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SOH and RUL Prediction of Lithium-Ion Batteries Based on Gaussian Process Regression with Indirect Health Indicators

Author

Listed:
  • Jianfang Jia

    (School of Electrical and Control Engineering, North University of China, No. 3 XueYuan Road, JianCaoPing District, Taiyuan 030051, China)

  • Jianyu Liang

    (School of Electrical and Control Engineering, North University of China, No. 3 XueYuan Road, JianCaoPing District, Taiyuan 030051, China)

  • Yuanhao Shi

    (School of Electrical and Control Engineering, North University of China, No. 3 XueYuan Road, JianCaoPing District, Taiyuan 030051, China)

  • Jie Wen

    (School of Electrical and Control Engineering, North University of China, No. 3 XueYuan Road, JianCaoPing District, Taiyuan 030051, China)

  • Xiaoqiong Pang

    (School of Data Science and Technology, North University of China, No.3 XueYuan Road, JianCaoPing District, Taiyuan 030051, China)

  • Jianchao Zeng

    (School of Data Science and Technology, North University of China, No.3 XueYuan Road, JianCaoPing District, Taiyuan 030051, China)

Abstract

The state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries are two important factors which are normally predicted using the battery capacity. However, it is difficult to directly measure the capacity of lithium-ion batteries for online applications. In this paper, indirect health indicators (IHIs) are extracted from the curves of voltage, current, and temperature in the process of charging and discharging lithium-ion batteries, which respond to the battery capacity degradation process. A few reasonable indicators are selected as the inputs of SOH prediction by the grey relation analysis method. The short-term SOH prediction is carried out by combining the Gaussian process regression (GPR) method with probability predictions. Then, considering that there is a certain mapping relationship between SOH and RUL, three IHIs and the present SOH value are utilized to predict RUL of lithium-ion batteries through the GPR model. The results show that the proposed method has high prediction accuracy.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:2:p:375-:d:308036
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    References listed on IDEAS

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    5. Son, Seho & Jeong, Siheon & Kwak, Eunji & Kim, Jun-hyeong & Oh, Ki-Yong, 2022. "Integrated framework for SOH estimation of lithium-ion batteries using multiphysics features," Energy, Elsevier, vol. 238(PA).
    6. Yongsheng Shi & Tailin Li & Leicheng Wang & Hongzhou Lu & Yujun Hu & Beichen He & Xinran Zhai, 2023. "A Method for Predicting the Life of Lithium-Ion Batteries Based on Successive Variational Mode Decomposition and Optimized Long Short-Term Memory," Energies, MDPI, vol. 16(16), pages 1-16, August.
    7. Jiahui Zhao & Yong Zhu & Bin Zhang & Mingyi Liu & Jianxing Wang & Chenghao Liu & Yuanyuan Zhang, 2022. "Method of Predicting SOH and RUL of Lithium-Ion Battery Based on the Combination of LSTM and GPR," Sustainability, MDPI, vol. 14(19), pages 1-16, September.
    8. Jikai Bi & Jae-Cheon Lee & Hao Liu, 2022. "Performance Comparison of Long Short-Term Memory and a Temporal Convolutional Network for State of Health Estimation of a Lithium-Ion Battery using Its Charging Characteristics," Energies, MDPI, vol. 15(7), pages 1-24, March.
    9. Zhengyi Bao & Jiahao Jiang & Chunxiang Zhu & Mingyu Gao, 2022. "A New Hybrid Neural Network Method for State-of-Health Estimation of Lithium-Ion Battery," Energies, MDPI, vol. 15(12), pages 1-16, June.
    10. Rauf, Huzaifa & Khalid, Muhammad & Arshad, Naveed, 2022. "Machine learning in state of health and remaining useful life estimation: Theoretical and technological development in battery degradation modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    11. Juraj Bilansky & Milan Lacko & Marek Pastor & Adrian Marcinek & Frantisek Durovsky, 2023. "Improved Digital Twin of Li-Ion Battery Based on Generic MATLAB Model," Energies, MDPI, vol. 16(3), pages 1-21, January.
    12. Nagulapati, Vijay Mohan & Lee, Hyunjun & Jung, DaWoon & Brigljevic, Boris & Choi, Yunseok & Lim, Hankwon, 2021. "Capacity estimation of batteries: Influence of training dataset size and diversity on data driven prognostic models," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    13. Zhonghua Yun & Wenhu Qin & Weipeng Shi & Peng Ping, 2020. "State-of-Health Prediction for Lithium-Ion Batteries Based on a Novel Hybrid Approach," Energies, MDPI, vol. 13(18), pages 1-22, September.
    14. Jun Yuan & Zhili Qin & Haikun Huang & Xingdong Gan & Shuguang Li & Baihai Li, 2023. "State of Health Estimation and Remaining Useful Life Prediction for a Lithium-Ion Battery with a Two-Layer Stacking Regressor," Energies, MDPI, vol. 16(5), pages 1-15, February.

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