Electric vehicles play a critical part in the transition to environmentally friendly transportation while minimizing greenhouse gas emissions. However increasing the integration of Electric Vehicles (EVs) into modern power systems introduces the reliability, security, and resilience challenges in transportation energy networks. The bidirectional power flow between the grid and EVs (V2G) produce a complex cyber intrusion physical network susceptible to electrical faults and cyber threats. this research proposed AI can perform early fault detection and cyber attack identification in integrating EV to smart grid (EV-Smart Grid) systems, ensuring secure and reliable energy transfer. This paper presents an intelligent framework for detection of faults and cybersecurity enhancement in EV to Smart Grid systems. This paper proposes an AI-based hybrid framework joining Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) models, and attention mechanisms of Transformer for real-time fault and cyber-anomaly detection. The system processes high-resolution voltage, current, and frequency measurements collected from EV charging systems. The model is trained and validated on simulated EV–Smart Grid datasets comprising high resolution three-phase voltage, current, and frequency signals under normal, faulted, and cyber-attack conditions. The proposed framework demonstrates high detection performance that achieve an accuracy of 0.9970, F1-score of 0.9990, and AUC of 0.9999. The results demonstrate robustness and high accuracy in detecting faults and cyber anomalies in electrical signals. This work contributes toward building secure, intelligent, and resilient smart mobility structures by merging AI-based fault diagnostics with cybersecurity monitoring, supporting the realization of trustworthy electric mobility and connected smart grid systems.
Riaz et al. (Thu,) studied this question.