A Resilient Distributed Charging Scheduling Strategy for Electric Vehicles Under Cyber-Attacks

With the large-scale integration of electric vehicles (EVs), distributed charging scheduling has become a key enabler for coordinated charging management. However, its reliance on information exchange makes it susceptible to cyberattacks, including False Data Injection (FDI), Denial of Service (DoS), and replay attacks. Such attacks may compromise privacy and corrupt or interrupt communication, leading to incorrect consensus prices and degraded scheduling performance. To mitigate these threats, this paper proposes a distributed resilient charging scheduling strategy. Specifically, anomalous nodes are detected through neighbor-based observations, while a belief-degree-based trust mechanism is employed to isolate low-trust nodes and suppress attack propagation. In addition, an individual price resetting mechanism is developed to restore convergence to the optimal price of the remaining EVs following node isolation. Simulations on communication networks with 5 to 100 EVs show that, under all three attacks, the compromised node is detected at the second and isolated at the third consensus iteration after attack onset, no healthy node is falsely isolated, and the remaining fleet converges to the optimum of the reduced scheduling problem with a price deviation below 8.2×10−3. A buffered detection envelope extends these guarantees to asynchronous communication, heterogeneous time-varying delays, packet losses, and intermittent attacks: in 245 randomized stress runs on 5-to-100-EV networks, every attacker is isolated, no healthy node is isolated outside the harshest composite scenario, and the final price deviation remains below 1.1×10−2. Extensive simulations under representative cyberattack scenarios verify the effectiveness and robustness of the proposed strategy within the stated assumptions.