State of Charge (SoC) algorithm for Battery Management System (BMS)

It is critically important for a Battery Management System (BMS) to estimate the State of Charge (SoC) of the batteries [1]. A battery with this feature is protected. By protecting the battery from serious problems and early degradation, it prolongs its life and prevents its degradation over time. In this paper, the Unscented Kalman Filter (UKF) and Coulomb Counting (CC) methods are implemented to calculate the state of charge. There have been many applications across a variety of fields that have utilized KF as state observer mechanisms. With the help of statistics, a state observer will eventually estimate immeasurable states with high accuracy after observing some real-time output values (measurable states). In the paper, MATLAB model of the SoC for BMS is illustrated. Based on the experimental results, a comparison of KF and CC methods can be made. The results indicate that KF method gives much more accurate results than CC method, as well as the ability to reduce errors very quickly.