling, penalize deep discharges, and incorporate fair usage-based pricing. The proposed architectures and pricing models offer a viable pathway to accelerate commercial EV adoption while optimizing asset utilization and profitability for station operators. Full article

► Show Figures

Graphical abstract

19 pages, 589 KB

Open AccessArticle

The Impact of the Expected Utility and Experienced Utility Gap on Electric Vehicle Repurchase Intention in Jiangsu, China

by Xiao Zheng, Jiaxin Huang, Mengzhe Wang and Wenbo Li

World Electr. Veh. J. 2025, 16(9), 517; https://doi.org/10.3390/wevj16090517 (registering DOI) - 12 Sep 2025

Abstract

The global automotive industry’ s rapid transformation has led to electric vehicles (EVs) capturing a significant market share as a sustainable transportation option. To sustain this growth, it is crucial to not only attract new users but also retain existing ones through repurchases. [...] Read more.

The global automotive industry’ s rapid transformation has led to electric vehicles (EVs) capturing a significant market share as a sustainable transportation option. To sustain this growth, it is crucial to not only attract new users but also retain existing ones through repurchases. This decision is shaped by both vehicle attributes and users’ prior experiences. This study examines the impact of five dimensions of expected utility and experienced utility gap (including cost utility, functional utility, emotional utility, environmental utility, and social utility) on the repurchase intentions of 863 Chinese EV users. Discrete choice experiments were used to analyze these factors, considering both vehicle and personal attributes. The results show that when emotional utility exceeds expectations, users are more likely to repurchase pure electric and plug-in hybrid electric vehicles. However, if environmental and social utilities fall short of expectations, users may be discouraged from choosing these two vehicle types. In contrast, decisions regarding gasoline vehicles are primarily driven by economic and habitual factors, with minimal influence from emotional, environmental, or social utilities. Additionally, EV users show a preference for medium-sized models that offer shorter charging times and longer driving ranges. These findings offer insights for enhancing consumer acceptance, accelerating EV market penetration, and supporting the automotive industry’s sustainable development, thereby contributing to the achievement of environmental sustainability goals. Full article

► Show Figures

Figure 1

24 pages, 921 KB

Open AccessArticle

Assessing Consumers’ Willingness to Pay for Secondary Utilization of Retired Battery Products: The Role of Incentive Policy, Knowledge, and Perceived Risks

by Ziyi Zhao, Pengyu Dai, Chaoqun Zheng and Huaming Song

World Electr. Veh. J. 2025, 16(9), 516; https://doi.org/10.3390/wevj16090516 - 12 Sep 2025

Abstract

The rapid development of the new energy vehicle industry has resulted in a large number of retired power batteries. Creating products from second-use retired batteries (SURB) is crucial for sustainability by extending the batteries’ operational life, which, in turn, conserves resources and protects [...] Read more.

The rapid development of the new energy vehicle industry has resulted in a large number of retired power batteries. Creating products from second-use retired batteries (SURB) is crucial for sustainability by extending the batteries’ operational life, which, in turn, conserves resources and protects the environment. Consequently, this paper establishes a structural equation model (SEM) based on an interpretive structural model (ISM). It investigates consumers’ willingness to pay (WTP) for secondary utilization of retired batteries (SURB) products by extending the theory of planned behavior (TPB)with incentive policy, knowledge, and perceived risk. The study reveals that incentive policies and knowledge are fundamental factors, while subjective norms, perceived risk, and perceived behavioral control exert moderate influence. Attitude emerges as the most significant predictor, directly affecting consumers’ WTP, with perceived behavioral control also playing a key role. Incentive policies and knowledge have an indirect influence through perceived behavioral control and perceived risk. Finally, this paper discusses the theoretical and practical significance of the findings and provides relevant policy recommendations. Full article

► Show Figures

Graphical abstract

25 pages, 3429 KB

Open AccessArticle

Active and Reactive Power Scheduling of Distribution System Based on Two-Stage Stochastic Optimization

by Yangchao Xu, Jia Ren, Qiang He, Dongyang Dong and Haoxiang Zou

World Electr. Veh. J. 2025, 16(9), 515; https://doi.org/10.3390/wevj16090515 - 11 Sep 2025

Abstract

With the large-scale integration of distributed resources into the distribution network, such as wind/solar power and electric vehicles (EVs), the uncertainties have rapidly increased in the operation optimization of the distribution network. In this context, it is of great practical interest to ensure [...] Read more.

With the large-scale integration of distributed resources into the distribution network, such as wind/solar power and electric vehicles (EVs), the uncertainties have rapidly increased in the operation optimization of the distribution network. In this context, it is of great practical interest to ensure the security and economic operation of the distribution network. This paper addresses this issue and makes the following contributions. Firstly, a two-stage stochastic rolling optimization framework for active–reactive power scheduling is established. In the first stage, it dispatches the active power of distributed resources. In the second stage, it optimizes the reactive power compensation based on the first-stage scheduling plan. Secondly, the simulation-based Rollout method is proposed to obtain the improved active power dispatching policy for cost optimization in the first stage. Meanwhile, the aggregated power of EVs can be determined based on the mobility and charging demand of EVs. Thirdly, based on the aggregated power of EVs, a scenario-based second-order cone programming is applied to perform the rolling optimization of reactive power compensation for voltage performance improvement in the second stage. The numerical results demonstrate that this method can effectively improve the economic operation of the distribution network while enhancing its operational security by leveraging the charging elasticity of EVs. Full article

► Show Figures

Figure 1

16 pages, 23983 KB

Open AccessArticle

A Novel Railgun-Based Actuation System for Ultrafast DC Circuit Breakers in EV Fast-Charging Applications

by Fermín Gómez de León, Ara Bissal, Maurizio Repetto and Fabio Freschi

World Electr. Veh. J. 2025, 16(9), 514; https://doi.org/10.3390/wevj16090514 (registering DOI) - 11 Sep 2025

Abstract

This paper presents a novel ultrafast DC circuit breaker concept based on a railgun actuator, designed for ultrafast charging stations operating at 800

V

and delivering up to 640

k

W

. The proposed breaker achieves contact opening speeds exceeding 190

m

/ [...] Read more.

This paper presents a novel ultrafast DC circuit breaker concept based on a railgun actuator, designed for ultrafast charging stations operating at 800

V

and delivering up to 640

k

W

. The proposed breaker achieves contact opening speeds exceeding 190

m

/

s

, enabling fault current interruption within 200

μ s

and limiting the peak fault current to 2200

A

. This performance significantly reduces breaker stress compared with conventional mechanical solutions. System-level simulations demonstrate a dramatic reduction in energy dissipation during faults—from 11,000 J with a conventional fast breaker to just 250

J

using the proposed design. A 3D finite element method model of the railgun actuator confirms the feasibility of achieving a 15

m

m

stroke in 150

μ s

. The evolution of current density and magnetic field is analyzed, highlighting the influence of skin and velocity skin effects. Results confirm that the proposed solution acts both as a circuit breaker and a fault current limiter, enhancing safety, reliability, and durability in high-power DC systems. Full article

(This article belongs to the Special Issue Fast-Charging Station for Electric Vehicles: Challenges and Issues)

► Show Figures

Figure 1

18 pages, 4627 KB

Open AccessArticle

Railway Fastener Defect Detection Model Based on Dual Attention and MobileNetv3

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

World Electr. Veh. J. 2025, 16(9), 513; https://doi.org/10.3390/wevj16090513 - 11 Sep 2025

Abstract

Defect detection in rail fasteners constitutes a fundamental requirement for ensuring safe and reliable railway operations. Confronted with increasingly demanding inspection requirements of modern rail networks, traditional manual visual inspection methods have proven inadequate. To achieve accurate, efficient, and intelligent detection of rail [...] Read more.

Defect detection in rail fasteners constitutes a fundamental requirement for ensuring safe and reliable railway operations. Confronted with increasingly demanding inspection requirements of modern rail networks, traditional manual visual inspection methods have proven inadequate. To achieve accurate, efficient, and intelligent detection of rail fasteners, this paper presents an enhanced YOLOv5m-based defect detection model. Firstly, a dual-attention mechanism comprising Squeeze-and-Excitation and Coordinate Attention modules is employed to enhance the model. Secondly, the network architecture is redesigned by adopting MobileNetv3 as the backbone while incorporating structures with Ghost Shuffle Convolution (GSConv) modules and lightweight upsampling operators to reduce computational overhead. Finally, the original CIoU loss function in YOLOv5 is replaced with SIoU to accelerate convergence rate during training. Experimental results on a custom-built rail fastener dataset comprising 6500 images demonstrate that the enhanced model achieves 96.5% mAP and 17.9 FPS, surpassing the baseline by 3.1% and 2.1 FPS, respectively. Compared to existing detection models, this solution exhibits higher accuracy, faster inference, and lower memory consumption, providing critical technical support for edge deployment of rail fastener defect detection systems. Full article

► Show Figures

Figure 1

24 pages, 3343 KB

Open AccessArticle

Modelling, Analysis, and Nonlinear Control of a Dynamic Wireless Power Transfer Charger for Electrical Vehicle

by Ahmed Hamed, Abdellah Lassioui, Hassan El Fadil, Hafsa Abbade, Sidina El jeilani, Marouane El Ancary, Mohammed Chiheb and Zakariae El Idrissi

World Electr. Veh. J. 2025, 16(9), 512; https://doi.org/10.3390/wevj16090512 - 11 Sep 2025

Abstract

This article presents an in-depth study of a dynamic wireless power transfer (DWPT) system used to charge electric vehicles (EVs), with a focus on modeling and controlling a double-D (DD) coil structure. The chosen DD coil design improves energy transfer efficiency and minimizes [...] Read more.

This article presents an in-depth study of a dynamic wireless power transfer (DWPT) system used to charge electric vehicles (EVs), with a focus on modeling and controlling a double-D (DD) coil structure. The chosen DD coil design improves energy transfer efficiency and minimizes mutual coupling between adjacent transmit coils, a common problem in dynamic applications. A comprehensive mathematical model is developed to account for the nonlinear dynamics of the system, i.e., when the vehicle is moving and misalignments and coupling variations occur. A robust nonlinear control method based on sliding mode control (SMC) is implemented to ensure stable operation and accurate regulation of the output voltage. The controller is tested in different scenarios where the vehicle speed changes, thus ensuring its robustness and stability under all operating conditions. Particular attention is paid to the critical transition zone, in which the receiver coil is placed between two transmitter coils in order to achieve minimal magnetic coupling. The simulation results demonstrate that the proposed controller offers a fast dynamic response (~0.07 s) and stable voltage tracking, even in the event of significant variations in mutual inductance and different EV movement speeds. These results confirm the effectiveness of the control approach and its potential for real-time charging of electric vehicles in large-scale DWPT applications. Full article

► Show Figures

Figure 1

26 pages, 8589 KB

Open AccessArticle

Remaining Useful Life Prediction of PEMFC Based on 2-Layer Bidirectional LSTM Network

by Wenxu Niu, Xiaokang Li, Haobin Tian and Caiping Liang

World Electr. Veh. J. 2025, 16(9), 511; https://doi.org/10.3390/wevj16090511 - 11 Sep 2025

Abstract

Proton exchange membrane fuel cells (PEMFCs) are considered promising solutions to address global energy and environmental challenges. This is largely due to their high efficiency in energy transformation, low emission of pollutants, quick responsiveness, and suitable operating conditions. However, their widespread application is [...] Read more.

Proton exchange membrane fuel cells (PEMFCs) are considered promising solutions to address global energy and environmental challenges. This is largely due to their high efficiency in energy transformation, low emission of pollutants, quick responsiveness, and suitable operating conditions. However, their widespread application is limited by high cost, limited durability and system complexity. To maintain system reliability and optimize cost-effectiveness, it is essential to predict the remaining operational lifespan of PEMFC systems with precision. This study introduces a prediction framework integrating a dual-layer bidirectional LSTM architecture enhanced by an attention mechanism for accurately predicting the RUL of PEMFCs. Raw data is preprocessed, and important features are selected by the smoothing technique and random forest method to reduce manual intervention. To enhance model adaptability and predictive accuracy, the Optuna optimization framework is employed to automatically fine-tune hyperparameters. The proposed prediction model is benchmarked against several existing approaches using aging datasets from two separate PEMFC stacks. Experimental findings indicate that the proposed two-layer BiLSTM with attention mechanism surpasses other baseline models in performance. Notably, the designed prediction model demonstrates strong performance on both benchmark datasets and real-world data acquired through a custom-built experimental fuel cell platform. This research offers meaningful guidance for prolonging the service life of PEMFCs and enhancing the efficiency of maintenance planning. Full article

► Show Figures

Figure 1

24 pages, 23437 KB

Open AccessArticle

Fusing Direct and Indirect Visual Odometry for SLAM: An ICM-Based Framework

by Jeremias Gaia, Javier Gimenez, Eugenio Orosco, Francisco Rossomando, Carlos Soria and Fernando Ulloa-Vásquez

World Electr. Veh. J. 2025, 16(9), 510; https://doi.org/10.3390/wevj16090510 - 10 Sep 2025

Abstract

The loss of localization in robots navigating GNSS-denied environments poses a critical challenge that can compromise mission success and safe operation. This article presents a method that fuses visual odometry outputs from both direct and feature-based (indirect) methods using Iterated Conditional Modes (ICMs), [...] Read more.

The loss of localization in robots navigating GNSS-denied environments poses a critical challenge that can compromise mission success and safe operation. This article presents a method that fuses visual odometry outputs from both direct and feature-based (indirect) methods using Iterated Conditional Modes (ICMs), an efficient iterative optimization algorithm that maximizes the posterior probability in Markov random fields, combined with uncertainty-aware gain adjustment to perform pose estimation and mapping. The proposed method enhances the performance of visual localization and mapping algorithms in low-texture or visually degraded scenarios. The method was validated using the TUM RGB-D benchmark dataset and through real-world tests in both indoor and outdoor environments. Outdoor experiments were conducted on an electric vehicle, where the method maintained stable tracking. These initial results suggest that the technique could be transferable to electric vehicle platforms and applicable in a variety of real-world conditions. Full article

► Show Figures

Figure 1

29 pages, 3223 KB

Open AccessArticle

Optimization of Prefabricated Building Component Distribution Under Dynamic Charging Strategy for Electric Heavy-Duty Trucks

by Xinran Qi, Weichen Zheng, Heping Wang and Fuyu Wang

World Electr. Veh. J. 2025, 16(9), 509; https://doi.org/10.3390/wevj16090509 - 10 Sep 2025

Abstract

To align with the adoption of electric vehicles in the transportation sector, this paper proposes the use of electric heavy-duty trucks for the logistics and distribution of large prefabricated building components. This approach aims to address the problems of high total costs and [...] Read more.

To align with the adoption of electric vehicles in the transportation sector, this paper proposes the use of electric heavy-duty trucks for the logistics and distribution of large prefabricated building components. This approach aims to address the problems of high total costs and significant energy waste in prefabricated component transportation. Focusing on the multi-to-multi distribution mode, a two-level optimization model is constructed. The upper-level model is responsible for the reasonable allocation of demand points. The lower-level model optimizes the selection of road network nodes and charging stations along the delivery routes. It also dynamically adjusts charging timing and volume according to the real-time power situation. To enhance solution performance, a two-level multi-objective evolutionary algorithm based on Pareto theory is designed. This algorithm simultaneously optimizes distribution costs while coordinating path planning and charging strategies. Comparative experiments across different cases show that, compared with traditional single-level and multi-stage models, the proposed algorithm improves both solution accuracy and quality. Additionally, when compared with the scheduling scheme based on the full-charge capacity strategy, the dynamic charging strategy proposed in this paper reduces the total distribution cost by approximately 15.83%. These findings demonstrate that the constructed model and algorithm can effectively optimize the logistics and distribution of prefabricated components. They also provide a feasible solution for the practical application of electric vehicles in engineering logistics. Full article

► Show Figures

Figure 1

21 pages, 6796 KB

Open AccessArticle

Optimal Air Gap Magnetic Flux Density Distribution of an IPM Synchronous Motor Using a PM Rotor Parameter-Stratified Sensitivity Analysis

by Jun Zhang, Wenjing Hu, Yanhong Gao, Sizhan Hua, Xin Zhou, Huihui Geng and Yixin Liu

World Electr. Veh. J. 2025, 16(9), 508; https://doi.org/10.3390/wevj16090508 - 10 Sep 2025

Viewed by 262

Abstract

In addressing the challenges posed by the numerous rotor structure parameters and the difficulty in analyzing the air gap magnetic field distribution in interior permanent magnet (IPM) motors, and to enhance the performance of automotive IPM synchronous motors, this paper proposes a multi-objective [...] Read more.

In addressing the challenges posed by the numerous rotor structure parameters and the difficulty in analyzing the air gap magnetic field distribution in interior permanent magnet (IPM) motors, and to enhance the performance of automotive IPM synchronous motors, this paper proposes a multi-objective optimization method based on sensitivity stratification. Firstly, sensitivity analysis is conducted on the positional and shape parameters of the rotor permanent magnets (PMs), and the parameters are stratified according to their sensitivity levels. Subsequently, distinct analysis and optimization methods are applied to parameters of different strata for dual-objective optimization, which aims to increase the amplitude of the air gap flux density and reduce its total harmonic distortion (THD). Moreover, the waveform of the air gap flux density is analyzed to propose a targeted arrangement of magnetic isolation slots, thereby further optimizing the magnetic field distribution. Meanwhile, the demagnetization conditions and influencing factors of the PMs under overload are analyzed to enhance their demagnetization resistance and determine the final structural parameters. Simulation results indicate that, with the application of the proposed optimization method, the fundamental amplitude of the air gap flux density is increased by 0.035 T and THD is decreased by 9.9% when the proposed optimization method is applied. This verifies the effectiveness and feasibility of the method. Full article

► Show Figures

Figure 1

15 pages, 3461 KB

Open AccessArticle

Research on Noise Suppression Strategies for High-Frequency Harmonic Noise in Automotive Electronic Water Pumps

by Xiaodan Feng, Xipei Ma, Pingqing Fan and Yansong Wang

World Electr. Veh. J. 2025, 16(9), 507; https://doi.org/10.3390/wevj16090507 - 9 Sep 2025

Viewed by 469

Abstract

In this paper, in order to effectively reduce the electromagnetic noise of automotive electronic water pumps, a Hybrid Random Carrier Space Vector Pulse Width Modulation Hybrid Random Carrier Space Vector Pulse Width Modulation, (HRCSVPWM) technique based on linear congruential generator (LCG) algorithm is [...] Read more.

In this paper, in order to effectively reduce the electromagnetic noise of automotive electronic water pumps, a Hybrid Random Carrier Space Vector Pulse Width Modulation Hybrid Random Carrier Space Vector Pulse Width Modulation, (HRCSVPWM) technique based on linear congruential generator (LCG) algorithm is proposed to study the suppression effect of current harmonics and acoustic vibration response with an automotive electronic water pump as the research object. Firstly, the HRCSVPWM based technique is proposed on the basis of SVPWM and pulse width modulation strategies. Secondly, the performance of random numbers generated for HRCSVPWM is analyzed, and it is proposed to use an LCG random number generator to generate excellent random numbers combined with a genetic algorithm to quickly determine the optimal values of three random parameters, namely, random number Ri, mixing degree coefficient Ki, and spreading width Ti, which enhances the stochasticity and spatial traversal of random sequences and ensures the effect of the HRSVPWM control method. Finally, simulation analysis is carried out, and a noise experimental platform is built for experimental verification. The results show that using the improved HRCSVPWM control strategy, compared with the SVPWM control strategy, the total harmonic content decreased by close to 21.81%, and the sound pressure level amplitude decreased by an average of approximately 6 dB. Full article

► Show Figures

Figure 1

20 pages, 1822 KB

Open AccessArticle

Maximum Power Point Tracking Strategy for Fuel Cells Based on an Adaptive Particle Swarm Optimization Algorithm

by Jing Han, Xinyao Zhou and Chunsheng Wang

World Electr. Veh. J. 2025, 16(9), 506; https://doi.org/10.3390/wevj16090506 - 9 Sep 2025

Viewed by 192

Abstract

With the growing global demand for clean energy, fuel cells have been adopted as key components in renewable energy systems. Their high efficiency and environmentally friendly operation make them attractive. However, during maximum power point tracking (MPPT), traditional proportional–integral–derivative (PID) controllers often fail [...] Read more.

With the growing global demand for clean energy, fuel cells have been adopted as key components in renewable energy systems. Their high efficiency and environmentally friendly operation make them attractive. However, during maximum power point tracking (MPPT), traditional proportional–integral–derivative (PID) controllers often fail to maintain optimal power output. Dynamic load changes and complex operating conditions exacerbate this issue. As a result, system response is slowed, and tracking accuracy is reduced. To address these problems, an online identification method based on recursive least squares (RLS) is employed. A cubic power–current model is identified in real time. Polynomial fitting and the golden section search are then applied to estimate the current at the maximum power point. Following model-based estimation, adaptive particle swarm optimization (APSO) is utilized to tune the PID controller parameters. Precise regulation is thus achieved. The use of RLS enables real-time model identification. The golden section search improves the efficiency of current estimation. APSO enhances global optimization, while PID provides fast dynamic response. By integrating these methods, both tracking accuracy and system responsiveness are significantly improved in fuel cell MPPT applications. Simulation results demonstrate that the proposed strategy enhances maximum power output by up to 12.40% compared to conventional P&O, fuzzy logic control, GWO-PID, and PSO-PID methods, as well as maintaining a consistent improvement of 1.50% to 1.90% even when compared to other optimization algorithms. Full article

► Show Figures

Figure 1

22 pages, 1969 KB

Open AccessArticle

Assessment of Ejector-Expansion Heat Pump Systems with Low GWP Refrigerants for Electric Vehicles

by Zhenying Zhang, Yuying Wang, Zhengdao Zhou, Zheng Guan, Li Chang and Meiyuan Yang

World Electr. Veh. J. 2025, 16(9), 505; https://doi.org/10.3390/wevj16090505 - 8 Sep 2025

Viewed by 894

Abstract

This study addresses the critical challenge of developing efficient thermal management systems for electric vehicles by proposing and evaluating two novel ejector-expansion heat pump configurations: single-evaporator (SEEHP) and dual-evaporator (DEEHP) systems. Through comprehensive thermodynamic analysis across six representative Chinese cities using four refrigerants [...] Read more.

This study addresses the critical challenge of developing efficient thermal management systems for electric vehicles by proposing and evaluating two novel ejector-expansion heat pump configurations: single-evaporator (SEEHP) and dual-evaporator (DEEHP) systems. Through comprehensive thermodynamic analysis across six representative Chinese cities using four refrigerants (R134a, R32, R152a, R290), system performance via coefficient of performance (COP) and lifecycle CO₂ emissions were assessed. The results demonstrate significant advantages over conventional (CBHP) and vapor injection (VIHP) systems, particularly in extreme cold conditions. The SEEHP configuration achieves 10–30% COP improvements versus CBHP, while DEEHP shows 7–15% enhancement. The corresponding lifecycle emission reductions reach 9–14% for SEEHP and 2–11% for DEEHP relative to conventional systems. Among the refrigerants, R290 systems achieve the lowest equivalent CO₂ emissions due to superior COP in Beijing, Shanghai, Chongqing, Kunming and Guangzhou, whereas R32 systems yield minimal emissions owing to its exceptional heating capacity in Harbin. These findings highlight ejector technology’s potential for substantially improving electric vehicle energy efficiency while reducing environmental impact. Full article

(This article belongs to the Special Issue Solutions to the Challenge of Implementing Air Conditioning Systems in Electric Vehicles)

► Show Figures

Figure 1

19 pages, 11446 KB

Open AccessArticle

Research on Constant-Voltage/Constant-Current Characteristics of Variable-Structure Dual-Frequency Dual-Load Wireless Power Transfer Technology

by Lu Zhang, Jundan Mao, Yonglin Ke, Yueliang Chen, Yao Dong and Qinzheng Zhang

World Electr. Veh. J. 2025, 16(9), 504; https://doi.org/10.3390/wevj16090504 - 8 Sep 2025

Viewed by 1016

Abstract

To address the limitations of conventional magnetically coupled resonant wireless power transfer (MCR-WPT) systems in multi-frequency multi-load applications—specifically inadequate load power independence and high complexity inconstant-voltage/constant-current (CV/CC) control—this paper proposes a variable-structure dual-frequency dual-load wireless power transfer system by first establishing its mathematical [...] Read more.

To address the limitations of conventional magnetically coupled resonant wireless power transfer (MCR-WPT) systems in multi-frequency multi-load applications—specifically inadequate load power independence and high complexity inconstant-voltage/constant-current (CV/CC) control—this paper proposes a variable-structure dual-frequency dual-load wireless power transfer system by first establishing its mathematical model and implementing hybrid-frequency modulation for multi-frequency output, then developing an improved T/LCC hybrid resonant topology by deriving parameter design conditions for compensation network reconfiguration under CV/CC requirements, subsequently employing an orthogonal planar solenoid coupling mechanism and frequency-division demodulation to achieve load-independent power regulation across wide load ranges for enhanced stability, and finally constructing a 120 W dual-frequency dual-load prototype to validate the system’s CV/CC characteristics, where simulations and experimental results demonstrate stronger consistency with theoretical predictions. Full article

(This article belongs to the Special Issue Power Electronics for Electric Vehicles)

► Show Figures

Figure 1

21 pages, 6144 KB

Open AccessArticle

A Flexible Assembly and Gripping Process of Hairpin Baskets

by Felix Fraider, Peter Dreher, Josette Lindner, Dominik Reichl, Florian Kößler and Jürgen Fleischer

World Electr. Veh. J. 2025, 16(9), 503; https://doi.org/10.3390/wevj16090503 - 7 Sep 2025

Viewed by 255

Abstract

Established hairpin stators for electric traction motors are made up of a large number of so-called hairpins. To produce these stators, the individual hairpins must first be pre-assembled into an auxiliary device in order to achieve the desired winding scheme. The resulting hairpin [...] Read more.

Established hairpin stators for electric traction motors are made up of a large number of so-called hairpins. To produce these stators, the individual hairpins must first be pre-assembled into an auxiliary device in order to achieve the desired winding scheme. The resulting hairpin basket must then be picked up and transported to the lamination stack. Automated solutions for both processes are characterized by a high degree of complexity and low flexibility. Manual assembly, however, is prone to errors. The new approach presented in this paper is therefore based on the collaborative assembly of the hairpins and a flexible hairpin basket gripper. A cobot hands the hairpins in the correct sequence to the operator. The correct positioning of the hairpins in the auxiliary device is ensured by the use of a monitor located under it. The creation of the correct assembly sequence is partly automated by a collision detection program. In addition, a new and flexible hairpin basket gripping concept is presented. Tests show that the cycle times of both new processes are slow due to hardware limitations. This restricts their use to specific applications, such as complex winding patterns or very small quantities. Full article

(This article belongs to the Special Issue EVS37—International Electric Vehicle Symposium and Exhibition (Seoul, Republic of Korea))

► Show Figures

Figure 1

30 pages, 13345 KB

Open AccessArticle

Prediction of Electric Vehicle Charging Load Considering User Travel Characteristics and Charging Behavior

by Haihong Bian, Xin Tang, Kai Ji, Yifan Zhang and Yongqing Xie

World Electr. Veh. J. 2025, 16(9), 502; https://doi.org/10.3390/wevj16090502 - 6 Sep 2025

Viewed by 220

Abstract

Accurate forecasting of the electric vehicle (EV) charging load is a prerequisite for developing coordinated charging and discharging strategies. This study proposes a method for predicting the EV charging load by incorporating user travel characteristics and charging behavior. First, a transportation network–distribution network [...] Read more.

Accurate forecasting of the electric vehicle (EV) charging load is a prerequisite for developing coordinated charging and discharging strategies. This study proposes a method for predicting the EV charging load by incorporating user travel characteristics and charging behavior. First, a transportation network–distribution network coupling framework is established based on a road network model with multi-source information fusion. Second, considering the multiple-intersection features of urban road networks, a time-flow model is developed. A time-optimal path selection method is designed based on the topological structure of the road network. Then, an EV driving energy consumption model is developed, accounting for both the mileage energy consumption and air conditioning energy consumption. Next, the user travel characteristics are finely modeled under two scenarios: working days and rest days. A user charging decision model is established using a fuzzy logic inference system, taking into account the state of charge (SOC), average electricity price, and parking duration. Finally, the Monte Carlo method is applied to simulate user travel and charging behavior. A simulation of the spatiotemporal distribution of the EV charging load was conducted in a specific area of Jiangning District, Nanjing. The simulation results show that there is a significant difference in the time distribution of EV charging loads between working days and rest days, with peak-to-valley differences of 3100.8 kW and 3233.5 kW, respectively. Full article

(This article belongs to the Special Issue Sustainable EV Rapid Charging, Challenges, and Development)

► Show Figures

Figure 1

31 pages, 8391 KB

Open AccessArticle

Evaluating Key Spatial Indicators for Shared Autonomous Vehicle Integration in Old Town Spaces

by Sucheng Yao, Kanjanee Budthimedhee, Sakol Teeravarunyou, Xinhao Chen and Ziqiang Zhang

World Electr. Veh. J. 2025, 16(9), 501; https://doi.org/10.3390/wevj16090501 - 5 Sep 2025

Viewed by 293

Abstract

As Shared Autonomous Vehicles (SAVs) emerge as a transformative force in urban mobility, integrating them into dense, historic urban environments presents distinct spatial and planning challenges—such as narrow street patterns, irregular road networks, and the need to protect cultural heritage. This study investigates [...] Read more.

As Shared Autonomous Vehicles (SAVs) emerge as a transformative force in urban mobility, integrating them into dense, historic urban environments presents distinct spatial and planning challenges—such as narrow street patterns, irregular road networks, and the need to protect cultural heritage. This study investigates the spatial adaptability of SAVs in Suzhou old town, a representative example of East Asian heritage cities. To assess spatial readiness, a hybrid weighting approach combining the Analytic Hierarchy Process (AHP) and the Entropy Weight Method (EWM) is used to evaluate 22 spatial indicators across livability, mobility, and spatial quality. These weighted indicators are mapped using a spatial density analysis based on Point of Interest (POI) data, revealing urban service distribution patterns and spatial mismatches. Results show that “Accessibility to Transportation Hubs” receives the highest composite weight, emphasizing the priority of linking SAVs with existing subway and bus networks. Environmental comfort factors—such as air quality, noise reduction, and access to green and recreational spaces—also rank highly, reflecting a growing emphasis on urban livability. Drawing on these findings, this study proposes four strategic directions for SAV integration that focus on network flexibility, public service redistribution, ecological enhancement, and cultural preservation. The proposed framework provides a transferable planning reference for historic urban areas transitioning toward intelligent, human-centered mobility systems. Full article

► Show Figures

Figure 1

24 pages, 5081 KB

Open AccessArticle

Simulative Consumption Analysis of an All-Electric Vehicle Fleet in an Urban Environment

by Paul Heckelmann, Tobias Peichl, Johanna Krettek and Stephan Rinderknecht

World Electr. Veh. J. 2025, 16(9), 500; https://doi.org/10.3390/wevj16090500 - 5 Sep 2025

Viewed by 314

Abstract

The increasing shift towards battery electric vehicles (BEVs) in urban environments raises the question of how real-world traffic conditions affect their energy consumption. While BEVs are expected to reduce local emissions, their total energy demand, particularly in city traffic with with low average [...] Read more.

The increasing shift towards battery electric vehicles (BEVs) in urban environments raises the question of how real-world traffic conditions affect their energy consumption. While BEVs are expected to reduce local emissions, their total energy demand, particularly in city traffic with with low average speeds, and therefore a higher impact of secondary consumption, remains insufficiently understood. To address this, a simulative framework to analyze the average energy consumption of an all-electric vehicle fleet in a mid-sized city, using Darmstadt, Germany, as a case study, is presented. A validated microscopic traffic simulation is built based on 2024 data and enriched with representative powertrain models for various vehicle classes, including passenger cars, trucks, and buses. The simulation allows the assessment of consumption under different traffic densities and speeds, revealing the substantial influence of secondary consumers and traffic flow on total energy demand. Furthermore, the study compares the

C O_{2}

emissions of an all-BEV fleet with those of a fully combustion-based fleet. The findings aim to highlight the role of secondary consumers in urban traffic and to identify the potential for energy-saving. Full article

(This article belongs to the Special Issue Electric Vehicle Networking and Traffic Control)

► Show Figures

Figure 1

17 pages, 2279 KB

Open AccessArticle

Systematic Planning of Electric Vehicle Battery Swapping and Charging Station Location and Driver Routing with Bi-Level Optimization

by Bowen Chen, Jianling Chen and Haixia Feng

World Electr. Veh. J. 2025, 16(9), 499; https://doi.org/10.3390/wevj16090499 - 4 Sep 2025

Viewed by 322

Abstract

The rapid growth of electric vehicles (EVs) has significantly increased the demand for charging infrastructure, posing a challenge in balancing charging demand and infrastructure supply. The development of battery swapping and charging stations (BSCSs) is crucial for addressing these challenges and serves as [...] Read more.

The rapid growth of electric vehicles (EVs) has significantly increased the demand for charging infrastructure, posing a challenge in balancing charging demand and infrastructure supply. The development of battery swapping and charging stations (BSCSs) is crucial for addressing these challenges and serves as a fundamental pillar for the sustainable advancement of EVs. This study develops a bi-level optimization model for the location and route planning of BSCSs. The upper-level model optimizes station locations to minimize total cost and service delay, while the lower-level model optimizes driver travel routes to minimize total time. An updated Non-Dominated Sorting Genetic Algorithm (UNSGA) is applied to enhance solution efficiency. The experimental results show that the bi-level model outperforms the single-level model, reducing total cost by 1.5% and travel time by 6.6%. Compared to other algorithms, the UNSGA achieves 9.43% and 8.23% lower costs than MOPSO and MOSA, respectively. Furthermore, BSCSs, despite 15.42% higher construction costs, reduce driver travel time by 22.43% and waiting time by 71.19%, highlighting their operational advantages. The bi-level optimization method provides more cost-effective decision support for EV infrastructure investors, enabling them to adapt to dynamic drivers’ needs and optimize resource allocation. Full article

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

► Show Figures

Graphical abstract

21 pages, 6687 KB

Open AccessArticle

Research on the Charging Point Business Model of EV Users with Variable Roles

by Weihua Wu, Jieyun Wei, Yifan Zhang, Eun-Young Nam and Dongphil Chun

World Electr. Veh. J. 2025, 16(9), 498; https://doi.org/10.3390/wevj16090498 - 3 Sep 2025

Viewed by 359

Abstract

The current global utilization rate of electric vehicle (EV) charging stations ranges from approximately 20% to 40%. Despite numerous studies focusing on enhancing this utilization through single-variable approaches—such as optimizing charging point (CP) locations, analyzing charging behaviors, and adjusting pricing—low utilization rates persist. [...] Read more.

The current global utilization rate of electric vehicle (EV) charging stations ranges from approximately 20% to 40%. Despite numerous studies focusing on enhancing this utilization through single-variable approaches—such as optimizing charging point (CP) locations, analyzing charging behaviors, and adjusting pricing—low utilization rates persist. This paper examines the business model for EVs and charging stations integrated into the 5G Real-Time System for EVs and Transportation (5gRTS-ET) platform, which was operational in China in 2021. It establishes three distinct business models for EV users: the Government Subsidy Model, the Self-Operating Model without Government Subsidies, and the 5gRTS-ET Operating Model. Utilizing an integrated service modeling approach, the study constructs a dynamic business model for charging stations. Findings indicate that incorporating variables related to EV user roles significantly enhances the utilization rates of charging stations. Furthermore, onboarding EV CPs onto the 5gRTS-ET platform emerges as an effective strategy for ensuring their sustainable operation. This research offers a sustainable business model for EV charging stations in light of the evolving roles of EV users and serves as a reference for applying integrated business modeling methods in practical operational platforms. Full article

► Show Figures

Figure 1

29 pages, 4169 KB

Open AccessArticle

Evaluation of Waveform Distortion in BESS-Integrated Fast-Charging Station

by Manav Giri and Sarah Rönnberg

World Electr. Veh. J. 2025, 16(9), 497; https://doi.org/10.3390/wevj16090497 - 2 Sep 2025

Viewed by 446

Abstract

This paper presents a detailed, measurement-based assessment of interharmonic, harmonic, and supraharmonic emissions from a Battery Energy Storage System (BESS) supporting electric vehicle (EV) fast charging. In contrast to prior literature, which is largely simulation-based and often neglects interharmonic and even harmonic components, [...] Read more.

This paper presents a detailed, measurement-based assessment of interharmonic, harmonic, and supraharmonic emissions from a Battery Energy Storage System (BESS) supporting electric vehicle (EV) fast charging. In contrast to prior literature, which is largely simulation-based and often neglects interharmonic and even harmonic components, this study provides real-world data under dynamic operating conditions. Emission limits are established in accordance with relevant international standards, with the observed deviations from standard practices highlighted in existing studies. The operation of the BESS-assisted fast-charging system is classified into five distinct operating stages, and the variations in spectral emissions across these stages are analyzed. A comparative evaluation with a grid-fed fast charger reveals the influence of BESS integration on power quality. Notably, the analysis shows a significant increase in even harmonics during EV charging events. This component is identified as the limiting factor in the network’s harmonic hosting capacity, underscoring the need to account for even harmonics in future grid compatibility assessments. These findings provide valuable insights for grid operators, EV infrastructure planners, and standardization bodies aiming to ensure compliance with power quality standards in evolving charging scenarios. Full article

(This article belongs to the Special Issue Fast-Charging Station for Electric Vehicles: Challenges and Issues)

► Show Figures

Figure 1

25 pages, 2842 KB

Open AccessArticle

Design of Coordinated EV Traffic Control Strategies for Expressway System with Wireless Charging Lanes

by Yingying Zhang, Yifeng Hong and Zhen Tan

World Electr. Veh. J. 2025, 16(9), 496; https://doi.org/10.3390/wevj16090496 - 1 Sep 2025

Viewed by 284

Abstract

With the development of dynamic wireless power transfer (DWPT) technology, the introduction of wireless charging lanes (WCLs) in traffic systems is seen as a promising trend for electrified transportation. Though there has been extensive discussion about the planning and allocation of WCLs in [...] Read more.

With the development of dynamic wireless power transfer (DWPT) technology, the introduction of wireless charging lanes (WCLs) in traffic systems is seen as a promising trend for electrified transportation. Though there has been extensive discussion about the planning and allocation of WCLs in different situations, studies on traffic control models for WCLs are relatively lacking. Thus, this paper aims to design a coordinated optimization strategy for managing electric vehicle (EV) traffic on an expressway network, which integrates a corridor traffic flow model with a wireless power transmission model. Two components are considered in the control objective: the total energy increased for the EVs and the total number of EVs served by the expressway, over the problem horizon. By setting the trade-off coefficients for these two objectives, our model can be used to achieve mixed optimization of WCL traffic management. The decisions include metering of different on-ramps as well as routing plans for different groups of EVs defined by origin/destination pairs and initial SOC levels. The control problem is formulated as a novel linear programming model, rendering an efficient solution. Numerical examples are used to verify the effectiveness of the proposed traffic control model. The results show that with the properly designed traffic management strategy, a notable increase in charging performance can be achieved by compromising slightly the traffic performance while maintaining overall smooth operation throughout the expressway system. Full article

► Show Figures

Figure 1

20 pages, 7286 KB

Open AccessArticle

Fault Identification Method for Flexible Traction Power Supply System by Empirical Wavelet Transform and 1-Sequence Faulty Energy

by Jiang Lu, Shuai Wang, Shengchun Yan, Nan Chen, Daozheng Tan and Zhongrui Sun

World Electr. Veh. J. 2025, 16(9), 495; https://doi.org/10.3390/wevj16090495 - 1 Sep 2025

Viewed by 282

Abstract

The 2 × 25 kV flexible traction power supply system (FTPSS), using a three-phase-single-phase converter as its power source, effectively addresses the challenges of neutral section transitions and power quality issues inherent in traditional power supply systems (TPSSs). However, the bidirectional fault current [...] Read more.

The 2 × 25 kV flexible traction power supply system (FTPSS), using a three-phase-single-phase converter as its power source, effectively addresses the challenges of neutral section transitions and power quality issues inherent in traditional power supply systems (TPSSs). However, the bidirectional fault current and low short-circuit current characteristics degrade the effectiveness of traditional TPSS protection schemes. This paper analyzes the fault characteristics of FTPSS and proposes a fault identification method based on empirical wavelet transform (EWT) and 1-sequence faulty energy. First, a composite sequence network model is developed to reveal the characteristics of three typical fault types, including ground faults and inter-line short circuits. The 1-sequence differential faulty energy is then calculated. Since the 1-sequence component is unaffected by the leakage impedance of autotransformers (ATs), the proposed method uses this feature to distinguish the TPSS faults from disturbances caused by electric multiple units (EMUs). Second, EWT is used to decompose the 1-sequence faulty energy, and relevant components are selected by permutation entropy. The fault variance derived from these components enables reliable identification of TPSS faults, effectively avoiding misjudgment caused by AT excitation inrush or harmonic disturbances from EMUs. Finally, real-time digital simulator experimental results verify the effectiveness of the proposed method. The fault identification method possesses high tolerance to transition impedance performance and does not require synchronized current measurements from both sides of the TPSS. Full article

► Show Figures

Figure 1

26 pages, 1799 KB

Open AccessArticle

Formal Modelling and Verification of Multi-Parameter Context and Agent Transition Systems: Application to Urban Delivery Zone and Autonomous Electric Vehicle

by Abir Nemouchi, Ahmed Bouzenada, Djamel Eddine Saidouni and Gregorio Díaz

World Electr. Veh. J. 2025, 16(9), 494; https://doi.org/10.3390/wevj16090494 - 1 Sep 2025

Viewed by 264

Abstract

The increasing integration of autonomous electric vehicles (EVs) into Intelligent Transportation Systems (ITSs) needs rigorous mechanisms to ensure their safe and effective operation in dynamic environments. The reliability of such vehicles depends not only on their internal capabilities but also on the suitability [...] Read more.

The increasing integration of autonomous electric vehicles (EVs) into Intelligent Transportation Systems (ITSs) needs rigorous mechanisms to ensure their safe and effective operation in dynamic environments. The reliability of such vehicles depends not only on their internal capabilities but also on the suitability and safety of the environments in which they operate. This paper introduces a formal modelling framework that captures independently the dynamic evolution of the environmental context and the EV agent using multi-parameter transition systems. Two distinct models are defined: the Context Transition System (CTS), which models changes in environmental states, and the Agent Transition System (ATS), which captures the internal state evolution of the EV. Safety and liveness properties are formally specified in Computation Tree Logic (CTL) and verified using the nuXmv model checker. The framework is validated through two representative use cases: a dynamic urban delivery zone and an autonomous electric delivery vehicle. The results highlight the framework’s effectiveness in detecting unsafe conditions, verifying mission objectives, and supporting the reliable deployment of EVs in ITS. Full article

► Show Figures

Figure 1

16 pages, 11354 KB

Open AccessArticle

MTC-BEV: Semantic-Guided Temporal and Cross-Modal BEV Feature Fusion for 3D Object Detection

by Qiankai Xi, Li Ma, Jikai Zhang, Hongying Bai and Zhixing Wang

World Electr. Veh. J. 2025, 16(9), 493; https://doi.org/10.3390/wevj16090493 - 1 Sep 2025

Viewed by 368

Abstract

We propose MTC-BEV, a novel multi-modal 3D object detection framework for autonomous driving that achieves robust and efficient perception by combining spatial, temporal, and semantic cues. MTC-BEV integrates image and LiDAR features in the Bird’s-Eye View (BEV) space, where heterogeneous modalities are aligned [...] Read more.

We propose MTC-BEV, a novel multi-modal 3D object detection framework for autonomous driving that achieves robust and efficient perception by combining spatial, temporal, and semantic cues. MTC-BEV integrates image and LiDAR features in the Bird’s-Eye View (BEV) space, where heterogeneous modalities are aligned and fused through the Bidirectional Cross-Modal Attention Fusion (BCAP) module with positional encodings. To model temporal consistency, the Temporal Fusion (TTFusion) module explicitly compensates for ego-motion and incorporates past BEV features. In addition, a segmentation-guided BEV enhancement projects 2D instance masks into BEV space, highlighting semantically informative regions. Experiments on the nuScenes dataset demonstrate that MTC-BEV achieves a nuScenes Detection Score (NDS) of 72.4% at 14.91 FPS, striking a favorable balance between accuracy and efficiency. These results confirm the effectiveness of the proposed design, highlighting the potential of semantic-guided cross-modal and temporal fusion for robust 3D object detection in autonomous driving. Full article

(This article belongs to the Special Issue Electric Vehicle Autonomous Driving Based on Image Recognition)

► Show Figures

Figure 1

49 pages, 1459 KB

Open AccessArticle

A Deep Learning Approach for Real-Time Intrusion Mitigation in Automotive Controller Area Networks

by Anila Kousar, Saeed Ahmed and Zafar A. Khan

World Electr. Veh. J. 2025, 16(9), 492; https://doi.org/10.3390/wevj16090492 - 1 Sep 2025

Viewed by 504

Abstract

The digital revolution has profoundly influenced the automotive industry, shifting the paradigm from conventional vehicles to smart cars (SCs). The SCs rely on in-vehicle communication among electronic control units (ECUs) enabled by assorted protocols. The Controller Area Network (CAN) serves as the de [...] Read more.

The digital revolution has profoundly influenced the automotive industry, shifting the paradigm from conventional vehicles to smart cars (SCs). The SCs rely on in-vehicle communication among electronic control units (ECUs) enabled by assorted protocols. The Controller Area Network (CAN) serves as the de facto standard for interconnecting these units, enabling critical functionalities. However, inherited non-delineation in SCs— transmits messages without explicit destination addressing—poses significant security risks, necessitating the evolution of an astute and resilient self-defense mechanism (SDM) to neutralize cyber threats. To this end, this study introduces a lightweight intrusion mitigation mechanism based on an adaptive momentum-based deep denoising autoencoder (AM-DDAE). Employing real-time CAN bus data from renowned smart vehicles, the proposed framework effectively reconstructs original data compromised by adversarial activities. Simulation results illustrate the efficacy of the AM-DDAE-based SDM, achieving a reconstruction error (RE) of less than 1% and an average execution time of 0.145532 s for data recovery. When validated on a new unseen attack, and on an Adversarial Machine Learning attack, the proposed model demonstrated equally strong performance with RE < 1%. Furthermore, the model’s decision-making capabilities were analysed using Explainable AI techinques such as SHAP and LIME. Additionally, the scheme offers applicable deployment flexibility: it can either be (a) embedded directly into individual ECU firmware or (b) implemented as a centralized hardware component interfacing between the CAN bus and ECUs, preloaded with the proposed mitigation algorithm. Full article

(This article belongs to the Special Issue Vehicular Communications for Cooperative and Automated Mobility)

► Show Figures

Graphical abstract

17 pages, 2803 KB

Open AccessArticle

Analysis of Moving Work Vehicles on Traffic Flow in City Tunnel

by Song Fang, Wenting Lu, Jianxiao Ma and Linghong Shen

World Electr. Veh. J. 2025, 16(9), 491; https://doi.org/10.3390/wevj16090491 - 1 Sep 2025

Viewed by 380

Abstract

Within urban tunnels, the lane boundary lines are typically solid, thereby prohibiting lane changes and overtaking. The establishment of a mobile operation zone in the slow lane can pose significant driving safety hazards not only to the slow lane within the tunnel but [...] Read more.

Within urban tunnels, the lane boundary lines are typically solid, thereby prohibiting lane changes and overtaking. The establishment of a mobile operation zone in the slow lane can pose significant driving safety hazards not only to the slow lane within the tunnel but also to the middle and overtaking lanes at the tunnel exit. This article adopts the method of simulation of the establishment of an urban expressway three-lane VISSIM model, and selects the road traffic volume and speed of moving work zone as the independent variable parameters. Then, the influence range of a low-speed vehicle on the rear vehicles in the middle lane and slow lane and the traffic risk caused by a low-speed vehicle are analyzed. The results show that, irrespective of the variations in traffic volume and moving operation zone speed, the traffic flow within a 150 m range after the tunnel section was significantly influenced. This was because queuing and congested vehicles from the slow lane exited the tunnel, causing vehicles to change lanes and overtake in a concentrated manner. The moving operation zone has a substantial impact on the traffic flow in the slow lane. Under different moving operation zone speed conditions, the speed change trend of the following vehicles is consistent. When the moving operation zone speed was 5 km/h and the traffic volume exceeded 1200 pcu/h, the traffic flow behind the operation zone was directly affected, and within an observable longitudinal distance of 500 m, this impact did not dissipate. The research results can provide a scientific basis for the operation and management of urban tunnel low-speed vehicles. Full article

(This article belongs to the Special Issue Vehicle Safe Motion in Mixed Vehicle Technologies Environment)

► Show Figures

Figure 1

25 pages, 3924 KB

Open AccessArticle

Research on Energy Saving for Hybrid Tractor Based on Working Condition Prediction and DDPG-Fuzzy Control

by Shilong Fan, Xianghai Yan, Shuaishuai Ge, Junjiang Zhang and Mengnan Liu

World Electr. Veh. J. 2025, 16(9), 490; https://doi.org/10.3390/wevj16090490 - 29 Aug 2025

Viewed by 545

Abstract

To significantly reduce fuel consumption and improve fuel economy in hybrid tractor under complex working conditions, an energy—saving strategy based on working condition prediction and Deep Deterministic Policy Gradient and Fuzzy control (DDPG-Fuzzy) was proposed. Firstly, a hybrid tractor system dynamics model containing [...] Read more.

To significantly reduce fuel consumption and improve fuel economy in hybrid tractor under complex working conditions, an energy—saving strategy based on working condition prediction and Deep Deterministic Policy Gradient and Fuzzy control (DDPG-Fuzzy) was proposed. Firstly, a hybrid tractor system dynamics model containing diesel, motor, and power battery was established. Secondly, a working condition prediction model for plowing velocity and resistance was constructed based on the adaptive cubic exponential smoothing method. Finally, a two-layer control architecture was designed. The upper layer adopted the DDPG algorithm, which takes demand torque, equivalent fuel consumption, and the State of Charge (SOC) as state inputs to optimize energy consumption by generating the diesel benchmark torque through the policy network. The lower layer introduced a fuzzy control compensation mechanism that calculates the torque correction based on the plowing velocity error and the plowing resistance deviation to adjust the power allocation. In light of on this, an energy—saving strategy for hybrid tractor based on working condition prediction and DDPG-Fuzzy control was proposed. Under a standard 140 s plowing cycle, the results showed that the working condition prediction model achieved mean prediction accuracies of 97% for plowing velocity and 96.8% for plowing resistance. Under plowing conditions, the proposed strategy reduced the equivalent fuel consumption by 9.7% compared to the power-following strategy, and reduced SOC by 4.4% while maintaining it within a reasonable range. By coordinating the operation of the diesel and motor within high-efficiency regions, this approach enhances fuel economy under complex working conditions. Full article

► Show Figures

Figure 1

Journal Menu

Journal Browser

World Electr. Veh. J., Volume 16, Issue 9 (September 2025) – 41 articles

Further Information

Guidelines

MDPI Initiatives

Follow MDPI