Electric Vehicle Fleet and Charging Infrastructure Planning

Abstract

We study electric vehicle (EV) fleet and charging infrastructure planning in a spatial setting. With customer requests arriving continuously at 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. While non-EV systems require extra $\Theta(\lambda^{2/3})$ vehicles due to pickup times, EV systems differ. Charging increases nominal capacity, enabling pickup time reductions and allowing for an extra fleet requirement of only $\Theta(\lambda^{\nu})$ 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. Extensive simulations verify our scaling results, insights, and policy effectiveness while also showing the viability of low-range, low-cost fleets.