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

Chunxiang Zhu, Zhiwei He, Zhengyi Bao, Changcheng Sun and Mingyu Gao ()
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Chunxiang Zhu: School of Electronics and Information Engineering, Hangzhou Dianzi University, Hangzhou 310018, China
Zhiwei He: School of Electronics and Information Engineering, Hangzhou Dianzi University, Hangzhou 310018, China
Zhengyi Bao: School of Electronics and Information Engineering, Hangzhou Dianzi University, Hangzhou 310018, China
Changcheng Sun: School of Electronics and Information Engineering, Hangzhou Dianzi University, Hangzhou 310018, China
Mingyu Gao: School of Electronics and Information Engineering, Hangzhou Dianzi University, Hangzhou 310018, China

Energies, 2023, vol. 16, issue 2, 1-16

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.

Keywords: remaining useful life prediction; sequence-to-sequence deep learning; variational mode decomposition; bi-directional long short-term memory (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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