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Lithium-Ion Battery Remaining Useful Life Prediction Based on Hybrid Model

Author

Listed:
  • Xuliang Tang

    (School of Railway Transportation, Shanghai Institute of Technology, Shanghai 201418, China)

  • Heng Wan

    (School of Railway Transportation, Shanghai Institute of Technology, Shanghai 201418, China)

  • Weiwen Wang

    (Shanghai MTR No.1 Operation Co., Ltd., Shanghai 201418, China)

  • Mengxu Gu

    (School of Railway Transportation, Shanghai Institute of Technology, Shanghai 201418, China)

  • Linfeng Wang

    (School of Railway Transportation, Shanghai Institute of Technology, Shanghai 201418, China)

  • Linfeng Gan

    (School of Railway Transportation, Shanghai Institute of Technology, Shanghai 201418, China)

Abstract

Accurate prediction of the remaining useful life (RUL) is a key function for ensuring the safety and stability of lithium-ion batteries. To solve the capacity regeneration and model adaptability under different working conditions, a hybrid RUL prediction model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and a bi-directional gated recurrent unit (BiGRU) is proposed. CEEMDAN is used to divide the capacity into intrinsic mode functions (IMFs) to reduce the impact of capacity regeneration. In addition, an improved grey wolf optimizer (IGOW) is proposed to maintain the reliability of the BiGRU network. The diversity of the initial population in the GWO algorithm was improved using chaotic tent mapping. An improved control factor and dynamic population weight are adopted to accelerate the convergence speed of the algorithm. Finally, capacity and RUL prediction experiments are conducted to verify the battery prediction performance under different training data and working conditions. The results indicate that the proposed method can achieve an MAE of less than 4% with only 30% of the training set, which is verified using the CALCE and NASA battery data.

Suggested Citation

  • Xuliang Tang & Heng Wan & Weiwen Wang & Mengxu Gu & Linfeng Wang & Linfeng Gan, 2023. "Lithium-Ion Battery Remaining Useful Life Prediction Based on Hybrid Model," Sustainability, MDPI, vol. 15(7), pages 1-18, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:7:p:6261-:d:1116838
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Zhengyang Fan & Wanru Li & Kuo-Chu Chang, 2023. "A Bidirectional Long Short-Term Memory Autoencoder Transformer for Remaining Useful Life Estimation," Mathematics, MDPI, vol. 11(24), pages 1-17, December.
    3. Giovane Ronei Sylvestrin & Joylan Nunes Maciel & Marcio Luís Munhoz Amorim & João Paulo Carmo & José A. Afonso & Sérgio F. Lopes & Oswaldo Hideo Ando Junior, 2025. "State of the Art in Electric Batteries’ State-of-Health (SoH) Estimation with Machine Learning: A Review," Energies, MDPI, vol. 18(3), pages 1-77, February.
    4. Yuqi Liang & Shuai Zhao, 2024. "Early Prediction of Remaining Useful Life for Lithium-Ion Batteries with the State Space Model," Energies, MDPI, vol. 17(24), pages 1-16, December.
    5. Zhu, Tao & Wang, Shunli & Fan, Yongcun & Hai, Nan & Huang, Qi & Fernandez, Carlos, 2024. "An improved dung beetle optimizer- hybrid kernel least square support vector regression algorithm for state of health estimation of lithium-ion batteries based on variational model decomposition," Energy, Elsevier, vol. 306(C).
    6. 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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