Smart Charging Benefits in Autonomous Mobility on Demand Systems

Abstract

In this paper, we study the potential benefits from smart charging for a fleet of electric vehicles (EVs) providing autonomous mobility-on-demand (AMoD) services. We first consider a profit-maximizing platform operator who makes decisions for routing, charging, rebalancing, and pricing for rides based on a network flow model. Clearly, each of these decisions directly influence the fleet's smart charging potential; however, it is not possible to directly characterize the effects of various system parameters on smart charging under a classical network flow model. As such, we propose a modeling variation that allows us to decouple the charging and routing problems faced by the operator. This variation allows us to provide closedform mathematical expressions relating the charging costs to the maximum battery capacity of the vehicles as well as the fleet operational costs. We show that investing in larger battery capacities and operating more vehicles for rebalancing reduces the charging costs, while increasing the fleet operational costs. Hence, we study the trade-off the operator faces, analyze the minimum cost fleet charging strategy, and provide numerical results illustrating the smart charging benefits to the operator.

Fleet Management and Charging Scheduling for Shared Mobility-on-Demand System: A Systematic Review

“…The articles which not consider the interaction between power and transportation network could be divided into three similar categories. [56][57][58][59][60][61][62]. a) For the vehicle-centric modeling, different time scales are considered to decide vehicle scheduling [57].…”

“…The number of vehicles and customers at a node obeys the nonlinear time-delayed differential equations. In addition, the charging and routing problems could be decoupled under the assumption that electricity prices at the destination nodes of all current trips are unknown to the operator [59]. Electric traveling salesman with time windows is developed in [58] to solve customer routing and recharging with the aim of minimizing the total distance of the selected arcs and recharging paths.…”

Algorithms to Manage Congestion in Large-Scale Mobility-on-Demand Schemes that Use Electric Vehicles

“…They propose an online welfare maximization heuristic that allocates the vehicles and rebalances them while avoiding congestion at charging facilities and pick-up locations. Finally, Turan et al [36] studied the financial implications of smart charging in MoD schemes and they conclude that investing in larger battery capacities and operating more vehicles for rebalancing reduces the charging costs, but increases the fleet operational costs.…”

Congestion Management for Mobility-on-Demand Schemes that Use Electric Vehicles

“…Given this, they propose a methodology that uses a random search algorithm to optimize the fleet size and distribution to maximize the number of serviced customers. Moreover, Turan et al [22] study the financial implications of smart charging in MoD schemes and they conclude that investing in larger battery capacities and operating more vehicles for rebalancing reduces the charging costs, but increases the fleet operational costs. Finally, Gkourtzounis et al [12] propose a software package that allows for efficient management of a MoD scheme from the side of a company, and easy trip requests for customers.…”