Extended Kalman-Bucy Filter with Fuzzy Hybrid Model for State of Charge Estimation of Lithium-Ion Batteries

Abstract

In recent decades, the number of electric vehicles (EVs) on the road has skyrocketed. Electric vehicles have electric motors instead of internal combustion engines, and a battery powers the electric motor. Battery charging and discharging determine the maximum number of issues in EVS. State of charge (SOC) is a battery parameter that controls the charging and discharging of the battery. The precise estimation of SOC is more complicated. This article focused on accurately estimating SOC using a hybrid model with an Extended Kalman-Bucy filter and fuzzy logic controller (EKBF- FC). The proposed topology was implemented in the 2RC equivalent circuit model of the lithiumion battery Panasonic NCR18650PF. Using MATLAB Simscape tools, the proposed topology was modelled, and its operational characteristics were analysed. The proposed topology's SOC estimation has a mean absolute error (MAE) of 0.49% and a root mean square error (RMSR) of 0.72%, significantly less than the current SOC estimation methods. A comparative analysis reveals that the proposed method is the most appropriate for real-time SOC estimation for EV applications.