Abstract
State of charge (SOC) level can be considered as one of the imperative parts of the Battery Energy Management System (BEMS) for a microgrid power system to ensure that the battery is operating safely and reliably to give an optimum service for the system. There are several types of battery models are available and several methods are used to estimate the SOC level of a battery because there are certain types of drawbacks that can be identified in each battery model and estimation types. This study is focused on the Extended Kalman filter (EKF) method and machine learning time series forecasting method for the battery SOC level estimation and they are discussed in detail with the testing results by an electrothermal battery model. Finally, these results are compared and evaluated with the estimated results of the SOC level using the coulomb counting method. According to the final results, the mean absolute percentage errors of EKF and machine learning based SOC level estimation methods are 6.28% and 3.64% respectively.
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