LAUSR

While cyber-physical security of electric vehicles (EV) is gaining increased concerns due to the fast development of vehicle onboard communication networks, the existing literature focuses on the vehicle level and it does not explicitly address cyber-threat detection for the EV powertrain traction motor drives. Therefore, in this paper, we propose a fast, model-free approach to detect cyber-threats in electric vehicle traction motor drives with only four easy-to-get, trustworthy sensor signals. First, the trustworthy motor current signals are selected to undermine the impacts of the vehicle’s random driving cycles. Then a set of innovative time-domain current features that are the most sensitive to a wide range of anomalies are selected to reduce the number of observations needed, thus vastly reducing the computational burden and the time-to-detect. Next, four binary classifiers are developed to detect cyber-threats while a majority vote mechanism is adopted to reduce the false alarm rate. Finally, the proposed method is validated by the real-time hardware-in-the-loop simulations. Validation results show that the proposed detection method achieves much faster detection compared to traditional current signature analysis. Furthermore, the proposed detection methods achieve an accuracy higher than 98% with the false alarm rate less than 0.01%.