A Study of EV BMS Cyber Security Based on Neural Network SOC Prediction

TL;DR

This paper investigates cyber security threats to EV batteries by using neural network-based SOC prediction to detect tampering and cyber-attacks, demonstrating the effectiveness of BP neural networks in identifying false data.

Contribution

It introduces a neural network approach for detecting cyber-attacks on EV battery SOC data, providing a novel application of machine learning for EV cybersecurity.

Findings

BP neural network accurately detects false SOC entries

Neural network maintains stability under attack scenarios

Experimental results validate the approach's effectiveness

Abstract

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.