State-of-charge estimation of lithium-ion batteries in electric vehicles based on an adaptive extended Kalman filter

An adaptive extended Kalman filter (AEKF) algorithm was adopted to estimate the state-of-charge (SOC) of lithium-ion batteries in electric vehicles. Based on the hybrid pulse power characterization (HPPC) test, an improved Thevenin battery model was achieved by using the genetic algorithm to optimize the parameter identification method and identify the model parameters from the charge direction and the discharge direction respectively. In addition, the improved model was verified under the dynamic stress test cycle. Further, an AEKF algorithm was adopted to design the approach for estimation of SOC of lithium-ion batteries. Finally, the proposed method was verified by the simulation experiment and the bench test under the federal urban driving schedules. It is shown that the improved Thevenin model and the proposed SOC estimation method all have the high accuracy and their maximum errors are 1.70% and 2.53% respectively, and the AEKF algorithm is of robustness and it can efficiently solve the problems of cumulate error and inaccurate initial SOC estimation.