Biblio

Battery energy storage systems are facing risks of unreliable battery sensor data which might be caused by sensor faults in an embedded battery management system, communication failures, and even cyber-attacks. It is crucial to evaluate the trustworthiness of battery sensor data since inaccurate sensor data could lead to not only serious damages to battery energy storage systems, but also threaten the overall reliability of their applications (e.g., electric vehicles or power grids). This paper introduces a battery sensor data trust framework enabling detecting unreliable data using a deep learning algorithm. The proposed sensor data trust mechanism could potentially improve safety and reliability of the battery energy storage systems. The proposed deep learning-based battery sensor fault detection algorithm is validated by simulation studies using a convolutional neural network.

The increased power capacity and networking requirements in Extremely Fast Charging (XFC) systems for battery electric vehicles (BEVs) and the resulting increase in the adversarial attack surface call for security measures to be taken in the involved cyber-physical system (CPS). Within this system, the security of the BEV's battery management system (BMS) is of critical importance as the BMS is the first line of defense between the vehicle and the charge station. This study proposes an optimal control and moving-target defense (MTD) based novel approach for the security of the vehicle BMS) focusing on the charging process, during which a compromised vehicle may contaminate the XFC station and the whole grid. This paper is part of our ongoing research, which is one of the few, if not the first, reported studies in the literature on security-hardened BMS, aiming to increase the security and performance of operations between the charging station, the BMS and the battery system of electric vehicles. The developed MTD based switching strategy makes use of redundancies in the controller and feedback design. The performed simulations demonstrate an increased unpredictability and acceptable charging performance under adversarial attacks.

Recent changes to greenhouse gas emission policies are catalyzing the electric vehicle (EV) market making it readily accessible to consumers. While there are challenges that arise with dense deployment of EVs, one of the major future concerns is cyber security threat. In this paper, cyber security threats in the form of tampering with EV battery's State of Charge (SOC) was explored. A Back Propagation (BP) Neural Network (NN) was trained and tested based on experimental data to estimate SOC of battery under normal operation and cyber-attack scenarios. NeuralWare software was used to run scenarios. Different statistic metrics of the predicted values were compared against the actual values of the specific battery tested to measure the stability and accuracy of the proposed BP network under different operating conditions. The results showed that BP NN was able to capture and detect the false entries due to a cyber-attack on its network.