Electric Autonomous Mobility-on-Demand: Jointly Optimal Vehicle Design and Fleet Operation

TL;DR

This paper develops a joint optimization framework for electric autonomous vehicle fleet design and operation to maximize profit, demonstrating its effectiveness through a Manhattan case study.

Contribution

It introduces a mixed integer linear programming approach for joint vehicle design and fleet operation optimization, including scalable solution algorithms for large instances.

Findings

Small, light vehicles with 20 kWh batteries optimize profit and operational efficiency.

Joint optimization significantly improves system-level performance over separate design and operation.

The framework is validated on a real-world Manhattan case study.

Abstract

The advent of autonomous driving and electrification is enabling the deployment of Electric Autonomous Mobility-on-Demand (E-AMoD) systems, whereby electric autonomous vehicles provide on-demand mobility. Crucially, the design of the individual vehicles and the fleet, and the operation of the system are strongly coupled. Hence, to maximize the system-level performance, they must be optimized in a joint fashion. To this end, this paper presents a framework to jointly optimize the fleet design in terms of battery capacity and number of vehicles, and the operational strategies of the E-AMoD system, with the aim of maximizing the operator's total profit. Specifically, we first formulate this joint optimization problem using directed acyclic graphs as a mixed integer linear program, which can be solved using commercial solvers with optimality guarantees. Second, to solve large instances of the problem, we propose a solution algorithm that solves for randomly sampled sub-problems, providing a more conservative solution of the full problem, and devise a heuristic approach to tackle larger individual sub-problem instances. Finally, we showcase our framework on a real-world case study in Manhattan, where we demonstrate the interdependence between the number of vehicles, their battery size, and operational and fixed costs. Our results indicate that to maximize a mobility operator's profit, a fleet of small and light vehicles with battery capacity of 20 kWh only can strike the best trade-off in terms of battery degradation, fixed costs and operational efficiency.