Joint Optimization of Number of Vehicles, Battery Capacity and Operations of an Electric Autonomous Mobility-on-Demand Fleet

• Main Authors: Paparella, Fabio, Hofman, Theo, Salazar, Mauro
• Format: Conference Proceeding
• Language: English
• Subjects: Batteries; Energy consumption; Heating systems; Mechanical power transmission; NP-hard problem; Transportation; Urban areas
• ISSN: 2576-2370
• DOI: 10.1109/CDC51059.2022.9993118

The advent of vehicle autonomy and powertrain electrification is paving the way to the deployment of Autonomous Mobility-on-Demand (AMoD) systems whereby electric self-driving vehicles provide on-demand mobility. To maximize the performance of AMoD fleets, the number of vehicles and their individual design in terms of battery size must be tailored to the envisioned operation. At the same time, the operation of the electric AMoD fleet is strongly influenced by its design-e.g., smaller batteries will require to charge the vehicles more often, while entailing lower investment costs and energy consumption. This paper proposes to solve this tension in an integrated manner by devising a framework to jointly optimize the design and operation of an electric AMoD system, where the objective is to maximize the total profit of the fleet operator. In particular, we first define the fleet operational problem in terms of vehicle coordination and charge scheduling. Second, we include the battery sizing problem for the individual vehicles and its influence on their energy consumption. Finally, we capture the number of used vehicles as an additional degree of freedom, and frame the profit maximization problem as a mixed integer linear program which can be solved with global optimality guarantees using off-the-shelf optimization algorithms. We showcase our framework for a real-world case-study of New York City, revealing a trade-off between number of vehicles, their battery size and the amount of requests they can serve. Moreover, our results show that a significantly lower battery size can be used w.r.t. the state of the art, resulting in energy consumption reductions by up to 20%.