An Anomaly-Aware, Q-Learning Framework for Real-Time Scheduling in Multi-Station EV Charging Networks

Electric vehicle (EV) charging networks face major operational challenges, including demand uncertainty, peak-load congestion, and anomalous charging behavior, particularly in multi-station environments. This study proposes an anomaly-aware Q-learning framework for real-time scheduling in multi-station EV charging systems by integrating short-term load forecasting, anomaly detection, and intelligent scheduling within a unified operational pipeline. The framework combines Prophet, XGBoost, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models for short-term demand forecasting, while Convolutional Neural Networks (CNN), Autoencoders, and Isolation Forests are employed for anomaly detection. Forecasting and anomaly information are incorporated into a Q-learning scheduler to support adaptive charger allocation and congestion management. Evaluation using a four-year, real-world dataset comprising more than 2000 EV charging sessions demonstrates improved scheduling performance, achieving reductions in peak load and waiting time while improving energy delivery consistency. The framework further demonstrates low scheduling latency, supporting suitability for real-time deployment in OCPP-compliant smart charging infrastructures.