Analysis of State of Health Estimation for Lithium-Ion Cell Using Unscented and Extended Kalman Filter

There is tremendous potential for the growth of electric vehicles (EVs) in near future. Hence, investigations on different aspects of propulsion system, battery management and charging infrastructure are focused upon by academic and industrial researchers. Battery management systems (BMSs) are responsible to estimate battery parameters and protection. Precise estimation of the State of Charge (SoC) and State of Health (SoH) is crucial functionality of the BMS. Such estimation can inform the driver about the remaining range, replacement of battery pack in their vehicle and the charging requirements. SoC and SoH estimation necessitate a reliable model for battery. This paper provides a comparative study of the SoH and SoC assessment of lithium-ion cells with the help of Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF). Different factors such as temperature and variations in internal resistance are considered in the simulation studies. Important methods for estimation of SoH and SoC are reviewed. An equivalent circuit model with 3-RC branch is used in analyses, to simulate lithium-ion cell. The rise in internal resistance of lithium-ion cell as a consequence of charging and discharging is analyzed, and the resulting degradation is studied using UKF and EKF.