False Data Injection Attack Detection Methods for Battery Stacks with Input Noise

Battery systems are typically equipped with state of charge (SoC) estimation algorithms. Sensor measurements used to estimate SoC are susceptible to false data injection attacks (FDIAs) that aim to disturb state estimation and, consequently, damage the system. In this paper, SoC estimation methods are re-purposed to detect FDIAs targeting the current and voltage sensors of a battery stack using a combination of an improved input noise aware unscented Kalman filter (INAUKF) and a cumulative sum detector. The root mean squared error of the states estimated by the INAUKF was at least 85% lower than the traditional unscented Kalman filter for all noise levels tested. The proposed method was able to detect FDIA in the current and voltage sensors of a series-connected battery stack in 99.55% of the simulations.