World Electric Vehicle Journal

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

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)

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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

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)

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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

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

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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

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)

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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

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

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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

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

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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

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

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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

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)

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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

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)

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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

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)

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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

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

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21 pages, 3228 KB

Open AccessArticle

Research on Active Collision Avoidance Control of Vehicles Based on Estimation of Road Surface Adhesion Coefficient

by Hongxiang Wang, Jian Wang and Ruofei Du

World Electr. Veh. J. 2025, 16(9), 489; https://doi.org/10.3390/wevj16090489 - 27 Aug 2025

Abstract

In order to solve the problem that intelligent vehicle active collision avoidance systems have different decision-making results under different road conditions, the square-root cubature Kalman filtering algorithm is used to estimate the road adhesion coefficients, which are introduced into the safety distance model [...] Read more.

In order to solve the problem that intelligent vehicle active collision avoidance systems have different decision-making results under different road conditions, the square-root cubature Kalman filtering algorithm is used to estimate the road adhesion coefficients, which are introduced into the safety distance model and combined with the fireworks algorithm for braking and steering weight coefficient allocation to ensure that the vehicle can safely avoid collision. The simulation results show that the square-root cubature Kalman filter has higher estimation accuracy and robustness compared with the cubature Kalman filter, and a more reasonable collision avoidance control can be adopted in the subsequent collision avoidance control. Therefore, the proposed new estimation method of road adhesion coefficients proves effective in mitigating vehicle collision risks. Full article

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17 pages, 3057 KB

Open AccessArticle

Torque Capability Enhancement of Interior Permanent Magnet Motors Using Filleting and Notching Stator

by Supanat Chamchuen, Kantapat Tonchua, Kunasin Khonongbua, Jonggrist Jongudomkarn, Apirat Siritaratiwat, Pirat Khunkitti and Pattasad Seangwong

World Electr. Veh. J. 2025, 16(9), 488; https://doi.org/10.3390/wevj16090488 - 26 Aug 2025

Abstract

Interior permanent magnet (IPM) synchronous motors have gained widespread adoption in electric vehicles (EVs) owing to their durable rotor configurations, expansive operational speed range, and superior efficiency. Nonetheless, typical IPM motor designs frequently exhibit high torque ripple and constrained torque density. To address [...] Read more.

Interior permanent magnet (IPM) synchronous motors have gained widespread adoption in electric vehicles (EVs) owing to their durable rotor configurations, expansive operational speed range, and superior efficiency. Nonetheless, typical IPM motor designs frequently exhibit high torque ripple and constrained torque density. To address these issues, a torque enhancement method is introduced by applying both filleting and notching techniques to the stator core. These techniques help reshape the magnetic field directly at the stator, allowing for more precise control of torque production and torque ripple reduction while keeping the rotor structure unchanged. Design variables of the stator in a 12-slot/8-pole fractional-slot V-shaped IPM motor are optimized using a multi-objective genetic algorithm based on a sensitivity constraint for unidirectional operation. The electromagnetic performance of the motor is analyzed through 2D finite element simulations for both no-load and loaded scenarios. The proposed motor increases average torque by 2.45% and significantly reduces torque ripple by 47.73% compared to the conventional motor. These reflect a significant advancement in torque capability. Furthermore, the efficiency of the proposed motor reaches 93.8%. The findings suggest the potential of the proposed filleting and notching techniques for torque capability improvement in EV applications. Full article

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27 pages, 5560 KB

Open AccessArticle

SOH Estimation of Lithium Battery Under Improved CNN-BIGRU-Attention Model Based on Hiking Optimization Algorithm

by Qianli Dong, Ziyang Liu, Hainan Wang, Lujun Wang, Rui Dong and Lu Lv

World Electr. Veh. J. 2025, 16(9), 487; https://doi.org/10.3390/wevj16090487 - 25 Aug 2025

Abstract

Accurate State of Health (SOH) estimation is critical for ensuring the safe operation of lithium-ion batteries. However, current data-driven approaches face significant challenges: insufficient feature extraction and ambiguous physical meaning compromise prediction accuracy, while initialization sensitivity to noise undermines stability; the inherent nonlinearity [...] Read more.

Accurate State of Health (SOH) estimation is critical for ensuring the safe operation of lithium-ion batteries. However, current data-driven approaches face significant challenges: insufficient feature extraction and ambiguous physical meaning compromise prediction accuracy, while initialization sensitivity to noise undermines stability; the inherent nonlinearity and temporal complexity of battery degradation data further lead to slow convergence or susceptibility to local optima. To address these limitations, this study proposes an enhanced CNN-BIGRU model. The model replaces conventional random initialization with a Hiking Optimization Algorithm (HOA) to identify superior initial weights, significantly improving early training stability. Furthermore, it integrates an Attention mechanism to dynamically weight features, strengthening the capture of key degradation characteristics. Rigorous experimental validation, utilizing multi-dimensional features extracted from the NASA dataset, demonstrates the model’s superior convergence speed and prediction accuracy compared to the CNN-BIGRU-Attention benchmark. Compared with other methods, the HOA-CNN-BIRGU-Attention model proposed in this study has a higher prediction accuracy and better robustness under different conditions, and the RMSEs on the NASA dataset are all controlled within 0.01, with R2 kept above 0.91. The RMSEs on the University of Maryland dataset are all below 0.006, with R2 kept above 0.98. Compared with the CNN-BIGRU-ATTENTION baseline model without HOA optimization, the RMSE is reduced by at least 0.15% across different battery groups in the NASA dataset. Full article

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21 pages, 3389 KB

Open AccessReview

Electric Tractors in China: Current Situation, Trends, and Potential

by Hongguang Yang, Feng Wu, Fengwei Gu, Hongbo Xu, Lili Shi, Xinsheng Zhou, Jiangtao Wang and Zhichao Hu

World Electr. Veh. J. 2025, 16(9), 486; https://doi.org/10.3390/wevj16090486 - 25 Aug 2025

Abstract

Tractors are widely used self-propelled power machinery. Electrification is one of the main directions for the green and low-carbon development of tractors. Currently, electric tractors have become one of the main research hotspots in countries around the world. This study provides a comprehensive [...] Read more.

Tractors are widely used self-propelled power machinery. Electrification is one of the main directions for the green and low-carbon development of tractors. Currently, electric tractors have become one of the main research hotspots in countries around the world. This study provides a comprehensive review of the research progress on electric tractors in China. Firstly, a brief analysis is conducted on the development history of electric tractors, the current research status in other countries around the world, and the situation regarding China’s tractor industry. Secondly, the classifications and characteristics of electric tractors are summarized. We focused on the research progress of electric tractor motors and their drive transmission systems, batteries, and energy management technology, as well as other key technologies. Finally, some opportunities and challenges faced by the development of electric tractors in China are pointed out from the aspects of market demand, national policies, and standard setting. Full article

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23 pages, 8223 KB

Open AccessArticle

Evaluating Visual eHMI Formats for Pedestrian Crossing Confirmation in Electric Autonomous Vehicles: A Comprehension-Time Study with Simulation and Preliminary Field Validation

by Nuksit Noomwongs, Natchanon Kitpramongsri, Sunhapos Chantranuwathana and Gridsada Phanomchoeng

World Electr. Veh. J. 2025, 16(9), 485; https://doi.org/10.3390/wevj16090485 - 25 Aug 2025

Abstract

Effective communication between electric autonomous vehicles (EAVs) and pedestrians is critical for safety, yet the absence of a driver removes traditional cues such as eye contact or gestures. While external human–machine interfaces (eHMIs) have been proposed, few studies have systematically compared visual formats [...] Read more.

Effective communication between electric autonomous vehicles (EAVs) and pedestrians is critical for safety, yet the absence of a driver removes traditional cues such as eye contact or gestures. While external human–machine interfaces (eHMIs) have been proposed, few studies have systematically compared visual formats across demographic groups and validated findings in both simulation and real-world settings. This study addresses this gap by evaluating various eHMI designs using combinations of textual cues (“WALK” and “CROSS”), symbolic indicators (pedestrian and arrow icons), and display colors (white and green). Twenty simulated scenarios were developed in the CARLA simulator, where 100 participants observed an EAV equipped with eHMIs and responded by pressing a button upon understanding the vehicle’s intention. The results showed that green displays facilitated faster comprehension than white, “WALK” was understood more quickly than “CROSS,” and pedestrian symbols outperformed arrows in clarity. The fastest overall comprehension occurred with the green pedestrian symbol paired with the word “WALK.” A subsequent field experiment using a Level 3 autonomous vehicle with a smaller participant group and differing speed/distance conditions provided preliminary support for the consistency of these observed trends. The novelty of this work lies in combining simulation with preliminary field validation, using comprehension time as the primary metric, and comparing results across four age groups to derive evidence-based eHMI design recommendations. These findings offer practical guidance for enhancing pedestrian safety, comprehension, and trust in EAV–pedestrian interactions. Full article

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19 pages, 1105 KB

Open AccessArticle

From Cell to Pack: Empirical Analysis of the Correlations Between Cell Properties and Battery Pack Characteristics of Electric Vehicles

by Jan Koloch, Mats Heienbrok, Maksymilian Kasperek and Markus Lienkamp

World Electr. Veh. J. 2025, 16(9), 484; https://doi.org/10.3390/wevj16090484 - 25 Aug 2025

Abstract

Lithium-ion batteries are pivotal components in battery electric vehicles, significantly influencing vehicle design and performance. This study investigates the interactions between cell properties and battery pack characteristics through statistical correlation analysis of datasets derived from industry-leading benchmarking platforms. Findings indicate that energy densities [...] Read more.

Lithium-ion batteries are pivotal components in battery electric vehicles, significantly influencing vehicle design and performance. This study investigates the interactions between cell properties and battery pack characteristics through statistical correlation analysis of datasets derived from industry-leading benchmarking platforms. Findings indicate that energy densities are comparable across cell formats at the pack level. While NMC and NCA chemistries outperform LFP in energy density at both cell and pack levels, LFP’s favorable cell-to-pack factors mitigate these differences. Analysis of cell properties suggests that increases in cell-level volumetric and gravimetric energy density result in proportionally smaller gains at the pack level due to the growing proportion of required passive components. The impact of cell chemistry and format on the z-dimension of a battery pack is analyzed in order to identify dependencies and influences between nominal cell properties and the geometry of the battery pack. The analysis suggests no significant influence of the used cell chemistry on the vertical dimension of a battery pack. The consideration of cell formats shows a dependency between the battery pack z-dimension and cell geometry, with prismatic cells reaching the highest pack heights and cylindrical cells being observed in packs of smaller vertical dimensions. The study also investigates the emerging sodium-ion battery technology and assesses pack-level energy densities derived from cell-level properties. The insights of this study contribute to the understanding of cell-to-pack relationships, guiding R&D toward improved energy storage solutions for electric vehicles. Full article

(This article belongs to the Special Issue EVS38—International Electric Vehicle Symposium and Exhibition (Gothenburg, Sweden))

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25 pages, 4739 KB

Open AccessArticle

YOLOv5s-F: An Improved Algorithm for Real-Time Monitoring of Small Targets on Highways

by Jinhao Guo, Guoqing Geng, Liqin Sun and Zhifan Ji

World Electr. Veh. J. 2025, 16(9), 483; https://doi.org/10.3390/wevj16090483 - 25 Aug 2025

Abstract

To address the challenges of real-time monitoring via highway vehicle-mounted cameras—specifically, the difficulty in detecting distant pedestrians and vehicles in real time—this study proposes an enhanced object detection algorithm, YOLOv5s-F. Firstly, the FasterNet network structure is adopted to improve the model’s runtime speed. [...] Read more.

To address the challenges of real-time monitoring via highway vehicle-mounted cameras—specifically, the difficulty in detecting distant pedestrians and vehicles in real time—this study proposes an enhanced object detection algorithm, YOLOv5s-F. Firstly, the FasterNet network structure is adopted to improve the model’s runtime speed. Secondly, the attention mechanism BRA, which is derived from the Transformer algorithm, and a 160 × 160 small-object detection layer are introduced to enhance small target detection performance. Thirdly, the improved upsampling operator CARAFE is incorporated to boost the localization and classification accuracy of small objects. Finally, Focal EIoU is employed as the localization loss function to accelerate model training convergence. Quantitative experiments on high-speed sequences show that Focal EIoU reduces bounding box jitter by 42.9% and improves tracking stability (consecutive frame overlap) by 11.4% compared to CIoU, while accelerating convergence by 17.6%. Results show that compared with the YOLOv5s baseline network, the proposed algorithm reduces computational complexity and parameter count by 10.1% and 24.6%, respectively, while increasing detection speed and accuracy by 15.4% and 2.1%. Transfer learning experiments on the VisDrone2019 and Highway-100k dataset demonstrate that the algorithm outperforms YOLOv5s in average precision across all target categories. On NVIDIA Jetson Xavier NX, YOLOv5s-F achieves 32 FPS after quantization, meeting the real-time requirements of in-vehicle monitoring. The YOLOv5s-F algorithm not only meets the real-time detection and accuracy requirements for small objects but also exhibits strong generalization capabilities. This study clarifies core challenges in highway small-target detection and achieves accuracy–speed improvements via three key innovations, with all experiments being reproducible. If any researchers need the code and dataset of this study, they can consult the author through email. Full article

(This article belongs to the Special Issue Recent Advances in Autonomous Vehicles)

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18 pages, 3345 KB

Open AccessArticle

Autonomous Public Transport: Evolution, Benefits, and Challenges in the Future of Urban Mobility

by Dalia Hafiz, Mariam AlKhafagy and Ismail Zohdy

World Electr. Veh. J. 2025, 16(9), 482; https://doi.org/10.3390/wevj16090482 - 25 Aug 2025

Abstract

Autonomous public transport (APT) is revolutionizing urban mobility by integrating advanced technologies, including electric autonomous buses and shared autonomous vehicles (SAVs). This paper examines the historical evolution of APT, from early automation efforts in the 1920s to the deployment of autonomous shuttles in [...] Read more.

Autonomous public transport (APT) is revolutionizing urban mobility by integrating advanced technologies, including electric autonomous buses and shared autonomous vehicles (SAVs). This paper examines the historical evolution of APT, from early automation efforts in the 1920s to the deployment of autonomous shuttles in contemporary cities. It highlights technological milestones, legislative developments, and shifts in public perception that have influenced the adoption of APT. The research identifies key benefits of APT, including enhanced road safety, reduced greenhouse gas emissions, and improved cost-efficiency in public transport operations. Additionally, the environmental potential of SAVs to reduce traffic congestion and emissions is explored, particularly when integrated with renewable energy sources and sustainable urban planning. However, the study also addresses significant challenges, such as handling emergencies without human intervention, rising cybersecurity threats, and employment displacement in the transportation sector. Social equity concerns are also discussed, especially regarding access and the risk of increasing urban inequality. This paper contributes to the broader discourse on sustainable mobility, transportation innovation, and the future of smart cities by providing a comprehensive analysis of both opportunities and obstacles. Effective policy frameworks and inclusive planning are essential for the successful implementation of APT systems worldwide. Full article

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