A Novel Adaptive Forgetting Factor Recursive Least Squares-Extended Kalman Filter Algorithm for Energy State Estimation of Decommissioned Power Lithium-ion Batteries

Aiming at the difficult problem of online estimation of the state of energy (SOE) for decommissioned power lithium-ion batteries, an estimation method based on an adaptive forgetting factor recursive least squares-extended Kalman filter (AFFRLS-EKF) algorithm is proposed. The AFFRLS-EKF algorithm uses fuzzy logic to add a forgetting factor based on the traditional recursive least squares algorithm so that the online parameter identification can reflect the model parameters in a more real-time manner. A second-order RC equivalent circuit model is constructed based on the dynamic characteristics of decommissioned power lithium-ion batteries, which can more accurately represent the electrochemical characteristics of the batteries. The proposed algorithms MAE, MAPE, RMSE, and ME are improved by 1.15%, 3.698%, 1.027%, and 1.64% respectively. The estimation accuracy and robustness of the algorithm are verified.