Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Improved Mode Decomposition and Time Series

Hairui Wang, Xin Ye, Yuanbo Li and Guifu Zhu ()
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Hairui Wang: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
Xin Ye: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
Yuanbo Li: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
Guifu Zhu: Information Technology Construction Management Center, Kunming University of Science and Technology, Kunming 650500, China

Sustainability, 2023, vol. 15, issue 12, 1-23

Abstract: Accurately predicting the remaining useful life (RUL) of lithium-ion batteries holds significant importance for their health management. Due to the capacity regeneration phenomenon and random interference during the operation of lithium-ion batteries, a single model may exhibit poor prediction accuracy and generalization performance under a single scale signal. This paper proposes a method for predicting the RUL of lithium-ion batteries. The method is based on the improved sparrow search algorithm (ISSA), which optimizes the variational mode decomposition (VMD) and long- and short-term time-series network (LSTNet). First, this study utilized the ISSA-optimized VMD method to decompose the capacity degradation sequence of lithium-ion batteries, acquiring global degradation trend components and local capacity recovery components, then the ISSA–LSTNet–Attention model and ISSA–LSTNet–Skip model were employed to predict the trend component and capacity recovery component, respectively. Finally, the prediction results of these different models were integrated to accurately estimate the RUL of lithium-ion batteries. The proposed model was tested on two public lithium-ion battery datasets; the results indicate a root mean square error (RMSE) under 2%, a mean absolute error (MAE) under 1.5%, and an absolute correlation coefficient ( R 2 ) and Nash–Sutcliffe efficiency index (NSE) both above 92.9%, implying high prediction accuracy and superior performance compared to other models. Moreover, the model significantly reduces the complexity of the series.

Keywords: lithium-ion battery; variational mode decomposition; remaining useful life prediction; long and short-term time-series network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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