Battery health prognostics based on improved incremental capacity using a hybrid grey modelling and Gaussian process regression

Abstract

Battery health prognostics management is an important prerequisite for ensuring its safe use. As the battery is charging and discharging, its capacity deteriorates. Incremental capacity (IC) analysis is a common tool to analyse the degradation process based on the change rate of capacity relative to voltage. However, measuring battery voltage and current is difficult and there are inherent errors. This paper improves the IC curve from the perspective of time series, extracts a health indicator (HI), and uses the hybrid prediction model to predict the remaining useful life (RUL). Firstly, we convert the traditional IC-voltage (IC-V) curve to the IC-time (IC-T) curve. The time series corresponding to the peak of the improved curve is extracted. Secondly, the extracted degradation HI is predicted using grey forecasting model, and a probabilistic and iterative hybrid battery prediction method is established combined with Gaussian process regression. Finally, public datasets are used to validate the proposed method at different starting points and comparisons with other prediction methods are carried out. Results show that the HI based on the improved IC curve is highly correlated with the degradation process and the proposed method can provide an accurate result, which verifies its effectiveness and rationality.