State-of-Charge Estimation of NMC-based Li-ion Battery Based on Continuous Transfer Function Model and Extended Kalman Filter

Lithium-ion (Li) battery based on nickel-manganese-cobalt (NMC) cathode has emerged as one of the most successful battery types for powertrain of Electric Vehicles (EVs). The effective management of the NMC-based battery relies on accurate estimation of its State-of-Charge (SoC) in the Battery Management System (BMS). In this paper, an effective system identification approach is applied to establish the battery model using a Continuous Transfer Function (CTF) model. The Akaike information criterion (AIC) is applied to obtain the suitable model structure considering the accuracy and real-time efficiency of the model. Then, the SoC Estimation is fulfilled based on the developed model and the Extended Kalman Filter (EKF) algorithm. The correct performance of the proposed method is evaluated and confirmed using experimental data of 3.4 Ah 3.7 V NMC-based battery cells. Likewise, the feasibility of embedded implementation is proven through some Hardware-in-the-Loop (HiL) tests.