An improved adaptive unscented kalman filtering for state of charge online estimation of lithium-ion battery

Precise state of charge (SOC) estimation is crucial to assure safe and reliable operation of lithium-ion battery in electric vehicles. Adaptive unscented Kalman filter (AUKF) has been intensively applied to estimate SOC due to its features of self-correction and high accuracy. Nevertheless, the estimation by traditional AUKF cannot proceed when error covariance matrix is non-positive definite, greatly influencing the stability of SOC estimation. To address this issue, an improved AUKF is proposed in this paper. Firstly, the forgetting factor recursive least square is employed to online identify parameters of electrical equivalent circuit model. With these identified parameters, an improved AUKF, whose Cholesky decomposition for error covariance matrix of tradition AUKF is replaced by singular value decomposition, is applied here to provide online accurate SOC estimation. The feasibility of the proposed method is verified by experimental data under Federal Urban Driving Schedule test. The validation results of robustness present that the algorithm has satisfactory robustness against inaccurate initial SOC. Moreover, through the comparison with traditional AUKF, it can be easily concluded that the proposed method can achieve precise and stable SOC estimation even though error covariance matrix is non-positive definite.