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Title: Joint Fleet Sizing and Charging System Planning for Autonomous Electric Vehicles

Abstract

This paper studies the joint fleet sizing and charging system planning problem for a company operating a fleet of autonomous electric vehicles (AEVs) for passenger and goods transportation. Most of the relevant published papers focus on intracity scenarios and adopt heuristic approaches, e.g., agent based simulation, which do not guarantee optimality. In contrast, we propose a mixed integer linear programming model for intercity scenarios. This model incorporates comprehensive considerations of 1) limited AEV driving range; 2) optimal AEV routing and relocating operations; 3) time-varying origin-destination transport demands; and 4) differentiated operation cost structure of passenger and goods transportation. The proposed model can be computational expensive when the scale of the transportation network is large. We then exploit the structure of this program to expedite its solution. Numerical experiments are conducted to validate the proposed method. Our experimental results show that AEVs in passenger and goods transportation have remarkable planning and operation differences. We also demonstrate that intelligent routing and relocating operations, charging system and vehicle parameters, e.g., charging power, battery capacity, driving speed etc., can significantly affect the economic efficiency and the planning results of an AEV fleet.

Authors:
ORCiD logo [1];  [2];  [3]; ORCiD logo [4]
  1. Univ. of California, Berkeley, CA (United States); Univ. of Macau (China)
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  3. Univ. of California, Berkeley, CA (United States)
  4. Univ. of California, Berkeley, CA (United States); Tsinghua-Berkeley Shenzhen Institute (China)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Transportation Office. Vehicle Technologies Office; Science and Technology Development Fund
OSTI Identifier:
1844923
Grant/Contract Number:  
AC02-05CH11231; SKL-IOTSC-2018-2020
Resource Type:
Accepted Manuscript
Journal Name:
IEEE Transactions on Intelligent Transportation Systems
Additional Journal Information:
Journal Volume: 21; Journal Issue: 11; Journal ID: ISSN 1524-9050
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
33 ADVANCED PROPULSION SYSTEMS; planning; companies; computational modeling; numerical models; routing; electric vehicles; autonomous vehicle; fleet size; charging system planning; relocating

Citation Formats

Zhang, Hongcai, Sheppard, Colin R., Lipman, Timothy E., and Moura, Scott J. Joint Fleet Sizing and Charging System Planning for Autonomous Electric Vehicles. United States: N. p., 2019. Web. doi:10.1109/tits.2019.2946152.
Zhang, Hongcai, Sheppard, Colin R., Lipman, Timothy E., & Moura, Scott J. Joint Fleet Sizing and Charging System Planning for Autonomous Electric Vehicles. United States. https://doi.org/10.1109/tits.2019.2946152
Zhang, Hongcai, Sheppard, Colin R., Lipman, Timothy E., and Moura, Scott J. Wed . "Joint Fleet Sizing and Charging System Planning for Autonomous Electric Vehicles". United States. https://doi.org/10.1109/tits.2019.2946152. https://www.osti.gov/servlets/purl/1844923.
@article{osti_1844923,
title = {Joint Fleet Sizing and Charging System Planning for Autonomous Electric Vehicles},
author = {Zhang, Hongcai and Sheppard, Colin R. and Lipman, Timothy E. and Moura, Scott J.},
abstractNote = {This paper studies the joint fleet sizing and charging system planning problem for a company operating a fleet of autonomous electric vehicles (AEVs) for passenger and goods transportation. Most of the relevant published papers focus on intracity scenarios and adopt heuristic approaches, e.g., agent based simulation, which do not guarantee optimality. In contrast, we propose a mixed integer linear programming model for intercity scenarios. This model incorporates comprehensive considerations of 1) limited AEV driving range; 2) optimal AEV routing and relocating operations; 3) time-varying origin-destination transport demands; and 4) differentiated operation cost structure of passenger and goods transportation. The proposed model can be computational expensive when the scale of the transportation network is large. We then exploit the structure of this program to expedite its solution. Numerical experiments are conducted to validate the proposed method. Our experimental results show that AEVs in passenger and goods transportation have remarkable planning and operation differences. We also demonstrate that intelligent routing and relocating operations, charging system and vehicle parameters, e.g., charging power, battery capacity, driving speed etc., can significantly affect the economic efficiency and the planning results of an AEV fleet.},
doi = {10.1109/tits.2019.2946152},
journal = {IEEE Transactions on Intelligent Transportation Systems},
number = 11,
volume = 21,
place = {United States},
year = {2019},
month = {10}
}