Journal
ENERGIES
Volume 13, Issue 2, Pages -Publisher
MDPI
DOI: 10.3390/en13020375
Keywords
lithium-ion batteries; state of health; remaining useful life; indirect health indicator; grey relation analysis; Gaussian process regression
Categories
Funding
- National Natural Science Foundation of China [61533013]
- key Program of Research and Development of Shanxi Province [201703D111011]
- Natural Science Foundation of Shanxi Province [201801D121159, 201801D221208, 201801D121188, 201901D111164]
- Graduate Education Innovation Program of Shanxi Province [2019SY459]
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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.
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