• Monte Carlo simulations capture uncertainty in EV fast-charging station demand. • Charging protocols are compared under realistic power and queuing constraints. • The optimized OPT-FDP protocol achieves shorter charging times. • Optimal station sizes: 200 kW (urban) and 900 kW (highway) The paper reports a comparative study on the performance of fast-charging protocols for electric vehicles (EVs), evaluated through a stochastic simulation framework, representing both urban and highway charging stations. Three charging protocols are modeled and compared: two derived from commercially available vehicles (Nio and Tesla Model 3) and one optimization-based protocol (OPT-FDP), developed by the Authors, that balances charging speed and battery degradation. The framework accounts for queue management and power-allocation strategies, and Monte Carlo simulations reproduce variability in daily traffic and initial state-of-charges for realistic operating conditions. Key performance indicators include total delivered energy, number of vehicles served, and median station time, representing both user −and operator- oriented perspectives. Two case studies have been developed. In urban contexts, station power capacity is the dominant variable affecting throughput, with 200 kW identified as the minimum rating to ensure user-perceived fast charging, keeping median station time below 30 min and serving approximately 100% of daily arrivals. In highway scenarios, power and sockets number jointly determine service rate, with 900 kW and 10 sockets identified as the ideal size for 200 EVs per day, delivering about 9000 kWh/day and serving approximately 99% of vehicles. Among the tested protocols, OPT-FDP consistently minimizes charging time, achieving median station times of approximately 15 min in properly sized highway configurations, compared to about 20–25 min for Tesla and Nio protocols, without compromising energy delivery. These findings provide quantitative insight into planning future high-power charging infrastructure, highlighting ideal station sizing and management strategies for large-scale EV adoption.
Mithoowani et al. (Sun,) studied this question.