Publications

Grid-scale battery energy storage systems (BESSs) are vulnerable to false data injection attacks (FDIAs), which could be used to disrupt state of charge (SoC) estimation. Inaccurate SoC estimation has negative impacts on system availability, reliability, safety, and the cost of operation. In this article a combination of a Cumulative Sum (CUSUM) algorithm and an improved input noise-aware extended Kalman filter (INAEKF) is proposed for the detection and identification of FDIAs in the voltage and current sensors of a battery stack. The series-connected stack is represented by equivalent circuit models, the SoC is modeled with a charge reservoir model and the states are estimated using the INAEKF. Further, the root mean squared error of the states’ estimation by the modified INAEKF was found to be superior to the traditional EKF. By employing the INAEKF, this article addresses the research gap that many state estimators make asymmetrical assumptions about the noise corrupting the system. Additionally, the INAEKF estimates the input allowing for the identification of FDIA, which many alternative methods are unable to achieve. The proposed algorithm was able to detect attacks in the voltage and current sensors in 99.16% of test cases, with no false positives. Utilizing the INAEKF compared to the standard EKF allowed for the identification of FDIA in the input of the system in 98.43% of test cases.

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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.

The state of charge (SoC) estimated by Battery Management Systems (BMSs) could be vulnerable to False Data Injection Attacks (FDIAs), which aim to disturb state estimation. Inaccurate SoC estimation, due to attacks or suboptimal estimators, could lead to thermal runaway, accelerated degradation of batteries, and other undesirable events. In this paper, an ambient temperature-dependent model is adopted to represent the physics of a stack of three series-connected battery cells, and an Unscented Kalman Filter (UKF) is utilized to estimate the SoC for each cell. A Cumulative Sum (CUSUM) algorithm is used to detect FDIAs targeting the voltage sensors in the battery stack. The UKF was more accurate in state and measurement estimation than the Extended Kalman Filter (EKF) for Maximum Absolute Error (MAE) and Root Mean Squared Error (RMSE). The CUSUM algorithm described in this paper was able to detect attacks as low as ±1 mV when one or more voltage sensor was attacked under various ambient temperatures and attack injection times.

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