Joint Optimization of Autonomous Electric Vehicle Fleet Operations and Charging Station Siting

TL;DR

This work addresses the coupling of charging infrastructure design with autonomous electric vehicle fleet operations by formulating a convex linear-programming framework on a time-expanded network that jointly optimizes charging-station siting, fleet size, routing, and charging. The objective aggregates fleet procurement, station procurement, energy costs, and demand charges, while ensuring all travel requests are fulfilled and charging constraints are respected, including throttling at stations. A San Francisco case study across three vehicle sizes demonstrates that smaller, high-energy-efficient EVs can minimize total ownership costs, and that joint siting with fleet operations yields up to about 10% reductions in total cost, peak charging load, and empty-vehicle travel, alongside substantial reductions in DC fast-charging requirements. The results underscore the value of integrating charging infrastructure planning with mobility-on-demand operations to achieve cost-effective, distributed charging patterns suitable for high-penetration autonomous EV fleets.

Abstract

Charging infrastructure is the coupling link between power and transportation networks, thus determining charging station siting is necessary for planning of power and transportation systems. While previous works have either optimized for charging station siting given historic travel behavior, or optimized fleet routing and charging given an assumed placement of the stations, this paper introduces a linear program that optimizes for station siting and macroscopic fleet operations in a joint fashion. Given an electricity retail rate and a set of travel demand requests, the optimization minimizes total cost for an autonomous EV fleet comprising of travel costs, station procurement costs, fleet procurement costs, and electricity costs, including demand charges. Specifically, the optimization returns the number of charging plugs for each charging rate (e.g., Level 2, DC fast charging) at each candidate location, as well as the optimal routing and charging of the fleet. From a case-study of an electric vehicle fleet operating in San Francisco, our results show that, albeit with range limitations, small EVs with low procurement costs and high energy efficiencies are the most cost-effective in terms of total ownership costs. Furthermore, the optimal siting of charging stations is more spatially distributed than the current siting of stations, consisting mainly of high-power Level 2 AC stations (16.8 kW) with a small share of DC fast charging stations and no standard 7.7kW Level 2 stations. Optimal siting reduces the total costs, empty vehicle travel, and peak charging load by up to 10%.

Figures (4)

• Figure 1: Distribution of vehicle status over time for the fleet, with charging station siting jointly optimized.
• Figure 2: (a) Heat map of installed charging capacity resulting from the siting optimization with the fleet. (b) Heat map of the difference in installed charging capacity for a fleet between the joint charging station siting optimization scenario and the baseline siting scenario. Red indicates greater installed capacity in the optimized scenario whereas blue indicates greater capacity in the baseline scenario.
• Figure 3: (a) Comparison of total charging load between fleets of varying models. (b) Comparison of total charging load for Leaf S fleet between optimized station siting and baseline station siting scenario. In both figures, the electricity price is plotted against the right axis.
• Figure 4: (a) Comparison of installed charging capacity by charging rate between fleets of varying models. (b) Comparison of the cumulative distribution of installed charging capacity across locations, between the baseline siting scenario and optimized siting results using varying model fleets.