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| 文献清单：“深度学习在电动汽车中的应用”方向 | MDPI WEVJ |
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期刊名：World Electric Vehicle Journal (WEVJ)
期刊主页：https://www.mdpi.com/journal/wevj
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1. Energy Management Strategies for Hybrid Electric Vehicles: A Technology Roadmap
混合动力电动汽车的能源管理策略：技术路线图
https://www.mdpi.com/2032-6653/15/9/424
Mittal, V.; Shah, R. Energy Management Strategies for Hybrid Electric Vehicles: A Technology Roadmap.World Electr. Veh. J.2024,15, 424.
2. Using a YOLO Deep Learning Algorithm to Improve the Accuracy of 3D Object Detection by Autonomous Vehicles
使用YOLO深度学习算法提高自动驾驶汽车3D物体检测的准确性
https://www.mdpi.com/2032-6653/16/1/9
Murendeni, R.; Mwanza, A.; Obagbuwa, I.C. Using a YOLO Deep Learning Algorithm to Improve the Accuracy of 3D Object Detection by Autonomous Vehicles.World Electr. Veh. J.2025,16, 9.
3. A Smart Battery Management System for Electric Vehicles Using Deep Learning-Based Sensor Fault Detection
基于深度学习的传感器故障检测的电动汽车智能电池管理系统
https://www.mdpi.com/2032-6653/14/4/101
Kosuru, V.S.R.; Kavasseri Venkitaraman, A. A Smart Battery Management System for Electric Vehicles Using Deep Learning-Based Sensor Fault Detection.World Electr. Veh. J.2023,14, 101.
4. Novel Deep Learning Domain Adaptation Approach for Object Detection Using Semi-Self Building Dataset and Modified YOLOv4
使用半自建数据集和改进的YOLOv4的新型深度学习领域自适应方法进行目标检测
https://www.mdpi.com/2032-6653/15/6/255
Gomaa, A.; Abdalrazik, A. Novel Deep Learning Domain Adaptation Approach for Object Detection Using Semi-Self Building Dataset and Modified YOLOv4.World Electr. Veh. J.2024,15, 255.
5. A Review of Lithium-Ion Battery State of Charge Estimation Methods Based on Machine Learning
基于机器学习的锂离子电池荷电状态预估方法综述
https://www.mdpi.com/2032-6653/15/4/131
Zhao, F.; Guo, Y.; Chen, B. A Review of Lithium-Ion Battery State of Charge Estimation Methods Based on Machine Learning.World Electr. Veh. J.2024,15, 131.
6. An Overview of Diagnosis Methods of Stator Winding Inter-Turn Short Faults in Permanent-Magnet Synchronous Motors for Electric Vehicles
电动汽车用永磁同步电机定子绕组匝间短路故障诊断方法综述
https://www.mdpi.com/2032-6653/15/4/165
Jiang, Y.; Ji, B.; Zhang, J.; Yan, J.; Li, W. An Overview of Diagnosis Methods of Stator Winding Inter-Turn Short Faults in Permanent-Magnet Synchronous Motors for Electric Vehicles.World Electr. Veh. J.2024,15, 165.
7. SLAM Meets NeRF: A Survey of Implicit SLAM Methods
SLAM与NeRF相遇：隐式SLAM方法综述
https://www.mdpi.com/2032-6653/15/3/85
Yang, K.; Cheng, Y.; Chen, Z.; Wang, J. SLAM Meets NeRF: A Survey of Implicit SLAM Methods.World Electr. Veh. J.2024,15, 85.
8. A Systematic Review on the Integration of Artificial Intelligence into Energy Management Systems for Electric Vehicles: Recent Advances and Future Perspectives
系统综述：人工智能与电动汽车能源管理系统的融合——最新进展与未来展望
https://www.mdpi.com/2032-6653/15/8/364
Arévalo, P.; Ochoa-Correa, D.; Villa-Ávila, E. A Systematic Review on the Integration of Artificial Intelligence into Energy Management Systems for Electric Vehicles: Recent Advances and Future Perspectives.World Electr. Veh. J.2024,15, 364.
9. A Comparative Study of Traffic Signal Control Based on Reinforcement Learning Algorithms
一项基于强化学习算法的交通信号控制比较研究
https://www.mdpi.com/2032-6653/15/6/246
Ouyang, C.; Zhan, Z.; Lv, F. A Comparative Study of Traffic Signal Control Based on Reinforcement Learning Algorithms.World Electr. Veh. J.2024,15, 246.
10. A Multi-Sensor 3D Detection Method for Small Objects
一种用于小物体探测的多传感器三维检测方法
https://www.mdpi.com/2032-6653/15/5/210
Zhao, Y.; Luo, S.; Huang, X.; Wei, D. A Multi-Sensor 3D Detection Method for Small Objects.World Electr. Veh. J.2024,15, 210.
11. Safety–Efficiency Balanced Navigation for Unmanned Tracked Vehicles in Uneven Terrain Using Prior-Based Ensemble Deep Reinforcement Learning
基于先验的集成深度强化学习在不平地形中实现无人履带车辆的安全效率平衡导航
https://www.mdpi.com/2032-6653/16/7/359
Xu, Y.; Zhu, S.; Zhang, D.; Fang, Y.; Van, M. Safety–Efficiency Balanced Navigation for Unmanned Tracked Vehicles in Uneven Terrain Using Prior-Based Ensemble Deep Reinforcement Learning.World Electr. Veh. J.2025,16, 359.
12. Survey on Image-Based Vehicle Detection Methods
基于图像的车辆检测方法综述
https://www.mdpi.com/2032-6653/16/6/303
Adam, M.A.A.; Tapamo, J.R. Survey on Image-Based Vehicle Detection Methods.World Electr. Veh. J.2025,16, 303.
13. Multi-Agent Deep Reinforcement Learning Cooperative Control Model for Autonomous Vehicle Merging into Platoon in Highway
https://www.mdpi.com/2032-6653/16/4/225
高速公路自动驾驶车辆并入车队的多智能体深度强化学习协同控制模型
Chen, J.; Zhu, B.; Zhang, M.; Ling, X.; Ruan, X.; Deng, Y.; Guo, N. Multi-Agent Deep Reinforcement Learning Cooperative Control Model for Autonomous Vehicle Merging into Platoon in Highway.World Electr. Veh. J.2025,16, 225.
14. End-to-End Differentiable Physics Temperature Estimation for Permanent Magnet Synchronous Motor
永磁同步电机端到端可微物理温度估计
https://www.mdpi.com/2032-6653/15/4/174
Wang, P.; Wang, X.; Wang, Y. End-to-End Differentiable Physics Temperature Estimation for Permanent Magnet Synchronous Motor.World Electr. Veh. J.2024,15, 174.
15. Testing Scenario Identification for Automated Vehicles Based on Deep Unsupervised Learning
基于深度无监督学习的自动驾驶汽车测试场景识别
https://www.mdpi.com/2032-6653/14/8/208
Liu, S.; Ren, F.; Li, P.; Li, Z.; Lv, H.; Liu, Y. Testing Scenario Identification for Automated Vehicles Based on Deep Unsupervised Learning.World Electr. Veh. J.2023,14, 208.
WEVJ期刊介绍
主编:Joeri Van Mierlo, Vrije Universiteit Brussel, Belgium
WEVJ（ISSN 2032-6653）是首个全面涵盖电池电动汽车、混合动力电动汽车和燃料电池电动汽车相关研究的同行评审国际科学期刊。为响应学术界的需求，本刊旨在与国际电动汽车研讨会暨展览会（EVS）相辅相成。自1969年创办以来，国际电动汽车研讨会暨展览会（EVS）系列活动始终走在电动汽车领域的前沿，并已发展成为全球规模最大、最具影响力的电动汽车行业、学术界和研究盛会，展示了市场上已有的以及正在研发中的新兴技术。
2025 Impact Factor：3.3
2025 CiteScore：5.4
Time to First Decision：18.7 Days
Acceptance to Publication：3.7 Days
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