State of charge estimation based on improved Li-ion battery model using extended Kalman filter

This paper applies the extended kalman filter (EKF) to estimate battery state of charge (SOC) based on improved Li-ion battery model. The dynamics of the battery is modeled by two series RC networks to describe the polarization effects of Li-ion battery. The current is taken into account to adjust the polarization parameters to improve the battery model accuracy. The test bench is developed by integrating dSPACE, programmable power supply and electronic load to realize the online SOC estimation. The comparison between the results from the battery model and those from the experiment indicates the higher SOC estimation accuracy can be achieved by the integration of the EKF and the improved battery model.