Real-time Abnormal Data Filtering Framework for Battery Energy Storage System Real-world Application

Battery Energy Storage Systems (BESS) are key components in microgrids for reliable operations. To ensure the safety and reliability of the BESS, accurate State-of-Charge (SOC) estimation is crucial. Most battery SOC estimation algorithms are developed assuming the measured load current and terminal voltage data are trust-worthy. However, in the real-world BESS applications, the measured battery data contains abnormal data which result in poor SOC estimation accuracy and will be harmful to the safety of the BESS and the stability of the microgrid. In this paper, a practical abnormal data filtering framework for real-world BESS applications is proposed. This framework real-time detects and filters abnormal data in real-world applications. Three typical abnormal data filters are investigated and demonstrated in this paper to illustrate the effectiveness of using data filtering for accurate SOC estimation in real-world BESS applications.