Abstract:

Lithium-ion batteries have been widely used in the field of energy storage power stations due to their high energy density, low self-discharge rate and low pollution. To solve the accurate prediction of various states of lithium-ion batteries, a second-order RC equivalent circuit model is first built, and then the parameters of the model are identified by using the forgetting factor recursive least squares (FFRLS) method. A joint SOC-SOH estimation method based on adaptive extended Kalman filtering (AEKF) algorithm is proposed, and the method is compared and verified under different battery conditions. Experimental results show that compared with the extended Kalman filter (EKF) and the unscented Kalman filter (UKF), the proposed method can improve the accuracy and computational efficiency of SOC and SOH prediction, and has certain practical value.

Key words: lithium battery, second-order RC model, parameter identification, SOH estimation, SOC estimation, AEKF algorithm