# Electric Vehicle Fleet and Charging Infrastructure Planning

arXiv: https://arxiv.org/abs/2306.10178 · HTML: https://mlsanity.com/paper/2306.10178/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2023.

## Built on by (most cited first, 2 of 2)

- [Atomic Proximal Policy Optimization for Electric Robo-Taxi Dispatch and Charger Allocation](https://mlsanity.com/paper/2502.13392/) (2025)
- [Emergent Charging Coordination in Electric Delivery Fleets](https://mlsanity.com/paper/2609.07689/) (2026)

## Abstract (arXiv)

We study electric vehicle (EV) fleet and charging infrastructure planning in a spatial setting. With customer requests arriving continuously at rate λ 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(λ2/3) 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(λnu) for nu ∈ (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.