Research Papers: Design Automation

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

[+] Author and Article Information
Namwoo Kang

Optimal Design Laboratory,
Department of Mechanical Engineering,
University of Michigan,
Ann Arbor, MI 48109
e-mail: nwkang@umich.edu

Fred M. Feinberg

Ross School of Business,
University of Michigan,
Ann Arbor, MI 48109
e-mail: feinf@umich.edu

Panos Y. Papalambros

Fellow ASME
Optimal Design Laboratory,
Department of Mechanical Engineering,
University of Michigan,
Ann Arbor, MI 48109
e-mail: pyp@umich.edu

On an Intel i7 CPU 860@2.80 GHz and 8.00 GB RAM, an optimization run took 36 hr on average.

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received September 24, 2014; final manuscript received February 16, 2015; published online March 30, 2015. Assoc. Editor: Gary Wang.

J. Mech. Des 137(6), 061402 (Jun 01, 2015) (10 pages) Paper No: MD-14-1638; doi: 10.1115/1.4029894 History: Received September 24, 2014; Revised February 16, 2015; Online March 30, 2015

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.

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Fig. 1

Framework of decision-making

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Fig. 2

Engineering simulation model

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Fig. 4

CSs coverage for each city under two business models

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Fig. 5

Southeast Michigan highway network and optimal locations of CSs

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Fig. 6

Local path coverage of CSs for Ann Arbor, MI, residents

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Fig. 7

Histogram of profit differences between results from the two business models

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Fig. 8

Tradeoff between EV profit and station profit

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Fig. 9

Postoptimal analysis




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