Joint Optimization of Autonomous Electric Vehicle Fleet Operations and Charging Station Siting
Justin Luke, Mauro Salazar, Ram Rajagopal, Marco Pavone

TL;DR
This paper presents a linear programming approach to jointly optimize charging station siting and fleet operations for autonomous electric vehicles, reducing costs and improving spatial distribution based on demand and energy costs.
Contribution
It introduces a novel joint optimization model for EV fleet routing and charging station siting, integrating infrastructure planning with fleet operations.
Findings
Optimal siting favors high-power Level 2 stations and some fast chargers.
Joint optimization reduces total costs, empty travel, and peak load by up to 10%.
Small, efficient EVs are most cost-effective despite range limitations.
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…
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Taxonomy
MethodsEmirates Airlines Office in Dubai
