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Remaining-Useful-Life Prediction for Li-Ion Batteries

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  • Yeong-Hwa Chang

    (Department of Electrical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
    Department of Electrical Engineering, Ming Chi University of Technology, New Taipei City 243, 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)

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.

Suggested Citation

  • Yeong-Hwa Chang & Yu-Chen Hsieh & Yu-Hsiang Chai & Hung-Wei Lin, 2023. "Remaining-Useful-Life Prediction for Li-Ion Batteries," Energies, MDPI, vol. 16(7), pages 1-20, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:7:p:3096-:d:1110074
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    References listed on IDEAS

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