Increasing Battery Management System Resilience Following Identification of Sensor Anomalies Using Unknown Input Observer

Battery energy storage systems (BESSs) are crucial for modernizing the power grid but are monitored by sensors that are susceptible to anomalies like failures, faults, or cyberattacks that could affect BESS functionality. Much work has been done to detect sensor anomalies, but a research gap persists in responding to anomalies. An approach is proposed to mitigate the damage caused by additive bias anomalies by employing one-of-three estimators based on the anomalies present. A tuned cumulative sum (CUSUM) algorithm is used to identify anomalies, and a set of rules are proposed to select an estimator that will isolate the effect of the anomaly. The proposed approach is evaluated using two simulated studies, one in which an anomaly impacts the input and one where an anomaly impacts an output sensor.