Survey on Electric Vehicle Charging Systems: Derivation of Security Requirements and Research Directions

Abstract

The proliferation of electric vehicles (EVs) is shifting the paradigms of transportation and energy, and electric vehicle charging infrastructure (EVCS) is emerging as a key element for achieving sustainable mobility. From a systems perspective, EVCS consists of heterogeneous components, including power electronics, communication controllers, and cloud-based management platforms, which are interconnected via standard protocols such as ISO 15118 and OCPP, yet still face technical challenges such as grid instability, lack of interoperability, and high deployment costs. From a security perspective, EVCS functions as a cyber-physical interface between vehicles and the grid and thus presents a broad attack surface, with threats such as charge manipulation attacks (CMA), distributed denial-of-service (DDoS), false data injection (FDI), and man-in-the-middle (MitM) attacks being actively reported. To address these challenges, various AI-based security techniques—including machine learning-based anomaly detection, transfer learning, federated learning, and temporal convolutional network (TCN)-based intrusion detection systems—have been explored; however, their practical deployment remains limited by insufficient datasets, computational resource constraints, and hardware compatibility issues. This paper comprehensively examines EVCS from both systems and security perspectives by analyzing recent charging technology trends, grid integration strategies, standardization issues, and major security vulnerabilities and countermeasures. Building on this analysis, this paper derives security requirements for EVCS and proposes future research directions for realizing more efficient, reliable, and secure charging infrastructures.