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.
Highlights
Lithium-ion batteries are the core power sources for electric vehicles (EVs), consumer electronics, and even spacecraft, etc. [1,2,3]
The indirect prediction process of state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries based on Gaussian process regression (GPR) is divided into the following seven steps: Extract data: In the data set provided by NASA, the capacity data set C is collected, and the data sets of eight
This research focused on the fact that in some practical applications, battery capacity cannot be obtained through online measurement, so it is difficult to predict the SOH and RUL of the battery
Summary
Lithium-ion batteries are the core power sources for electric vehicles (EVs), consumer electronics, and even spacecraft, etc. [1,2,3]. [26,27,28], Yang et al [29] extracted four specific parameters from charging curves, and used them as an input of the GPR model instead of cycle numbers This method was more meaningful in practical application, but by considering only the charging process voltage change curve, some parameters showed low correlation with capacity, which resulted in reduced prediction accuracy. The main contribution of this paper is that the proposed method can predict the short-term SOH and long-term RUL of lithium-ion batteries with indirect health indicators (IHIs) and the GPR model. IHIs with high correlation with the capacity degradation curve are chosen as high-dimensional input by means of grey relation analysis, and the GPR model is developed to predict the short-term SOH of lithium-ion batteries.
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