Risk Classification of EV Charging Stations Using Machine Learning

The rapid global adoption of Electric Vehicles (EVs) necessitates the development of reliable and resilient charging infrastructure. However, EV charging stations are subject to diverse risks, including operational downtime, safety hazards, financial inefficiencies, and cybersecurity threats. This research paper explores the application of Machine Learning (ML) techniques for classifying risks associated with EV charging stations. By employing algorithms such as Random Forest, XGBoost, Neural Networks, and Clustering methods, the study aims to identify the most effective approaches for risk prediction and mitigation. The methodology includes preprocessing both real-world and simulated datasets, feature engineering, and model evaluation using metrics like accuracy, precision, recall, F1-score, and ROC-AUC. Results highlight significant risk factors, such as equipment failures, load fluctuations, maintenance schedules, and cybersecurity vulnerabilities. A framework for real-time monitoring integrating ML-based risk alerts is proposed. This research provides actionable insights for operators, policymakers, and end-users, facilitating safer, more reliable, and economically sustainable EV charging networks.