Electronic Devices Jie Wei Zhang 15 2 2026 https://doi.org/10.6025/ed/2026/15/2/72-103 https://www.dline.info/ed/fulltext/v15n2/edv15n2_2.pdf The rapid adoption of electric vehicles (EVs) has intensified demands on charging infrastructure, yet conventional performance metrics based on energy delivery alone fail to capture operational inefficiencies such as post charging idle occupation. This study investigates station level utilization and idle time behavior in campus EV charging infrastructure using the SFU-EVP longitudinal dataset, comprising 91,518 charging sessions across 32 stations at Simon Fraser University. Three temporal indicators such ascharging time utilization, occupancy utilization, and idle time ratio are combined with demand and throughput measures to construct a seven dimensional station performance feature space. Descriptive analysis reveals that approximately 23.57% of total plugged in time is idle, with median occupancy utilization (26.64%) exceeding median charging utilization (20.78%), confirming pervasive post-charging occupation. Principal Component Analysis captures 86.01% of station level variation in two components, and K-Means clustering (silhouette score ï‚» 0.50) identifies two distinct station typologies: underutilized/low demand stations (28 stations, 16.96% charging utilization, 24.82% idle ratio) and high performing/high demand stations (4 stations, 46.93% charging utilization, 19.38% idle ratio). Only four stations, 12.5% of the infrastructure, account for approximately 43.6% of all sessions, demonstrating a strong demand concentration effect accompanied by greater behavioral efficiency. Hierarchical clustering validates the two group structure. The findings indicate that effective infrastructure planning must distinguish between capacity expansion at high demand hubs and behavioral interventions to reduce idle occupancy across the broader network, shifting analytical focus from how much infrastructure is required to how effectively existing infrastructure is utilized.