In this paper, a state of charge (SOC)estimation method for lithium battery based on extended Kalman filter is proposed, and the estimation accuracy of SOC for lithium battery at different temperatures is analyzed. Firstly, a Thevenin equivalent circuit model is adapted to describe the battery considering model complexity, model accuracy and robustness of the model. Secondly, battery capacity and dynamic working condition experiment are carried out based on the battery test bench. Then, battery model parameters are identified by Forgetting Factor Recursive Least Square Algorithms (FFLS) based on China City Bus Cycle (CCBC) experiment data at different temperatures. Last but not least, a state of charge estimation method based on Extended Kalman Filter is adapted and the estimation accuracy is analyzed base on Urban Dynamometer Driving Schedule (UDDS). The results show that the estimation error is less than 4% in different temperatures based on the proposed method.
Keywords Lithium-ion battery, Extended Kalman Filter, Forgetting Factor Recursive Least Square Algorithms, State of Charge estimation