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

• Published in: Energies (Basel) Vol. 13; no. 2; p. 375
• Main Authors: Jia, Jianfang, Liang, Jianyu, Shi, Yuanhao, Wen, Jie, Pang, Xiaoqiong, Zeng, Jianchao
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-01-2020
• Subjects: Accuracy; Aging; Artificial intelligence; Datasets; Electric vehicles; Gaussian process; gaussian process regression; grey relation analysis; Indicators; indirect health indicator; Lithium; Lithium-ion batteries; Mapping; Methods; Neural networks; Normal distribution; Parameter identification; Predictions; remaining useful life; Researchers; state of health; Statistical analysis; Support vector machines
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en13020375

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.