Recent Advances in Intelligent Energy Management and Battery Management for Hybrid/Electric Vehicles

Dear Colleagues,

Due to the energy crisis and environmental pollution, new energy/electric vehicles have been paid more and more attention and become the main direction of future automobile development. According to research conducted by Favigham in the United States, by 2035, the total number of vehicles in the world will be at least 2 billion, but electric vehicles will only account for one-tenth. Compared with traditional vehicles (internal combustion engine vehicles), electric vehicles (EVs) are energy-saving and environmentally friendly. Hybrid electric vehicles (HEVs) are a form between traditional vehicles and EVs, which can reduce fuel consumption and reduce emissions. Among them, plug-in HEVs are classified as new energy vehicles, while non-plug-in HEVs still belong to traditional vehicles.

During the operation of the vehicle, the estimation error of the internal state of the battery such as SOC, SOH, and SOP is large, and as the battery ages, the estimation error becomes larger and larger, so that the battery management system (BMS) cannot monitor the actual state of the battery, which can easily lead to the battery overcharge and over-discharge, accelerated aging, reduced usable capacity, and shortened remaining life. On the other hand, the energy management system cannot accurately allocate power requirements according to the actual state of the battery, resulting in the unreasonable implementation of energy management strategies, increased battery loss, and halfway breakdown.

Therefore, there is an urgent need to investigate new strategies and promising approaches for intelligent energy management and battery management for hybrid/electric vehicles. With this Special Issue, we aim to provide an overview of recent advances in artificial intelligence/machine learning/deep learning for energy management and battery management and their applications in different fields. A further aim of this Special Issue is to provide a contribution to advances in modeling, estimation, optimal control, optimal charging, energy management of electric vehicles and applications of related devices and components.

Potential topics include, but are not limited to:

• Electric vehicles
• Fuel cell vehicles
• Hybrid vehicles
• Plug-in vehicles
• Electric vehicle batteries
• Electric vehicle battery management systems
• Electric vehicle charging systems
• Autonomous driving
• Autonomous vehicles
• Performance of electric vehicles
• Artificial intelligence applications for vehicles and traffic
• Machine learning/deep learning algorithms for vehicles and transportation

Dr. Qi Zhang
Dr. Wenhui Pei
Prof. Dr. Xiaoling Fu
Dr. Zhongkai Zhou
Guest Editors

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• artificial intelligence
• hybrid/electric vehicles
• batteries and management systems
• autonomous driving/vehicles
• machine learning