A Method for Predicting the Life of Lithium-Ion Batteries Based on Successive Variational Mode Decomposition and Optimized Long Short-Term Memory

Yongsheng Shi, Tailin Li (), Leicheng Wang, Hongzhou Lu, Yujun Hu, Beichen He and Xinran Zhai
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Yongsheng Shi: School of Electrical and Control Engineering, Shaanxi University of Science & Technology, Xi’an 710021, China
Tailin Li: School of Electrical and Control Engineering, Shaanxi University of Science & Technology, Xi’an 710021, China
Leicheng Wang: School of Electrical and Control Engineering, Shaanxi University of Science & Technology, Xi’an 710021, China
Hongzhou Lu: School of Electrical and Control Engineering, Shaanxi University of Science & Technology, Xi’an 710021, China
Yujun Hu: School of Electrical and Control Engineering, Shaanxi University of Science & Technology, Xi’an 710021, China
Beichen He: School of Electrical and Control Engineering, Shaanxi University of Science & Technology, Xi’an 710021, China
Xinran Zhai: School of Electrical and Control Engineering, Shaanxi University of Science & Technology, Xi’an 710021, China

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

Abstract: Accurately predicting the remaining lifespan of lithium-ion batteries is critical for the efficient and safe use of these devices. Predicting a lithium-ion battery’s remaining lifespan is challenging due to the non-linear changes in capacity that occur throughout the battery’s life. This study proposes a fused prediction model that employs a multimodal decomposition approach to address the problem of non-linear fluctuations during the degradation process of lithium-ion batteries. Specifically, the capacity attenuation signal is decomposed into multiple mode functions using successive variational mode decomposition (SVMD), which captures capacity fluctuations and a primary attenuation mode function to account for the degradation of lithium-ion batteries. The hyperparameters of the long short-term memory network (LSTM) are optimized using the tuna swarm optimization (TSO) technique. Subsequently, the trained prediction model is used to forecast various mode functions, which are then successfully integrated to obtain the capacity prediction result. The predictions show that the maximum percentage error for the projected results of five unique lithium-ion batteries, each with varying capacities and discharge rates, did not exceed 1%. Additionally, the average relative error remained within 2.1%. The fused lifespan prediction model, which integrates SVMD and the optimized LSTM, exhibited robustness, high predictive accuracy, and a degree of generalizability.

Keywords: remaining useful life prediction; successive variational mode decomposition; tuna swarm optimization; long short-term memory; random fluctuations (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
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