Highlights The interplay of autonomous electric vehicles operations is analyzed. Integrated optimization between repositioning and charging management is proposed. Simulations in New York City demonstrate noteworthy benefits from optimizations. Optimization approaches can increase at least 11% successfully served requests. Optimizations result in at least 14% reduction in charging downtime as well.
Abstract The convergence of electrification and automated driving will introduce opportunities to improve the operation and energy-efficiency of transportation systems. This paper discusses the challenges of dispatching autonomous electric vehicles (AEVs) in a ride-hailing fleet and their interactions with charging infrastructure. An integrated decision-making framework for dispatching and charging has been proposed using system optimization approaches. An agent-based platform has been developed for simulating and testing the proposed methods. A case study using New York City taxi data has been performed with different fleet sizes, dispatching strategies, and charging networks. Advantages of optimization-based approaches for AEV fleet management have been studied and demonstrated, for example, for a fleet of 1,750 AEVs to meet 100,000 daily requests, optimization-based centralized fleet management would result in 14% more ride requests satisfied and 43% fewer zero-occupancy miles traveled than if AEVs make independent decisions based on heuristic strategy. Benefits on reducing fleet size and charging downtime from optimization approaches are also comprehensively illustrated.
A framework for integrated dispatching and charging management of an autonomous electric vehicle ride-hailing fleet
2021-01-01
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms
BASE | 2019
|Europäisches Patentamt | 2023
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