Proceedings of the International Conference on Information Economy, Data Modeling and Cloud Computing, ICIDC 2022, 17-19 June 2022, Qingdao, China

Research Article

State-of-Health Estimation of Lithium-ion Battery Based on Data-driven

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  • @INPROCEEDINGS{10.4108/eai.17-6-2022.2322708,
        author={Jingyi  Gao and Ping  Li and Yisu  Yan and Mengjie  Guo and Kai  Wang},
        title={State-of-Health Estimation of Lithium-ion Battery Based on Data-driven},
        proceedings={Proceedings of the International Conference on Information Economy, Data Modeling and Cloud Computing, ICIDC 2022, 17-19 June 2022, Qingdao, China},
        publisher={EAI},
        proceedings_a={ICIDC},
        year={2022},
        month={10},
        keywords={state-of-health; lithium-ion batteries; machine learning; data-driven model},
        doi={10.4108/eai.17-6-2022.2322708}
    }
    
  • Jingyi Gao
    Ping Li
    Yisu Yan
    Mengjie Guo
    Kai Wang
    Year: 2022
    State-of-Health Estimation of Lithium-ion Battery Based on Data-driven
    ICIDC
    EAI
    DOI: 10.4108/eai.17-6-2022.2322708
Jingyi Gao1, Ping Li2, Yisu Yan3, Mengjie Guo4, Kai Wang1,*
  • 1: Qingdao University
  • 2: Dongying District Science and Technology Bureau
  • 3: Qingdao Dagang Customs
  • 4: Shandong Wide Area Technology Co., Ltd
*Contact email: wkwj888@163.com

Abstract

With the increase of the output of electric vehicles, it is of great significance to predict the health status of lithium-ion batteries for the safe operation of electric vehicles. In this paper, some common data-driven methods for health state estimation of lithium-ion batteries are reviewed. First of all, this paper introduces the charge and discharge principle of lithium-ion battery. Then four common SOH prediction methods are introduced, and their advantages and disadvantages are summarized and reviewed. In the part of introducing the data-driven research on the health status of lithium-ion battery, it focuses on the application of machine learning and deep neural network. Finally, the research prospect of health state estimation of lithium-ion battery is explained.