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Open AccessArticle

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

1
School of Electrical and Control Engineering, North University of China, No. 3 XueYuan Road, JianCaoPing District, Taiyuan 030051, China
2
School of Data Science and Technology, North University of China, No.3 XueYuan Road, JianCaoPing District, Taiyuan 030051, China
*
Author to whom correspondence should be addressed.
Energies 2020, 13(2), 375; https://doi.org/10.3390/en13020375
Received: 16 December 2019 / Revised: 7 January 2020 / Accepted: 8 January 2020 / Published: 13 January 2020
(This article belongs to the Special Issue Testing and Management of Lithium-Ion Batteries)
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. View Full-Text
Keywords: lithium-ion batteries; state of health; remaining useful life; indirect health indicator; grey relation analysis; Gaussian process regression lithium-ion batteries; state of health; remaining useful life; indirect health indicator; grey relation analysis; Gaussian process regression
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MDPI and ACS Style

Jia, J.; Liang, J.; Shi, Y.; Wen, J.; Pang, X.; Zeng, J. SOH and RUL Prediction of Lithium-Ion Batteries Based on Gaussian Process Regression with Indirect Health Indicators. Energies 2020, 13, 375.

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