Detection of False Data Injection Attacks in Battery Stacks Using Physics-Based Modeling and Cumulative Sum Algorithm

Abstract

Variables estimated by Battery Management Systems (BMSs) such as the State of Charge (SoC) may be vulnerable to False Data Injection Attacks (FDIAs). Bad actors could use FDIAs to manipulate sensor readings, which could degrade Battery Energy Storage Systems (BESSs) or result in poor system performance. In this paper we propose a method for accurate SoC estimation for series-connected stacks of batteries and detection of FDIA in cell and stack voltage sensors using physics-based models, an Extended Kalman Filter (EKF), and a Cumulative Sum (CUSUM) algorithm. Utilizing additional sensors in the battery stack allowed the system to remain observable in the event of a single sensor failure, allowing the system to continue to accurately estimate states when one sensor at a time was offline. A priori residual data for each voltage sensor was used in the CUSUM algorithm to find the minimum detectable attack (500 µV) with no false positives.