Improved State-of-Charge and Voltage estimation of a Lithium-ion battery based on Adaptive Extended Kalman Filter

Battery management system (BMS) must ensure the safety and efficiency of a battery. State-of-Charge (SOC), being regarded as the fuel gauge of a battery constitutes one of the key components of the BMS. Various types of estimators can be found in the literature which gives an estimate of the battery SOC such as Kalman filter (KF), extended Kalman filter (EKF) etc. However, if there is any perturbation in the process or measurement noise due to any possible sensor malfunction, there can be errors in the estimates of the battery parameters, SOC, voltage etc. Hence, the aforementioned estimators may not provide an accurate estimate of the various quantities due to their non-adaptive nature. In order to take into account the various changes in noise uncertainties, an adaptive extended Kalman filter (AEKF) based SOC and voltage estimation approach has been proposed in the paper, where both the process and measurement noise co-variances are updated at every time step. The proposed approach has been validated with constant current discharge data from NASA PCoE along with Urban Dynamometer Driving Schedule (UDDS) drive cycle data. Moreover, to prove the effectiveness of the proposed approach, it has been compared with the EKF algorithm.