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Review of State Estimation and Remaining Useful Life Prediction Methods for Lithium–Ion Batteries

Author

Listed:
  • Jiahui Zhao

    (China Huaneng Group, Clean Energy Research Institute (CERI), Beijing 102209, China)

  • Yong Zhu

    (China Huaneng Group, Clean Energy Research Institute (CERI), Beijing 102209, China)

  • Bin Zhang

    (China Huaneng Group, Clean Energy Research Institute (CERI), Beijing 102209, China)

  • Mingyi Liu

    (China Huaneng Group, Clean Energy Research Institute (CERI), Beijing 102209, China)

  • Jianxing Wang

    (China Huaneng Group, Clean Energy Research Institute (CERI), Beijing 102209, China)

  • Chenghao Liu

    (China Huaneng Group, Clean Energy Research Institute (CERI), Beijing 102209, China)

  • Xiaowei Hao

    (China Huaneng Group, Clean Energy Research Institute (CERI), Beijing 102209, China)

Abstract

The accurate estimation of the state of charge, the state of health and the prediction of remaining useful life of lithium–ion batteries is an important component of battery management. It is of great significance to prolong battery life and ensure the reliability of the battery system. Many researchers have completed a large amount of work on battery state evaluation and RUL prediction methods and proposed a variety of methods. This paper first introduces the definition of the SOC, the SOH and the existing estimation methods. Then, the definition of RUL is introduced, and the main methods are classified and compared. Finally, the challenges of lithium–ion battery state estimation and RUL prediction are summarized, and the direction for future development is proposed.

Suggested Citation

  • Jiahui Zhao & Yong Zhu & Bin Zhang & Mingyi Liu & Jianxing Wang & Chenghao Liu & Xiaowei Hao, 2023. "Review of State Estimation and Remaining Useful Life Prediction Methods for Lithium–Ion Batteries," Sustainability, MDPI, vol. 15(6), pages 1-22, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:5014-:d:1094734
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    References listed on IDEAS

    as
    1. Jianfang Jia & Jianyu Liang & Yuanhao Shi & Jie Wen & Xiaoqiong Pang & Jianchao Zeng, 2020. "SOH and RUL Prediction of Lithium-Ion Batteries Based on Gaussian Process Regression with Indirect Health Indicators," Energies, MDPI, vol. 13(2), pages 1-20, January.
    2. Saurabh Saxena & Darius Roman & Valentin Robu & David Flynn & Michael Pecht, 2021. "Battery Stress Factor Ranking for Accelerated Degradation Test Planning Using Machine Learning," Energies, MDPI, vol. 14(3), pages 1-17, January.
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    6. Xia, Bizhong & Chen, Chaoren & Tian, Yong & Wang, Mingwang & Sun, Wei & Xu, Zhihui, 2015. "State of charge estimation of lithium-ion batteries based on an improved parameter identification method," Energy, Elsevier, vol. 90(P2), pages 1426-1434.
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    Citations

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    Cited by:

    1. Yan Liu & Jun Chen & Jun Yong & Cheng Yang & Liqin Yan & Yanping Zheng, 2024. "Remaining Service Life Prediction of Lithium-Ion Batteries Based on Randomly Perturbed Traceless Particle Filtering," Energies, MDPI, vol. 17(21), pages 1-17, November.
    2. Xingxing Wang & Peilin Ye & Shengren Liu & Yu Zhu & Yelin Deng & Yinnan Yuan & Hongjun Ni, 2023. "Research Progress of Battery Life Prediction Methods Based on Physical Model," Energies, MDPI, vol. 16(9), pages 1-20, April.
    3. Hairui Wang & Xin Ye & Yuanbo Li & Guifu Zhu, 2023. "Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Improved Mode Decomposition and Time Series," Sustainability, MDPI, vol. 15(12), pages 1-23, June.
    4. Bingyu Sang & Zaijun Wu & Bo Yang & Junjie Wei & Youhong Wan, 2024. "Joint Estimation of SOC and SOH for Lithium-Ion Batteries Based on Dual Adaptive Central Difference H-Infinity Filter," Energies, MDPI, vol. 17(7), pages 1-16, March.
    5. Athar Ahmad & Mario Iamarino & Antonio D’Angola, 2024. "A Nernst-Based Approach for Modeling of Lithium-Ion Batteries with Non-Flat Voltage Characteristics," Energies, MDPI, vol. 17(16), pages 1-14, August.
    6. You, Yuqiang & Lin, Mingqiang & Meng, Jinhao & Wu, Ji & Wang, Wei, 2024. "Multi-scenario surface temperature estimation in lithium-ion batteries with transfer learning and LGT augmentation," Energy, Elsevier, vol. 304(C).
    7. Yanming Li & Xiaojuan Qin & Furong Ma & Haoran Wu & Min Chai & Fujing Zhang & Fenghe Jiang & Xu Lei, 2024. "Fusion Technology-Based CNN-LSTM-ASAN for RUL Estimation of Lithium-Ion Batteries," Sustainability, MDPI, vol. 16(21), pages 1-22, October.
    8. Lu Liu & Wei Sun & Chuanxu Yue & Yunhai Zhu & Weihuan Xia, 2024. "Remaining Useful Life Estimation of Lithium-Ion Batteries Based on Small Sample Models," Energies, MDPI, vol. 17(19), pages 1-17, October.

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