A Study of EV BMS Cyber Security Based on Neural Network SOC Prediction
This work tackles cyber security for electric vehicle battery management systems, but it is incremental as it applies an existing neural network method to a new domain.
The study addressed cyber security threats in electric vehicles by detecting tampering with battery state of charge using a Back Propagation Neural Network, which successfully captured and detected false entries from cyber-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.