Abstract: A parameter identification method was proposed for the dynamic battery model based on the genetic algorithm(GA) and the extended Kalman filter(EKF) algorithm. An electrical equivalent circuit model was built for the lithium-ion power battery. In the model 2 RC networks describe the electrochemical activation process and the concentration difference polarization process of the battery respectively, and the hysteresis voltage characterizes the difference of equilibrium voltages between the charging and discharging processes. The coupled parametes of the model, i.e. the RC network parameters and the hysteresis coefficient were identified by the EKF algorithm with minimal mean square error estimation effect. For the problem of the EKF which is difficnlt to get the optimal filtering effect by test adjustment, the GA was used to optimizing the EKF noise matrix. The results of simulation and experiment showed that the proposed battery model parameter identification method meets the requirement of the electric vehicle on simulation precision.

Key words: electrical engineering, lithium-ion battery, dynamic battery model, parameter identification, GA-EKF algorthm