State of charge estimation with the adaptive unscented Kalman filter based on an accurate equivalent circuit model

Abstract The accurate estimation of the state of charge (SOC) plays an important role in optimizing the energy management of electric vehicles. To improve the estimation accuracy of SOC, this study proposes a SOC estimation method with the adaptive unscented Kalman filter (AUKF) based on an accurate equivalent circuit model. First, combined with the n-RC equivalent circuit model, the relationship curve between SOC and open-circuit voltage (OCV) is fitted via parameter identification. Next, the accuracy advantage of the n-RC model is proven by comparing and analyzing the voltage response curves of different n-RC models and several common equivalent circuit models. Moreover, the accuracy of the n-RC model becomes higher with the increasing n. Then the numerical validation experiments are established based on the AUKF algorithm, and the constant current discharge experiment, hybrid pulse experiment, robustness verification experiment are carried out. Finally, to effectively evaluate this estimation approach, in addition to setting up a control group experiment based on the extended Kalman filter (EKF) algorithm and unscented Kalman filter (UKF) algorithm. The experiment results show that, compared with the other two algorithms, the SOC estimation method based on AUKF is more accurate.