Battery state estimation methods and management system under vehicle–cloud collaboration: A Survey

Peng Mei, Hamid Reza Karimi, Jiale Xie, Fei Chen, Lei Ou, Shichun Yang and Cong Huang

Renewable and Sustainable Energy Reviews, 2024, vol. 206, issue C

Abstract: With the development of new energy vehicles, EVs have received ever-increasing research attention as an essential strategic orientation for the world to face climate change and energy issues. EVs have significant energy-saving and emission-reduction advantages, but power battery state estimation accuracy has always been a bottleneck restricting its promotion. Centered on power battery cloud management and control methodology, this work systematically examines the development of battery cloud models, formulates battery life and safety management strategies, and investigates the integration of cloud management technology within advanced electronic and electrical architectures. Firstly, the overall framework of the device–cloud fusion technology is introduced. Secondly, aiming at the complex problem of power battery state estimation, the models and fusion estimation methods of the cloud and vehicle battery models are summarized. Then, the joint estimation method is outlined for the power battery states, including the state of charge and state of health. Finally, a viable cloud-based management solution is elucidated through a comprehensive comparison and analysis of the current battery management technologies’ strengths and limitations. This offers a theoretical framework for advancing power battery cloud management and control technology.

Keywords: Electric vehicles; Vehicle–cloud collaboration; Battery state of charge; Battery state of health; Fusion estimation (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.rser.2024.114857

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