A novel model-based state of charge estimation for lithium-ion battery using adaptive robust iterative cubature Kalman filter

Accurate and robust state of charge (SOC) estimation is the key evaluation index for battery management system (BMS) in electric vehicles (EVs). To improve the SOC estimation precision and reliability, a novel model-based estimation approach has been proposed. Firstly, the dynamic property of lithium-ion battery (LIB) is approximated by the auto-regressive and moving average (ARMA) model which compensates the measurement errors of terminal voltage and discharge current. Secondly, a variant of the Kalman filter (KF), namely improved cubature Kalman filter (CKF) based on the combination of singular value decomposition (SVD) and Gauss Newton iterative technology is employed to develop a reliable estimator for SOC. Furthermore, an adaptive robust strategy is used to improve anti-interference performance by accounting for bidirectional adjustment of observation covariance and gain matrix. Finally, the Dynamic Stress Test (DST) and Federal Urban Driving Schedule (FUDS) are loaded on LIB to test the validity of the improved approach. The experiment results demonstrate the effectiveness of the combination of ARMA model and filtering method in terms of SOC estimation. Besides, simulated measurement noise is added to the test data to prove the robustness of the proposed method.