Multi-time-scale framework for prognostic health condition of lithium battery using modified Gaussian process regression and nonlinear regression

Prognostic and health management of lithium batteries is a multi-faceted approach that can provide crucial indexes for guaranteeing the reliability and safety of the energy storage system. Herein, a novel multi-time-scale framework is proposed that focuses on short-term battery state of health estimation and long-term remaining useful lifetime prediction. The proposed method extracts four significant features through in-depth analysis of partial incremental capacity and Gaussian process regression with nonlinear regression is applied to forecasting battery health conditions. First, the advanced signal filter methods are employed to smooth initial incremental capacity curves. After that, the significant feature variables are extracted from different degrees such as intercept, slope and peak by linear fitting the partial incremental capacity curves. Second, the significant feature variables feed to Gaussian process regression to establish a short-term battery degradation model using kernel-modified Gaussian process regression. Third, an autoregressive long-term battery prediction model is established by combining the offline short-term battery model with nonlinear regression. The predictive capability, robustness and effectiveness of proposed methods are verified using four datasets with different cycling test conditions and health levels. The results show that the proposed method can give accurate battery health conditions forecasting.