Robust lithium-ion state-of-charge and battery parameters joint estimation based on an enhanced adaptive unscented Kalman filter

Accurate modeling and state of charge (SOC) estimation of lithium-ion battery against the model uncertainty and data uncertainty are difficult tasks nowadays. In this paper, a model and data uncertainties-robust method is proposed simultaneous estimation of the model parameters and the SOC using an enhanced adaptive unscented Kalman filter (AUKF). An extended state observer is established to integrate all unknown variables including parameters and SOC into a vector. An covariance matching technique with adaptive forgetting factor is proposed to obtain uncertain model and data statistics, in combination with a singular value decomposition based unscented transform to guarantee the positive definiteness of the error covariance matrix. Furthermore, establishing new protocols to handle missing input and missing output separately, the battery SOC and parameters can be estimated from missing measurements. Benefits from above procedures, the proposed method is more robust to model uncertainties and the data uncertainties compared to the conventional SOC estimation method. The robustness of the proposed method is verified at different operation temperatures and dynamic load profiles. The results shows that the proposed method possesses high accuracy and excellent robustness.