State and Parameter Estimation of Lithium-Ion Battery using Dual Extended Kalman Filter

Electric vehicles (EVs) are becoming more popular around the world on a daily basis. Therefore, managing power flow in EVs requires efficient and cutting-edge battery management technologies. Accurate SOH and SOC measurement is essential for a successful BMS. The driver needs a precise estimate of the battery’s State of Charge of the electric vehicle (EV) at the end of a journey . Range anxiety can be lessened and the trip can be planned with the use of this knowledge. Since the battery’s SOC and SOH cannot be monitored directly, an accurate estimation of these quantities is required to provide a precise driving range. estimated accurately to provide an exact driving range. The goal of this work is to develop a Dual Extended Kalman-based approach for Lithium-ion battery State of Charge and State of Health determination that is accurate.