Prediction of Lithium Battery SOC Based on Extended Kalman Filter Algorithm

Accurate parameter identification and state of charge prediction of lithium battery model are the guarantees for the healthy operation of batteries. This paper presents a state of charge prediction method of lithium battery based on extended Kalman filter algorithm. Firstly, the equivalent circuit model of the second-stage RC lithium battery is established and the parameters of the model are identified by the discharge experiment of the hybrid power pulse characteristics. The reliability of the model is verified by comparing the simulation model with the discharge condition. Then the state space modeling of lithium battery is established. The MATLAB simulation experiment is carried out by comparing the extended Kalman filter algorithm and the state of charge prediction of coulomb counting method. The results show that the average prediction error of extended Kalman filter algorithm is 0.9%, which is far lower than 4.4% of coulomb counting method. The prediction accuracy is high, which can effectively improve the utilization rate of lithium battery.