SOH and RUL Prediction of Lithium-Ion Batteries Based on Gaussian Process Regression with Indirect Health Indicators

Jianfang Jia, Jianyu Liang, Yuanhao Shi, Jie Wen, Xiaoqiong Pang and Jianchao Zeng
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Jianfang Jia: School of Electrical and Control Engineering, North University of China, No. 3 XueYuan Road, JianCaoPing District, Taiyuan 030051, China
Jianyu Liang: School of Electrical and Control Engineering, North University of China, No. 3 XueYuan Road, JianCaoPing District, Taiyuan 030051, China
Yuanhao Shi: School of Electrical and Control Engineering, North University of China, No. 3 XueYuan Road, JianCaoPing District, Taiyuan 030051, China
Jie Wen: School of Electrical and Control Engineering, North University of China, No. 3 XueYuan Road, JianCaoPing District, Taiyuan 030051, China
Xiaoqiong Pang: School of Data Science and Technology, North University of China, No.3 XueYuan Road, JianCaoPing District, Taiyuan 030051, China
Jianchao Zeng: School of Data Science and Technology, North University of China, No.3 XueYuan Road, JianCaoPing District, Taiyuan 030051, China

Energies, 2020, vol. 13, issue 2, 1-20

Abstract: The state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries are two important factors which are normally predicted using the battery capacity. However, it is difficult to directly measure the capacity of lithium-ion batteries for online applications. In this paper, indirect health indicators (IHIs) are extracted from the curves of voltage, current, and temperature in the process of charging and discharging lithium-ion batteries, which respond to the battery capacity degradation process. A few reasonable indicators are selected as the inputs of SOH prediction by the grey relation analysis method. The short-term SOH prediction is carried out by combining the Gaussian process regression (GPR) method with probability predictions. Then, considering that there is a certain mapping relationship between SOH and RUL, three IHIs and the present SOH value are utilized to predict RUL of lithium-ion batteries through the GPR model. The results show that the proposed method has high prediction accuracy.

Keywords: lithium-ion batteries; state of health; remaining useful life; indirect health indicator; grey relation analysis; Gaussian process regression (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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

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