Power battery state of charge estimation based on extended Kalman filter

State of Charge (SOC) is one of the main parameters to characterize the power battery state. The accurate SOC estimation has a great impact on predicting the mileage of vehicles, prolonging the battery life, and improving the efficiency of electric vehicles. At present, the common SOC estimation methods have some shortcomings in accuracy, real time, and operability. In this paper, a new SOC estimation method based on Extended Kalman Filter (EKF), combined with the current integration method and the open circuit voltage method, is proposed. Taking lead-acid power batteries as the research object, the Thevenin equivalent circuit model of battery is established. The model parameters are identified by Hybrid Pulse Power Characterization (HPPC), then, the new algorithm based on EKF is used to estimate the state of the power battery, and the experimental verification is carried out. The experimental results show that the algorithm has high accuracy and good robustness, and the estimated error of SOC of the power battery is less than 4%, which can meet the practical requirements of engineering application. Published under license by AIP Publishing.