State of Charge (SOC) Estimation of Li-Ion Battery

To carry out batteries optimum usage and preservation, battery management system (BMS) is employed. This BMS saves batteries from over-discharge, over-charge and maintain balance in cell. SOC estimation is of good sense for the secured operation of the battery. A correct SOC estimation is an important task. Increasing certainty of estimating SOC and reducing the complexness of model is very important for the state estimation. According to the most popular evaluation methods, Proportional-Integral (PI) Observer, Kalman Filter (KF) methods are projected for battery SOC estimation in EV. It is implemented in Simulink, and Python software. The performance comparison is done on the basis of graphs. It was aimed to catch the battery characterization and provide parameters to the system so as to estimate SOC precisely.