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The increasing adoption of electric vehicles (EVs) places growing pressure on public charging infrastructure, where congestion and prolonged waiting times can adversely affect service responsiveness and charging-station operation. This paper presents a priority-based smart queue management framework for IoT-enabled electric vehicle charging stations (EVCSs). The proposed framework integrates vehicle-category priority, battery state-of-charge (SoC), reservation status, congestion-aware station assignment and reallocation, bounded deferral, and dynamic pricing to support differentiated charging service while maintaining infrastructure utilization and grid stability. A heuristic scheduling mechanism is employed for computationally efficient implementation of the underlying optimization formulation. The framework is evaluated using a publicly available synthetic EV charging dataset under a controlled simulation environment and compared with First-Come-First-Serve (FCFS), Static Scheduling, and SoC-Based Priority Scheduling. To account for stochastic variability, the evaluation is conducted over 30 independent simulation runs using matched workloads across the scheduling methods. The proposed method achieves a mean waiting time of 133.87 min, compared with 177.42 min for FCFS, 176.88 min for Static Scheduling, and 177.75 min for SoC-Based Priority Scheduling, corresponding to reductions of 24.55%, 24.32%, and 24.69%, respectively. Paired statistical tests confirm significant waiting-time reductions for all three comparisons ( p < 0.001 ), with large paired effect sizes. The proposed method also maintains infrastructure utilization and grid-stability characteristics broadly comparable to the considered baselines while providing differentiated service across user priority categories. These results demonstrate the effectiveness of the proposed framework as a computationally lightweight approach for congestion-aware and priority-sensitive EV charging management.
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Debnath et al. (2026) studied this question.
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