Improved forgetting factor recursive least square and adaptive square root unscented Kalman filtering methods for online model parameter identification and joint estimation of state of charge and state of energy of lithium-ion batteries

The estimation of the state of charge (SOC) and state of energy (SOE) of lithium-ion batteries is very important for the battery management system (BMS) and the analysis of the causes of equipment failures. Aiming at many problems such as the changes in the parameters of the lithium battery model and the accurate estimation of the SOC and SOE, this paper proposes a joint algorithm of forgetting factor recursive least square (FFRLS) and adaptive square root unscented Kalman filter (ASRUKF) based on the second-order RC equivalent circuit model. In this paper, the joint FFRLS-ASRUKF algorithm is used to perform simulation experiments under three different working conditions of HPPC, DST, and BBDST at different temperatures of 25, 15, and 5 & DEG;C. And a current & PLUSMN; 1 A offset is added as a disturbance to verify the robustness of ASRUKF. The results show that under HPPC working condition, the RMSE, MAE, and MAPE estimated by ASRUKF for SOC and SOE of lithium-ion batteries at three temperatures do not exceed 0.0016, 0.0012, and 0.43%, respectively. Under DST working condition, ASRUKF estimates that RMSE, MAE, and MAPE of SOC and SOE of lithium-ion batteries at three different temperatures do not exceed 0.0013, 0.0009, and 0.70% respectively. Under BBDST operating conditions, ASRUKF estimates that the RMSE, MAE, and MAPE of the SOC and SOE of lithium-ion batteries at three different temperatures do not exceed 0.0016, 0.0009, and 0.71% respectively. After adding the current offset, ASRUKF can still accurately estimate the SOC and SOE of lithium-ion batteries.