Guo Limeng

Data-driven framework for lithium-ion battery remaining useful life estimation based on improved nonlinear degradation factor

This paper proposes an improved nonlinear degradation factor based on the current percentage of life-cycle length (CPLL) which contains the battery capacity degradation characteristics information of different periods. This method is improved based on related nonlinear degradation Autoregressive (AR) data-driven prognostics model considering an improved scale nonlinear degradation factor. Then a combination is implemented between the proposed factor and data-driven AR model named nonlinear scale degradation parameter based AR (NSDP-AR) model for better nonlinear prediction ability. Extended Kalman Filter (EKF) is used to obtain the specific factor for certain kind of battery. In order to promote the modified model, a remaining useful life (RUL) prognostic framework using Grey Correlation Analysis (GCA) will be established. The experimental results with the battery data sets from NASA PCoE and CALCE show that the proposed NSDP-AR model and the corresponding prognostic framework can achieve satisfied RUL prediction performance.