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Title: A framework for integrated dispatching and charging management of an autonomous electric vehicle ride-hailing fleet

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.

Authors:
ORCiD logo [1]; ORCiD logo [1]
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  1. Idaho National Lab. (INL), Idaho Falls, ID (United States)
Publication Date:
Research Org.:
Idaho National Laboratory (INL), Idaho Falls, ID (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
OSTI Identifier:
1779826
Alternate Identifier(s):
OSTI ID: 1780513
Report Number(s):
INL/JOU-19-56960-Rev000
Journal ID: ISSN 1361-9209; TRN: US2209704
Grant/Contract Number:  
AC07-05ID14517
Resource Type:
Accepted Manuscript
Journal Name:
Transportation Research. Part D, Transport and Environment
Additional Journal Information:
Journal Volume: 95; Journal ID: ISSN 1361-9209
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; 97 MATHEMATICS AND COMPUTING; Autonomous Electric Vehicle; Fleet Management; Charging Infrastructure; Ride Hailing; Charging management

Citation Formats

Yi, Zonggen, and Smart, John G. A framework for integrated dispatching and charging management of an autonomous electric vehicle ride-hailing fleet. United States: N. p., 2021. Web. doi:10.1016/j.trd.2021.102822.
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Yi, Zonggen, & Smart, John G. A framework for integrated dispatching and charging management of an autonomous electric vehicle ride-hailing fleet. United States. https://doi.org/10.1016/j.trd.2021.102822
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Yi, Zonggen, and Smart, John G. Wed . "A framework for integrated dispatching and charging management of an autonomous electric vehicle ride-hailing fleet". United States. https://doi.org/10.1016/j.trd.2021.102822. https://www.osti.gov/servlets/purl/1779826.
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@article{osti_1779826,
title = {A framework for integrated dispatching and charging management of an autonomous electric vehicle ride-hailing fleet},
author = {Yi, Zonggen and Smart, John G.},
abstractNote = {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.},
doi = {10.1016/j.trd.2021.102822},
journal = {Transportation Research. Part D, Transport and Environment},
number = ,
volume = 95,
place = {United States},
year = {Wed Apr 28 00:00:00 EDT 2021},
month = {Wed Apr 28 00:00:00 EDT 2021}
}
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Journal Article:
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Publisher's Version of Record
https://doi.org/10.1016/j.trd.2021.102822
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