Integrated Decision Making in Electric Vehicle and Charging Station Location Network Design

被引:32
|
作者
Kang, Namwoo [1 ]
Feinberg, Fred M. [2 ]
Papalambros, Panos Y. [1 ]
机构
[1] Univ Michigan, Optimal Design Lab, Dept Mech Engn, Ann Arbor, MI 48109 USA
[2] Univ Michigan, Ross Sch Business, Ann Arbor, MI 48109 USA
关键词
ALLOCATION; DISPERSION; EMISSIONS; HYBRID; MODELS;
D O I
10.1115/1.4029894
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
A major barrier in consumer adoption of electric vehicles (EVs) is "range anxiety," the concern that the vehicle will run out of power at an inopportune time. Range anxiety is caused by the current relatively low electric-only operational range and sparse public charging station (CS) infrastructure. Range anxiety may be significantly mitigated if EV manufacturers and CS operators work in partnership using a cooperative business model to balance EV performance and CS coverage. This model is in contrast to a sequential decision-making model where manufacturers bring new EVs to the market first and CS operators decide on CS deployment given EV specifications and market demand. This paper proposes an integrated decision-making framework to assess profitability of a cooperative business model using a multidisciplinary optimization model that combines marketing, engineering, and operations considerations. This model is demonstrated in a case study involving battery EV design and direct current (DC) fast-CS location network in Southeast Michigan. The expected benefits can motive both government and private enterprise actions.
引用
收藏
页数:10
相关论文
共 50 条
  • [41] A decision support system for the optimal location of electric vehicle charging points
    Bersani, Chiara
    Zero, Enrico
    Sacile, Roberto
    2019 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC), 2019, : 2707 - 2712
  • [42] Electric vehicle charging station locations: Elastic demand, station congestion, and network equilibrium
    Huang, Yantao
    Kockelman, Kara M.
    TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT, 2020, 78
  • [43] Modeling of Electric Vehicle Fast Charging Station and Impact on Network Voltage
    Yong, Jia Ying
    Ramachandaramurthy, Vigna K.
    Tan, Kang Miao
    Arulampalam, Atputharajah
    2013 IEEE CONFERENCE ON CLEAN ENERGY AND TECHNOLOGY (CEAT), 2013, : 399 - 404
  • [44] Queueing Network Models for Electric Vehicle Charging Station with Battery Swapping
    Tan, Xiaoqi
    Sun, Bo
    Tsang, Danny H. K.
    2014 IEEE INTERNATIONAL CONFERENCE ON SMART GRID COMMUNICATIONS (SMARTGRIDCOMM), 2014, : 1 - 6
  • [45] Innovative Smart Network Enabled Charging Station for Future Electric Vehicle
    Paramasivam A.
    Vijayalakshmi S.
    Swamynathan K.
    Mahalingam N.
    Gughan N.M.
    Journal of The Institution of Engineers (India): Series B, 2024, 105 (01) : 147 - 156
  • [46] Pricing Differentiated Services in an Electric Vehicle Public Charging Station Network
    Moradipari, Ahmadreza
    Alizadeh, Mahnoosh
    2018 IEEE CONFERENCE ON DECISION AND CONTROL (CDC), 2018, : 6488 - 6494
  • [47] Location decision of electric vehicle charging station based on a novel grey correlation comprehensive evaluation multi-criteria decision method
    Zhao, Hui
    Hao, Xiang
    ENERGY, 2024, 299
  • [48] A three-phase fuzzy multi-criteria decision model for charging station location of the sharing electric vehicle
    Liu, Aijun
    Zhao, Yingxue
    Meng, Xiaoge
    Zhang, Yan
    INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS, 2020, 225
  • [49] A two-stage multi-objective interval location optimization decision of electric vehicle charging station under charging interruption scenario
    Sun B.-Z.
    Yang J.-N.
    Bai J.-C.
    Chu X.-L.
    Wang T.
    Chen X.-T.
    Kongzhi yu Juece/Control and Decision, 2022, 37 (04): : 1005 - 1014
  • [50] System design for a solar powered electric vehicle charging station for workplaces
    Mouli, G. R. Chandra
    Bauer, P.
    Zeman, M.
    APPLIED ENERGY, 2016, 168 : 434 - 443