ESTIMATION METHOD ON THE BATTERY STATE OF CHARGE FOR HYBRID ELECTRIC VEHICLE

A combined algorithm for battery state of charge (SOC) estimation is proposed to solve the critical issue of hybrid electric vehicle (HEV). To obtain a more accurate SOC, both coulomb-accumulation and battery resistance-capacitor (RC) model are weighted combined to compensate the deficiencies of individual methods. In order to solve the key issue of coulomb-accumulation, the battery thermal model is used. Based on the principle of energy conservation, the heat generated from battery charge and discharge process is converted into the equivalent electricity to calculate charge and discharge efficiency under variable current. The extended Kalman filter (EKF) as a closed loop algorithm is applied to estimate the parameters of resistance-capacitor model. The input variables do not increase much computing difficulty. The proposed combined algorithm is implemented by adjusting the weighting factor of coulomb-accumulation and resistance-capacitor model. In the end, four different methods including Ah-efficiency, Ah-Equip, RC-SOC and Combined-SOC are compared in federal testing procedure (FTP) drive cycle. The experiment results show that the proposed method has good robustness and high accuracy which is suitable for HEV application.