SOC estimation of lithium battery based online parameter identification and AEKF

The accurate estimation of SOC is very important for improving the dynamic performance and energy utilization efficiency of batteries. In the estimation process, the inaccuracy of model parameters and the uncertainty of system noise will greatly affect the results. In order to reduce the influence of model parameter identifcation and system noise on the SOC estimation accuracy, this paper adopts the second-order RC equivalent circuit model combined with the adaptive extended kalman filter algorithm (AEKF) to estimate the SOC of lithium batteries. In order to reduce the estimation error caused by parameter identifcation, the least square method with forgetting factoris used to identify the model parameters online. AEKF can correct the system and process noise, so as to reduce the impact of noise on SOC estimation. At last, EKF and AEKF are used for SOC estimation respectively and their errors are compared. The results show that joint AEKF and least square parameter online identifcation has higher accuracy and better adaptability.