Electric Vehicle Fleet and Charging Infrastructure Planning

We study electric vehicle (EV) fleet and charging infrastructure planning in a spatial setting. For a centrally managed fleet that serves customer requests arriving continuously at a rate $\lambda$ throughout the day, we determine the minimum number of vehicles and chargers for a target service level along with matching and charging policies. Whereas non-EV systems require extra $\mathrm{\Theta }({\lambda }^{2/3})$ vehicles because of pickup times, EV systems differ. Charging increases nominal capacity, enabling pickup time reductions and allowing for an extra fleet requirement of only $\mathrm{\Theta }({\lambda }^{\nu })$ for $\nu \in (1/2,2/3]$, depending on charging infrastructure and battery pack sizes. We propose the power-of-d dispatching policy, which achieves this performance by selecting the closest vehicle with the highest battery level from d options. We extend our results to accommodate time-varying demand patterns and discuss conditions for transitioning between EV and non-EV capacity planning. Simulations verify our scaling results, insights, and policy effectiveness. Whereas long-range, fast-charging fleets resemble non-EV systems, short-range, low-cost fleets can still perform competitively, underscoring the need for EV-aware management policies.

This paper was accepted by Victor Martínez de Albéniz, operations management.

Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2024.08524.