Artificial intelligence-augmented smart grid architecture for cyber intrusion detection and mitigation in electric vehicle charging infrastructure

Abstract

The role of electric vehicles (EV) is crucial in the shift toward sustainable transportation while reducing greenhouse gas emissions. However, integrating EVs into smart grids introduces significant cybersecurity and operational challenges. This study proposes AI-augmented smart grid architecture to establish a secure and efficient EV charging infrastructure. The proposed framework identifies key cybersecurity threats, including cyber-physical vulnerabilities and software-based attacks targeting EV charging infrastructure. It incorporates AI-driven security models and anomaly detection algorithms to enhance grid resilience and optimize energy utilization. By leveraging real-time data analytics, the system enables predictive threat mitigation and energy load balancing through vehicle-to-grid (V2G) technologies. Extensive performance evaluations reveal that the proposed framework surpasses existing solutions in terms of accuracy, scalability, and response time, ensuring a robust and reliable EV charging infrastructure. The system continuously monitors charging data, detects anomalies, and swiftly mitigates potential cyber threats. Experimental results demonstrate high accuracy (96.8%), recall (96.0%), F1-score (96.4%), and a cyberattack detection rate of 98.9%, proving the framework’s effectiveness in securing EV infrastructure. The proposed architecture facilitates seamless scalability and integration into existing EV charging infrastructure while ensuring a safe, resilient, and sustainable energy ecosystem.