Adaptive strong tracking unscented Kalman filter based SOC estimation for lithium-ion battery

Abstract

Power battery is the heart of electric vehicles, and the accurate state of charge (SOC) estimation is crucial for the management of the power battery. This paper proposes an adaptive strong tracking unscented Kalman filter (ASTUKF) algorithm to estimate the SOC of lithium-ion battery. This method doesn't need to compute the Jacobian matrix compared with the traditional strong tracking filter. This algorithm can correct the SOC estimation error caused by the model error and update the noise covariance in real time, which improves the accuracy and robustness of the SOC estimation for lithium-ion battery. The experiments of LiFePO4 power battery are conducted to validate the accuracy and robustness of the proposed algorithm.