Sharma Ankita, Rani Shalli, Shabaz Mohammad
Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, 140401, India.
Model Institute of Engineering and Technology, Jammu, J&K, India.
Sci Rep. 2025 Jul 1;15(1):21653. doi: 10.1038/s41598-025-04984-4.
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.
电动汽车（EV）在向可持续交通转型并减少温室气体排放方面发挥着关键作用。然而，将电动汽车集成到智能电网中带来了重大的网络安全和运营挑战。本研究提出了人工智能增强的智能电网架构，以建立一个安全高效的电动汽车充电基础设施。所提出的框架识别出关键的网络安全威胁，包括针对电动汽车充电基础设施的网络物理漏洞和基于软件的攻击。它纳入了人工智能驱动的安全模型和异常检测算法，以增强电网弹性并优化能源利用。通过利用实时数据分析，该系统通过车对网（V2G）技术实现预测性威胁缓解和能源负载平衡。广泛的性能评估表明，所提出的框架在准确性、可扩展性和响应时间方面超越了现有解决方案，确保了强大且可靠的电动汽车充电基础设施。该系统持续监控充电数据，检测异常，并迅速缓解潜在的网络威胁。实验结果显示出高准确率（96.8%）、召回率（96.0%）和F1分数（96.4%），以及98.9%的网络攻击检测率，证明了该框架在保障电动汽车基础设施安全方面的有效性。所提出的架构便于无缝扩展并集成到现有的电动汽车充电基础设施中，同时确保一个安全、有弹性且可持续的能源生态系统。
