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Nadya Novarizka Mawuntu,이덕진 제어로봇시스템학회 2021 제어로봇시스템학회 각 지부별 자료집 Vol.2021 No.12
With electric vehicles (EVs) being widely accepted as clean technology to solve carbon emissions in modern transportation, the lithium-ion battery (LIB) has become the prior choice for energy storage. Along with this rapid growth comes the need for a Battery Management System (BMS) to ensure the safe operation and reliability of the battery. The battery state of charge (SOC) is a crucial function in BMS. However, SOC cannot be directly measured. However, battery cell SOC cannot be measured directly and can only be reflected based on the measured parameters, such as voltage, current, temperature, and internal resistance. A variety of methods have been developed for both SOC estimation. Among the existing SOC estimation methods, Kalman filter-based estimation methods are widely used due to their high estimation accuracy. First, the battery model was established with a second-order Thevenin equivalent circuit model (ECM). Then, this study proposed Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) to estimate the SOC of the LiB. The result proves that EKF and UKF are both applicable, with the accuracy of the UKF algorithm being higher than that of EKF algorithm.