A Cost-Efficient Approach to Managing Simultaneous Charging Sessions in Large-Scale EV Stations

The rapid adoption of electric vehicles (EVs) poses significant challenges for large-scale EV charging stations in terms of efficiently addressing dynamic charging demands and proactively managing grid load. This paper proposes a cost-efficient framework that extends the M/M/s queuing model to a time-varying M(t)/M/s(t) system, enabling adaptive charging session management based on uncertainty-aware predicted EV arrivals. A Bayesian Neural Network (BNN)-based model generates mean and uncertainty-aware upper bound (UB) arrival predictions, while a novel cost function, with linear and non-linear waiting costs and a tolerable waiting time, balances grid capacity reservation costs with user waiting time costs. The simulation results demonstrate that UB EV arrival predictions ensure robust performance under uncertainties. The non-linear cost model with UB prediction effectively reduces maximum waiting times by approximately 30% compared to the linear model, improving user satisfaction. Key findings highlight that incorporating tolerable waiting times of 10 to 20 minutes reduces total cost by approximately 12% to 25% relative to the no-threshold approach. Moreover, the adaptive demand estimation strategies outperform static approaches by dynamically adjusting to varying EV arrivals. The proposed framework offers a scalable and practical solution for managing operational costs and user expectations in future EV charging infrastructures.