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A Review of SOH Prediction of Li-Ion Batteries Based on Data-Driven Algorithms

Author

Listed:
  • Ming Zhang

    (School of Electrical Engineering, Weihai Innovation Research Institute, Qingdao University, Qingdao 266000, China)

  • Dongfang Yang

    (Xi’an Traffic Engineering Institute, Xi’an 710300, China)

  • Jiaxuan Du

    (Electrical Engineering and Automation, Northeast Electric Power University, Ji’lin 132012, China)

  • Hanlei Sun

    (School of Electrical Engineering, Weihai Innovation Research Institute, Qingdao University, Qingdao 266000, China)

  • Liwei Li

    (School of Control Science and Engineering, Shandong University, Jinan 250100, China)

  • Licheng Wang

    (School of Information Engineering, Zhejiang University of Technology, Hangzhou 310000, China)

  • Kai Wang

    (School of Electrical Engineering, Weihai Innovation Research Institute, Qingdao University, Qingdao 266000, China)

Abstract

As an important energy storage device, lithium-ion batteries (LIBs) have been widely used in various fields due to their remarkable advantages. The high level of precision in estimating the battery’s state of health greatly enhances the safety and dependability of the application process. In contrast to traditional model-based prediction methods that are complex and have limited accuracy, data-driven prediction methods, which are considered mainstream, rely on direct data analysis and offer higher accuracy. Therefore, this paper reviews how to use the latest data-driven algorithms to predict the SOH of LIBs, and proposes a general prediction process, including the acquisition of datasets for the charging and discharging process of LIBs, the processing of data and features, and the selection of algorithms. The advantages and limitations of various processing methods and cutting-edge data-driven algorithms are summarized and compared, and methods with potential applications are proposed. Effort was also made to point out their application methods and application scenarios, providing guidance for researchers in this area.

Suggested Citation

  • Ming Zhang & Dongfang Yang & Jiaxuan Du & Hanlei Sun & Liwei Li & Licheng Wang & Kai Wang, 2023. "A Review of SOH Prediction of Li-Ion Batteries Based on Data-Driven Algorithms," Energies, MDPI, vol. 16(7), pages 1-28, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:7:p:3167-:d:1113168
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    References listed on IDEAS

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    Cited by:

    1. Shigui Dong & Na Wang & Xueyan Wang & Zihao Lu, 2023. "Extended Recursive Three-Step Filter for Linear Discrete-Time Systems with Dual-Unknown Inputs," Energies, MDPI, vol. 16(15), pages 1-18, July.
    2. Peng Liu & Cheng Liu & Zhenpo Wang & Qiushi Wang & Jinlei Han & Yapeng Zhou, 2023. "A Data-Driven Comprehensive Battery SOH Evaluation and Prediction Method Based on Improved CRITIC-GRA and Att-BiGRU," Sustainability, MDPI, vol. 15(20), pages 1-15, October.
    3. Izabela Rojek & Dariusz Mikołajewski & Adam Mroziński & Marek Macko, 2023. "Machine Learning- and Artificial Intelligence-Derived Prediction for Home Smart Energy Systems with PV Installation and Battery Energy Storage," Energies, MDPI, vol. 16(18), pages 1-26, September.

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