A Data-Driven Framework for Inefficient EV Charging-Session Identification and Intelligent Infrastructure Management

As public electric vehicle (EV) charging networks expand, infrastructure performance increasingly depends on whether occupied charging time is converted into useful energy delivery. This study develops a hybrid data-driven framework to identify, predict, and explain inefficient EV charging sessions for intelligent infrastructure management. Using charging-session data from California, inefficient sessions are defined by the joint condition of a high idle ratio and low energy delivered per occupied hour. K-means clustering and HDBSCAN are applied to examine charging-session typologies, while four machine learning methods are compared for session-level prediction. Model interpretation is conducted using permutation feature importance, partial dependence plots, and rule extraction. The results show that inefficient charging is concentrated in a long-stay, low-output profile distinct from productive long-duration charging. XGBoost provides the strongest overall predictive performance, and pricing conditions, user routines, temporal patterns, and station-utilisation context emerge as key drivers of inefficient charging risk.