Method of Predicting SOH and RUL of Lithium-Ion Battery Based on the Combination of LSTM and GPR

Jiahui Zhao, Yong Zhu (), Bin Zhang, Mingyi Liu, Jianxing Wang, Chenghao Liu and Yuanyuan Zhang
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Jiahui Zhao: China Huaneng Group Clean Energy Research Institute (CERI), Beijing 102209, China
Yong Zhu: China Huaneng Group Clean Energy Research Institute (CERI), Beijing 102209, China
Bin Zhang: China Huaneng Group Clean Energy Research Institute (CERI), Beijing 102209, China
Mingyi Liu: China Huaneng Group Clean Energy Research Institute (CERI), Beijing 102209, China
Jianxing Wang: China Huaneng Group Clean Energy Research Institute (CERI), Beijing 102209, China
Chenghao Liu: China Huaneng Group Clean Energy Research Institute (CERI), Beijing 102209, China
Yuanyuan Zhang: China Huaneng Group Clean Energy Research Institute (CERI), Beijing 102209, China

Sustainability, 2022, vol. 14, issue 19, 1-16

Abstract: The state of health and remaining useful life of lithium-ion batteries are important indicators to ensure the reliable operation of these batteries. However, because they cannot be directly measured and are affected by many factors, they are difficult to predict. This paper presents method of jointly predicting state of health and RUL based on the long short-term memory neural network and Gaussian process regression. This method extracts the batteries’ health factors from the charging curve, selects health factors with more relevance than the setting standard as the characteristic of capacity by the maximum information coefficient method, and establishes the battery aging and remaining useful life prediction models with Gaussian process regression. On this basis, the long short-term memory neural network is used to predict the trend of the change in health factors with the increase in cycles, and the results are input into a Gaussian process regression aging model to predict the state of health. Taking the health factors and state of health as the characteristics of remaining useful battery life, a battery remaining useful life model based on Gaussian process regression is established, and the change trend in the remaining useful life can be obtained by inputting the predicted health factors and state of health. In this study, four battery data sets with different depths of charge were used to verify the accuracy and adaptability of the algorithm. The results show that the proposed algorithm has high accuracy and reliability.

Keywords: lithium-ion battery; state of health; remaining useful life; long short-term memory; Gaussian process regression (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:19:p:11865-:d:920420

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