SOC Estimation for Lithium-Ion Battery Cell Using Extended Kalman Filter

Abstract

The State of Charge (SOC) estimation is an essential part of a Battery Management System. Nevertheless, because the discharge and charging of battery cells requires complicated chemical operations, therefore, it is hard to determine the state of charge of the battery cell. In this paper, a lithium iron phosphate battery cell with 8 Ah capacity and 3.2 Volt rated voltage was studied. The battery is modelled to reflect the dynamic of the battery encompassing mainly four elements; an Open Circuit Voltage (OCV) source, two RC network, and one resistor. The model parameters are identified by using Forgetting Factor Recursive Least Mean Squares and time domain extraction method. Parameters are converged to their real values and these values are used to estimate the state of charge of the cell using Extended Kalman Filter algorithm based on battery model dynamic. The results in our study show that SoC which is estimated by the implemented Extended Kalman Filter converges to battery’s real SoC value. It is also shown that the Extended Kalman Filter update and prediction stages iterations move forward to minimize the error between real SoC and estimated SoC.