Vulnerability analysis of an electric vehicle fleet for car-sharing service under cyber attacks

The booming development of the car-sharing industry and the continuous increase in the market share of electric vehicles demand effective dispatching strategies that consider collaborative optimization of route selection and charging management. Furthermore, in recent years, network attacks have posed significant threats to traffic management centers. Therefore, this paper presents a pioneering work aimed at investigating the vulnerability of electric vehicle sharing services in traffic management systems under network attacks. Firstly, we develop a bilevel optimization model to capture the game between network attackers and electric vehicle fleet operators. The upper-level problem represents the behavior of network attackers, who aim to maximize the total cost by manipulating traffic control signals and sensor data. The lower-level problem determines the system operator's decisions on optimizing route selection and charging scheduling to minimize costs. Secondly, a nested column generation algorithm is employed to solve the bilevel optimization model. Finally, several sensitivity studies are conducted using real-world traffic networks to examine the effectiveness of the proposed model and the influence of various parameters on system performance.