Electric Autonomous Mobility-on-Demand: Joint Optimization of Routing and Charging Infrastructure Siting

TL;DR

This paper introduces a joint optimization framework for routing and charging infrastructure siting in electric autonomous mobility-on-demand systems, demonstrating improved efficiency and cost trade-offs through real-world case studies.

Contribution

It presents a novel mesoscopic modeling and mixed-integer linear programming approach for simultaneous fleet routing and charging infrastructure placement, ensuring global optimality.

Findings

Joint optimization outperforms heuristic policies.

Optimal number of charging stations balances infrastructure costs and fleet efficiency.

Smaller batteries reduce energy consumption despite more frequent charging.

Abstract

The advent of vehicle autonomy, connectivity and electric powertrains is expected to enable the deployment of Autonomous Mobility-on-Demand systems. Crucially, the routing and charging activities of these fleets are impacted by the design of the individual vehicles and the surrounding charging infrastructure which, in turn, should be designed to account for the intended fleet operation. This paper presents a modeling and optimization framework where we optimize the activities of the fleet jointly with the placement of the charging infrastructure. We adopt a mesoscopic planning perspective and devise a time-invariant model of the fleet activities in terms of routes and charging patterns, explicitly capturing the state of charge of the vehicles by resampling the road network as a digraph with iso-energy arcs. Then, we cast the problem as a mixed-integer linear program that guarantees global optimality and can be solved in less than 10 min. Finally, we showcase two case studies with real-world taxi data in Manhattan, NYC: The first one captures the optimal trade-off between charging infrastructure prevalence and the empty-mileage driven by the fleet. We observe that jointly optimizing the infrastructure siting significantly outperforms heuristic placement policies, and that increasing the number of stations is beneficial only up to a certain point. The second case focuses on vehicle design and shows that deploying vehicles equipped with a smaller battery results in the lowest energy consumption: Although necessitating more trips to the charging stations, such fleets require about 12% less energy than the vehicles with a larger battery capacity.