Electric Vehicle User Data-Induced Cyber Attack on Electric Vehicle Charging Station

Abstract

Electric vehicle (EV) user data (e.g., arrival/departure times and initial/desired state of energy (SOE) of the EV at EV charging stations (EVCSs)) are crucial data based on which the energy management system (EMS) of EVCS calculates the economic charging schedules of EVs according to their preferred charging conditions. In this paper, we present a novel cyber attack via the manipulation of EV user data against the EMS of an EVCS that may result in incorrect electricity costs incurred by the EVCS through distorted charging schedules of EVs. The proposed attack method is formulated as a mixed-integer linear-programming-based bi-level optimization problem that comprises upper- and lower-level optimization problems. At the upper level, malicious EV user data injected into the communication network between the EVs and the EMS of the EVCS are calculated, while a normal operation of the EV charging optimization algorithm in the EMS is ensured at the lower level even if malicious data are delivered from the upper level. The formulated bi-level optimization problem is converted into a single-level optimization problem by replacing the lower-level problem with its corresponding Karush-Kuhn-Tucker conditions. The feasibility of the proposed cyber attack against EVCSs is demonstrated via a simulated scenario in which 40 EVs arrive at an EVCS, which has six charging poles with different charging speeds. The economic impact of such an attack is quantified in terms of the total electricity cost incurred by the EVCS, charging schedule, initial/desired SOE of EVs, and attack effort.