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Prognosis of Lithium-Ion Batteries’ Remaining Useful Life Based on a Sequence-to-Sequence Model with Variational Mode Decomposition

Author

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  • Chunxiang Zhu

    (School of Electronics and Information Engineering, Hangzhou Dianzi University, Hangzhou 310018, China
    Zhejiang Provincial Key Lab of Equipment Electronics, Hangzhou 310018, China
    College of Engineering Training Centre, China Jiliang University, Hangzhou 310018, China)

  • Zhiwei He

    (School of Electronics and Information Engineering, Hangzhou Dianzi University, Hangzhou 310018, China
    Zhejiang Provincial Key Lab of Equipment Electronics, Hangzhou 310018, China)

  • Zhengyi Bao

    (School of Electronics and Information Engineering, Hangzhou Dianzi University, Hangzhou 310018, China
    Zhejiang Provincial Key Lab of Equipment Electronics, Hangzhou 310018, China)

  • Changcheng Sun

    (School of Electronics and Information Engineering, Hangzhou Dianzi University, Hangzhou 310018, China
    Zhejiang Provincial Key Lab of Equipment Electronics, Hangzhou 310018, China)

  • Mingyu Gao

    (School of Electronics and Information Engineering, Hangzhou Dianzi University, Hangzhou 310018, China
    Zhejiang Provincial Key Lab of Equipment Electronics, Hangzhou 310018, China)

Abstract

The time-varying, dynamic, nonlinear, and other characteristics of lithium-ion batteries, as well as the capacity regeneration phenomenon, leads to the low accuracy of the traditional deep learning models in predicting the remaining useful life of lithium-ion batteries. This paper established a sequence-to-sequence model for remaining useful life prediction by combining the variational modal decomposition with bi-directional long short-term memory and Bayesian hyperparametric optimization. First, variational modal decomposition is used for noise reduction processing to maximize the retention of the original information of capacity degradation. Second, the capacity declining trend after noise reduction is modeled and predicted by the combination of bi-directional long-short term memory and temporal attention mechanism. In addition, a Bayesian optimizer is used to adaptively adjust the hyperparameters while training the model. Finally, the model was validated on NASA and CALCE data sets, and the results show that the model can accurately predict the future trend with only the initial 12% capacity data.

Suggested Citation

  • Chunxiang Zhu & Zhiwei He & Zhengyi Bao & Changcheng Sun & Mingyu Gao, 2023. "Prognosis of Lithium-Ion Batteries’ Remaining Useful Life Based on a Sequence-to-Sequence Model with Variational Mode Decomposition," Energies, MDPI, vol. 16(2), pages 1-16, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:2:p:803-:d:1031238
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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.

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