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Indirect Prediction of Lithium-Ion Battery RUL Based on CEEMDAN and CNN-BiGRU

Author

Listed:
  • Kai Lv

    (School of Data Science and Application, Inner Mongolia University of Technology, Hohhot 010080, China)

  • Zhiqiang Ma

    (School of Data Science and Application, Inner Mongolia University of Technology, Hohhot 010080, China
    Engineering Research Center of Large-Scale Energy Storage Technology, Ministry of Education, Hohhot 010080, China)

  • Caijilahu Bao

    (School of Data Science and Application, Inner Mongolia University of Technology, Hohhot 010080, China)

  • Guangchen Liu

    (Engineering Research Center of Large-Scale Energy Storage Technology, Ministry of Education, Hohhot 010080, China
    School of Electric Power, Inner Mongolia University of Technology, Hohhot 010080, China)

Abstract

Predicting the remaining useful life (RUL) of lithium-ion batteries is crucial for enhancing their reliability and safety. Addressing the issue of inaccurate RUL predictions caused by the nonlinear decay resulting from capacity regeneration, this paper proposes an indirect lithium-ion battery RUL prediction method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and convolutional neural network (CNN)–bidirectional gated recurrent unit (BiGRU). The method extracts Health Indicators (HI) from the battery-charging stage and employs CEEMDAN to decompose HI into several components. These components are then input into a component prediction model for forecasting. Finally, the predicted component results are fused and input into a capacity prediction model to achieve indirect RUL prediction. Validation is conducted using the lithium-ion battery dataset provided by NASA. The results indicate that, under prediction starting points (STs) of 80 and 100, the maximum average absolute errors do not exceed 0.0096 and 0.0081, and the maximum root mean square errors do not exceed 0.0196 and 0.0115, demonstrating high precision and reliability.

Suggested Citation

  • Kai Lv & Zhiqiang Ma & Caijilahu Bao & Guangchen Liu, 2024. "Indirect Prediction of Lithium-Ion Battery RUL Based on CEEMDAN and CNN-BiGRU," Energies, MDPI, vol. 17(7), pages 1-19, April.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:7:p:1704-:d:1369160
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    References listed on IDEAS

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    1. Lin, Chun-Pang & Cabrera, Javier & Yang, Fangfang & Ling, Man-Ho & Tsui, Kwok-Leung & Bae, Suk-Joo, 2020. "Battery state of health modeling and remaining useful life prediction through time series model," Applied Energy, Elsevier, vol. 275(C).
    2. Chen, Zewang & Shi, Na & Ji, Yufan & Niu, Mu & Wang, Youren, 2021. "Lithium-ion batteries remaining useful life prediction based on BLS-RVM," Energy, Elsevier, vol. 234(C).
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