The remaining useful life (RUL) of LIBs is important in the prognostics and health management of battery systems. However, an accurate RUL prediction is difficult to achieve. Using experimental historical data, this study builds a battery degradation model for estimating battery working state and maintaining and replacing equipment in a timely manner to ensure a stable operation. A method for predicting the RUL of LIBs that employs artificial bee colony (ABC) and support vector regression (SVR) is proposed to improve prediction accuracy. SVR can deal with problems such as small samples, nonlinearity, and time-series analysis. However, SVR is problematic when applied to kernel parameter selection. The ABC algorithm is accordingly employed to optimize the SVR kernel parameters. A simulation with experimental data is conducted by utilizing the NASA Ames Prognostics Center of Excellence battery datasets to validate the proposed method. Results show that parameter optimization with the ABC algorithm is better than that with the PSO algorithm. Furthermore, the ABC-SVR method is more accurate than PSO-SVR and other existing methods are. Therefore, the proposed method achieves high prediction accuracy and prediction stability when used to predict the RUL of LIBs.

Better performance consistency of regrouped batteries retired from electric vehicles can guarantee the residual value maximized, which greatly improves the second-use application economy of retired batteries. This paper develops a fast identification approach for micro-health parameters characterizing negative electrode material and electrolyte in LiFePO₄ batteries on the basis of a simplified pseudo two-dimensional model by using Padé approximation is developed. First, as the basis for accurately identifying micro-health parameters, the liquid-phase and solid-phase diffusion processes of pseudo two-dimensional model are simplified based on Padé approximation, especially according to enhanced boundary conditions of liquid-phase diffusion. Second, the reduced pseudo two-dimensional model with the lumped parameter is proposed, the target parameters characterizing negative electrode material (ε_n, D_s,n) and electrolyte (D_e, C_e) are grouped with other unknown but fixed parameters, which ensures that no matter whether the target parameters can be achieved, the corresponding varying traces is able to be effectively and independently monitored by lumped parameters. Third, the fast identification method for target micro-health parameters is developed based on the sensitivity of target parameters to constant-current charging voltage, which shortens the parameter identification time in comparison to that obtained by other approaches. Finally, the identification accuracy of the lumped micro-health parameters is verified under 1 C constant-current charging condition.