IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i9p2436-d169751.html
   My bibliography  Save this article

Deploying Electric Vehicle Charging Stations Considering Time Cost and Existing Infrastructure

Author

Listed:
  • Yuan Qiao

    (State Key Laboratory of Automotive Safety and Energy, Department of Automotive Engineering, Tsinghua University, Beijing 100084, China)

  • Kaisheng Huang

    (State Key Laboratory of Automotive Safety and Energy, Department of Automotive Engineering, Tsinghua University, Beijing 100084, China
    Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing 100081, China)

  • Johannes Jeub

    (State Key Laboratory of Automotive Safety and Energy, Department of Automotive Engineering, Tsinghua University, Beijing 100084, China)

  • Jianan Qian

    (State Key Laboratory of Automotive Safety and Energy, Department of Automotive Engineering, Tsinghua University, Beijing 100084, China)

  • Yizhou Song

    (State Key Laboratory of Automotive Safety and Energy, Department of Automotive Engineering, Tsinghua University, Beijing 100084, China)

Abstract

Under the challenge of climate change, fuel-based vehicles have been receiving increasingly harsh criticism. To promote the use of battery electric vehicles (BEVs) as an alternative, many researchers have studied the deployment of BEVs. This paper proposes a new method to choose locations for new BEV charging stations considering drivers’ perceived time cost and the existing infrastructure. We construct probability equations to estimate drivers’ demanding time for charging (and waiting to charge), use the Voronoi diagram to separate the study area (i.e., Shanghai) into service areas, and apply an optimization algorithm to deploy the charging stations in the right locations. The results show that (1) the probability of charging at public charging stations is 39.6%, indicating BEV drivers prefer to charge at home; (2) Shanghai’s central area and two airports have the busiest charging stations, but drivers’ time costs are relatively low; and (3) our optimization algorithm successfully located two new charging stations surrounding the central area, matching with our expectations. This study provides a time-efficient way to decide where to build new charging stations to improve the existing infrastructure.

Suggested Citation

  • Yuan Qiao & Kaisheng Huang & Johannes Jeub & Jianan Qian & Yizhou Song, 2018. "Deploying Electric Vehicle Charging Stations Considering Time Cost and Existing Infrastructure," Energies, MDPI, vol. 11(9), pages 1-13, September.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:9:p:2436-:d:169751
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/9/2436/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/9/2436/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Daniel Kahneman, 2003. "Maps of Bounded Rationality: Psychology for Behavioral Economics," American Economic Review, American Economic Association, vol. 93(5), pages 1449-1475, December.
    2. Morrissey, Patrick & Weldon, Peter & O’Mahony, Margaret, 2016. "Future standard and fast charging infrastructure planning: An analysis of electric vehicle charging behaviour," Energy Policy, Elsevier, vol. 89(C), pages 257-270.
    3. Tversky, Amos & Kahneman, Daniel, 1986. "Rational Choice and the Framing of Decisions," The Journal of Business, University of Chicago Press, vol. 59(4), pages 251-278, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chunhong Sheng & Yun Cao & Bing Xue, 2018. "Residential Energy Sustainability in China and Germany: The Impact of National Energy Policy System," Sustainability, MDPI, vol. 10(12), pages 1-18, December.
    2. Yuan Qiao & Yizhou Song & Kaisheng Huang, 2019. "A Novel Control Algorithm Design for Hybrid Electric Vehicles Considering Energy Consumption and Emission Performance," Energies, MDPI, vol. 12(14), pages 1-28, July.
    3. Grzegorz Sierpiński & Marcin Staniek & Marcin Jacek Kłos, 2020. "Decision Making Support for Local Authorities Choosing the Method for Siting of In-City EV Charging Stations," Energies, MDPI, vol. 13(18), pages 1-28, September.
    4. LaMonaca, Sarah & Ryan, Lisa, 2022. "The state of play in electric vehicle charging services – A review of infrastructure provision, players, and policies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    5. Zixuan Wang & Qingyuan Yang & Chuwen Wang & Lanxi Wang, 2023. "Spatial Layout Analysis and Evaluation of Electric Vehicle Charging Infrastructure in Chongqing," Land, MDPI, vol. 12(4), pages 1-18, April.
    6. Bong-Gi Choi & Byeong-Chan Oh & Sungyun Choi & Sung-Yul Kim, 2020. "Selecting Locations of Electric Vehicle Charging Stations Based on the Traffic Load Eliminating Method," Energies, MDPI, vol. 13(7), pages 1-20, April.
    7. Wenhao Yu & Yujie Chen & Zhanlong Chen & Zelong Xia & Qi Zhou, 2020. "Service Area Delimitation of Fire Stations with Fire Risk Analysis: Implementation and Case Study," IJERPH, MDPI, vol. 17(6), pages 1-24, March.
    8. Zbigniew Czapla & Grzegorz Sierpiński, 2023. "Driving and Energy Profiles of Urban Bus Routes Predicted for Operation with Battery Electric Buses," Energies, MDPI, vol. 16(15), pages 1-19, July.
    9. Pablo Tamay & Esteban Inga, 2022. "Charging Infrastructure for Electric Vehicles Considering Their Integration into the Smart Grid," Sustainability, MDPI, vol. 14(14), pages 1-21, July.
    10. Marcin Jacek Kłos & Grzegorz Sierpiński, 2023. "Strategy for the Siting of Electric Vehicle Charging Stations for Parcel Delivery Service Providers," Energies, MDPI, vol. 16(6), pages 1-18, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dan K. Hsu & Johan Wiklund & Richard D. Cotton, 2017. "Success, Failure, and Entrepreneurial Reentry: An Experimental Assessment of the Veracity of Self–Efficacy and Prospect Theory," Entrepreneurship Theory and Practice, , vol. 41(1), pages 19-47, January.
    2. Barberis, Nicholas & Huang, Ming, 2009. "Preferences with frames: A new utility specification that allows for the framing of risks," Journal of Economic Dynamics and Control, Elsevier, vol. 33(8), pages 1555-1576, August.
    3. Tarnanidis, Theodore & Owusu-Frimpong, Nana & Nwankwo, Sonny & Omar, Maktoba, 2015. "Why we buy? Modeling consumer selection of referents," Journal of Retailing and Consumer Services, Elsevier, vol. 22(C), pages 24-36.
    4. Jou, Rong-Chang & Chen, Ke-Hong, 2013. "An application of cumulative prospect theory to freeway drivers’ route choice behaviours," Transportation Research Part A: Policy and Practice, Elsevier, vol. 49(C), pages 123-131.
    5. Shambhavi Tiwari & Morten Moshagen & Benjamin E. Hilbig & Ingo Zettler, 2021. "The Dark Factor of Personality and Risk-Taking," IJERPH, MDPI, vol. 18(16), pages 1-18, August.
    6. Bogliacino, Francesco & Codagnone, Cristiano, 2021. "Microfoundations, behaviour, and evolution: Evidence from experiments," Structural Change and Economic Dynamics, Elsevier, vol. 56(C), pages 372-385.
    7. Hatime Kamilcelebi, 2019. "Framing Effect and Decision-Making in the Market: A Research on Profit-Seeking and Religiousness in Turkey," International Journal of Asian Social Science, Asian Economic and Social Society, vol. 9(7), pages 417-425, July.
    8. Zamri Ahmad & Haslindar Ibrahim & Jasman Tuyon, 2017. "Institutional investor behavioral biases: syntheses of theory and evidence," Management Research Review, Emerald Group Publishing Limited, vol. 40(5), pages 578-603, May.
    9. Altman, Morris, 2014. "Insights from behavioral economics on how labor markets work," Working Paper Series 3466, Victoria University of Wellington, School of Economics and Finance.
    10. Marco Fabbri & Michael Faure, 2018. "Toward a “constitution” for behavioral policy-making," International Review of Economics, Springer;Happiness Economics and Interpersonal Relations (HEIRS), vol. 65(3), pages 241-270, September.
    11. Drakopoulos, Stavros A., 2022. "The Conceptual Resilience of the Atomistic Individual in Mainstream Economic Rationality," MPRA Paper 112944, University Library of Munich, Germany.
    12. Daniel Serra, 2019. "Neuroeconomics and modern neuroscience," CEE-M Working Papers halshs-02160907, CEE-M, Universtiy of Montpellier, CNRS, INRA, Montpellier SupAgro.
    13. Rhodes, Charles, 2012. "A Dynamic Model of Failure to Maximize Utility in the Chronic Consumer Choice to Consume Foods High in Added Sugars," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 124693, Agricultural and Applied Economics Association.
    14. Altman, Morris, 2014. "Insights from behavioral economics on how labor markets work," Working Paper Series 18843, Victoria University of Wellington, School of Economics and Finance.
    15. Leonardo Ivarola & Gustavo Marqués, 2012. "Behavioural Procedural Models – a multipurpose mechanistic account," The Journal of Philosophical Economics, Bucharest Academy of Economic Studies, The Journal of Philosophical Economics, vol. 5(2), pages 84-108, May.
    16. Schwanen, Tim & Ettema, Dick, 2009. "Coping with unreliable transportation when collecting children: Examining parents' behavior with cumulative prospect theory," Transportation Research Part A: Policy and Practice, Elsevier, vol. 43(5), pages 511-525, June.
    17. Hobman, Elizabeth V. & Frederiks, Elisha R. & Stenner, Karen & Meikle, Sarah, 2016. "Uptake and usage of cost-reflective electricity pricing: Insights from psychology and behavioural economics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 455-467.
    18. Luigino Bruni & Pier Luigi Porta, 2011. "Happiness and Experienced Utility," Chapters, in: John B. Davis & D. Wade Hands (ed.), The Elgar Companion to Recent Economic Methodology, chapter 7, Edward Elgar Publishing.
    19. Bashir Ahmad Joo & Kokab Durri, 2015. "Comprehensive Review of Literature on Behavioural Finance," Indian Journal of Commerce and Management Studies, Educational Research Multimedia & Publications,India, vol. 6(2), pages 11-19, May.
    20. Mark Schneider, 2016. "Dual Process Utility Theory: A Model of Decisions Under Risk and Over Time," Working Papers 16-23, Chapman University, Economic Science Institute.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:11:y:2018:i:9:p:2436-:d:169751. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.