Remaining-Useful-Life Prediction for Li-Ion Batteries

Yeong-Hwa Chang (), Yu-Chen Hsieh, Yu-Hsiang Chai and Hung-Wei Lin
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Yeong-Hwa Chang: Department of Electrical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
Yu-Chen Hsieh: Department of Electrical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
Yu-Hsiang Chai: Department of Electrical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
Hung-Wei Lin: Department of Electrical Engineering, Chang Gung University, Taoyuan City 333, Taiwan

Energies, 2023, vol. 16, issue 7, 1-20

Abstract: This paper aims to establish a predictive model for battery lifetime using data analysis. The procedure of model establishment is illustrated in detail, including the data pre-processing, modeling, and prediction. The characteristics of lithium-ion batteries are introduced. In this study, data analysis is performed with MATLAB, and the open-source battery data are provided by NASA. The addressed models include the decision tree, nonlinear autoregression, recurrent neural network, and long short-term memory network. In the part of model training, the root-mean-square error, integral of the squared error, and integral of the absolute error are considered for the cost functions. Based on the defined health indicator, the remaining useful life of lithium-ion batteries can be predicted. The confidence interval can be used to describe the level of confidence for each prediction. According to the test results, the long short-term memory network provides the best performance among all addressed models.

Keywords: lithium-ion battery; predictive maintenance; remaining useful life (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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