Systematic Assessment of Cyber Physical Security of Energy Management System for Connected and Automated Electric Vehicles

Abstract

The rapid advancement of Connected and Automated Electric Vehicles (CAEVs) has increased reliance on Energy Management Systems (EMS), making them vulnerable to cyber-physical security threats that can compromise vehicle performance, safety, and energy efficiency. This paper presents a systematic assessment framework for evaluating the cyber-physical security of EMS in CAEVs. The proposed approach integrates multiple vehicle sensors and communication components to identify potential vulnerabilities within the control architecture. Various cyber-physical attack scenarios, including false data injection, sensor spoofing, and communication manipulation attacks, are modeled to analyze their impact on system operation. An EMS based on Model Predictive Control (MPC) is implemented to optimize energy utilization while maintaining vehicle performance under normal and attack conditions. Extensive simulations are conducted to collect operational data related to energy consumption, control actions, and vehicle dynamics. The collected data are analyzed using security-oriented performance metrics that evaluate system resilience, stability, comfort, and energy efficiency. The results provide a comprehensive understanding of how cyber-attacks influence EMS behavior and overall vehicle operation. The proposed assessment methodology enables the identification of critical vulnerabilities and supports the development of effective detection, mitigation, and recovery mechanisms, thereby enhancing the security, reliability, and sustainability of future connected and automated electric transportation systems.