A Method for Predicting the Remaining Useful Life of Lithium Batteries Considering Capacity Regeneration and Random Fluctuations

Haipeng Pan, Chengte Chen and Minming Gu
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Haipeng Pan: School of Mechanical and Automatic, Zhejiang Sci-Tech University, Hangzhou 310018, China
Chengte Chen: School of Mechanical and Automatic, Zhejiang Sci-Tech University, Hangzhou 310018, China
Minming Gu: School of Mechanical and Automatic, Zhejiang Sci-Tech University, Hangzhou 310018, China

Energies, 2022, vol. 15, issue 7, 1-15

Abstract: Accurately predicting the remaining useful life (RUL) of lithium-ion batteries (LIBs) is important for electronic equipment. A new algorithm is proposed to aim at the nonlinear degradation caused by capacity regeneration and random fluctuations. Firstly, the health state degradation curve of LIBs is divided into the normal degradation trend part, capacity regeneration part, and random fluctuation part. Secondly, the capacity degradation curve of LIBs is decomposed by the empirical mode decomposition (EMD) to obtain the known long-term degradation trend part of LIBs. Then, the long short-term memory (LSTM) neural network is used to predict the future normal degradation trend part based on the known long-term degradation trend part of LIBs. In addition, the LIBs’ state of health (SOH), the initial state of charge (SOC), and the rest time are taken as the inputs of Gaussian process regression (GPR) to predict the LIBs’ capacity regeneration part. After that, random numbers obeying the Stable distribution are generated as the random fluctuation part of LIBs. Finally, the Monte Carlo simulation is used to predict the probability density distribution of the RUL of LIBs. The paper is verified by the LIBs’ public dataset provided by the University of Maryland. The experimental results show that the predicted RMSE of the proposed method is lower than 0.6%.

Keywords: lithium-ion battery; data-driven methods; capacity regeneration; random fluctuations; remaining useful life prediction (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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