In the next ten years, over 200 GWh lithium-ion batteries will reach the end of their first life. The reuse of these batteries for stationary energy storage offers economic and environmental benefits. However, the heterogeneity and complicated aging behaviors of retired batteries demand a specialized second-life battery management system (BMS-2) that can accurately monitor their status. An online adaptive estimator is needed to address this challenge. In this study, we investigate and improve classic state-and-parameter co-estimation methods and propose a novel approach integrating Newton’s method and Extended Kalman Filter (EKF) for co-estimation. Results demonstrate that the proposed method outperforms the parameter-augmented EKF and dual EKF methods, especially in addressing uncertainties and aging effects in the batteries.
