IDEAS home Printed from https://ideas.repec.org/p/cdl/itsdav/qt6vk0h1mj.html
   My bibliography  Save this paper

Charging Forward: Deploying Electric Vehicle Infrastructure for Uber and Lyft in California

Author

Listed:
  • Jenn, Alan

Abstract

With recent policies such as the Clean Miles Standard in California and Lyft’s announcement to reach 100% electric vehicles (EVs) by 2030, the electrification of vehicles on ride-hailing platforms is inevitable. The impacts of this transition are not well-studied. This work attempts to examine the infrastructure deployment necessary to meet demand from electric vehicles being driven on Uber and Lyft platforms using empirical trip data from the two services. The Widespread Infrastructure for Ride-hail EV Deployment (WIRED) model was developed to examine a set of case studies for charger installation in San Diego, Los Angeles, and the San Francisco Bay Area. A set of sensitivity scenarios was also conducted to measure the tradeoff between explicit costs of infrastructure versus weighting factors for valuing the time for drivers to travel to a charger (from where they are providing rides) and valuing the rate of charging (to minimize the amount of time that drivers have to wait to charge their vehicle). There are several notable findings from the study: 1) DC fast charging infrastructure is the dominant charger type necessary to meet ride-hailing demand, 2) shifting to overnight charging behavior that places less emphasis on daytime public charging can significantly reduce costs, and 3) the necessary ratio of chargers is approximately 10 times higher for EVs in Uber and Lyft compared to chargers for the general EV owning public.

Suggested Citation

  • Jenn, Alan, 2021. "Charging Forward: Deploying Electric Vehicle Infrastructure for Uber and Lyft in California," Institute of Transportation Studies, Working Paper Series qt6vk0h1mj, Institute of Transportation Studies, UC Davis.
    • Handle: RePEc:cdl:itsdav:qt6vk0h1mj
    as

    Download full text from publisher

    File URL: https://www.escholarship.org/uc/item/6vk0h1mj.pdf;origin=repeccitec
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ferro, G. & Minciardi, R. & Robba, M., 2020. "A user equilibrium model for electric vehicles: Joint traffic and energy demand assignment," Energy, Elsevier, vol. 198(C).
    2. Globisch, Joachim & Plötz, Patrick & Dütschke, Elisabeth & Wietschel, Martin, 2018. "Consumer evaluation of public charging infrastructure for electric vehicles," Working Papers "Sustainability and Innovation" S13/2018, Fraunhofer Institute for Systems and Innovation Research (ISI).
    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. Lai, Zhijie & Li, Sen, 2024. "Towards a multimodal charging network: Joint planning of charging stations and battery swapping stations for electrified ride-hailing fleets," Transportation Research Part B: Methodological, Elsevier, vol. 183(C).
    2. Rye, Jamie & Sintov, Nicole D., 2024. "Predictors of electric vehicle adoption intent in rideshare drivers relative to commuters," Transportation Research Part A: Policy and Practice, Elsevier, vol. 179(C).

    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. Zhang, Tianren & Huang, Yuping & Liao, Hui & Liang, Yu, 2023. "A hybrid electric vehicle load classification and forecasting approach based on GBDT algorithm and temporal convolutional network," Applied Energy, Elsevier, vol. 351(C).
    2. Albert Hiesl & Jasmine Ramsebner & Reinhard Haas, 2021. "Modelling Stochastic Electricity Demand of Electric Vehicles Based on Traffic Surveys—The Case of Austria," Energies, MDPI, vol. 14(6), pages 1-19, March.
    3. Zhang, Yuanjian & Huang, Yanjun & Chen, Haibo & Na, Xiaoxiang & Chen, Zheng & Liu, Yonggang, 2021. "Driving behavior oriented torque demand regulation for electric vehicles with single pedal driving," Energy, Elsevier, vol. 228(C).
    4. Alan Jenn, 2024. "Charging forward: deploying EV infrastructure for Uber and Lyft in California," Transportation, Springer, vol. 51(5), pages 1663-1678, October.
    5. Virginia Casella & Daniel Fernandez Valderrama & Giulio Ferro & Riccardo Minciardi & Massimo Paolucci & Luca Parodi & Michela Robba, 2022. "Towards the Integration of Sustainable Transportation and Smart Grids: A Review on Electric Vehicles’ Management," Energies, MDPI, vol. 15(11), pages 1-23, May.
    6. Wang, Yanxia & Gan, Shaojun & Li, Kang & Chen, Yanyan, 2022. "Planning for low-carbon energy-transportation system at metropolitan scale: A case study of Beijing, China," Energy, Elsevier, vol. 246(C).
    7. Yuan, Quan & Ye, Yujian & Tang, Yi & Liu, Yuanchang & Strbac, Goran, 2022. "A novel deep-learning based surrogate modeling of stochastic electric vehicle traffic user equilibrium in low-carbon electricity–transportation nexus," Applied Energy, Elsevier, vol. 315(C).
    8. Rahman, Md Mustafizur & Gemechu, Eskinder & Oni, Abayomi Olufemi & Kumar, Amit, 2023. "The development of a techno-economic model for assessment of cost of energy storage for vehicle-to-grid applications in a cold climate," Energy, Elsevier, vol. 262(PA).

    More about this item

    Keywords

    Engineering; Social and Behavioral Sciences; Case studies; Costs; Electric vehicle charging; Electric vehicles; Forecasting; Implementation; Infrastructure; Ridesourcing; Travel demand; Travel time;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:cdl:itsdav:qt6vk0h1mj. 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: Lisa Schiff (email available below). General contact details of provider: https://edirc.repec.org/data/itucdus.html .

    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.