IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v254y2019ics0306261919312711.html
   My bibliography  Save this article

Predictive modeling of energy consumption and greenhouse gas emissions from autonomous electric vehicle operations

Author

Listed:
  • Zhang, Cheng
  • Yang, Fan
  • Ke, Xinyou
  • Liu, Zhifeng
  • Yuan, Chris

Abstract

Autonomous electric vehicles have attracted enormous interests as an effective way to significantly improve urban transportation efficiency, reduce commute cost and the corresponding environmental burden. This work proposed a multiphysics energy model to quantify the energy consumption and greenhouse gas emissions from an autonomous electric vehicle based on vehicle dynamics and the vehicle system energy demand. A case study is conducted on a mid-size autonomous electric vehicles taxi operating in New York City based on possible driving data and scenarios. It is found that the monthly average unit energy consumption for the autonomous electric vehicle ranges from 325 to 397 Wh km−1, and the greenhouse gas emissions is 6.5% more from an autonomous electric vehicle with a driver than that without a driver. The study provides a physical approach for quantifying the energy consumption and greenhouse gas emissions from an autonomous electric vehicle, and can support the sustainable development and deployment of autonomous electric vehicle technologies in future.

Suggested Citation

  • Zhang, Cheng & Yang, Fan & Ke, Xinyou & Liu, Zhifeng & Yuan, Chris, 2019. "Predictive modeling of energy consumption and greenhouse gas emissions from autonomous electric vehicle operations," Applied Energy, Elsevier, vol. 254(C).
    • Handle: RePEc:eee:appene:v:254:y:2019:i:c:s0306261919312711
    DOI: 10.1016/j.apenergy.2019.113597
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261919312711
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2019.113597?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Howard, B. & Waite, M. & Modi, V., 2017. "Current and near-term GHG emissions factors from electricity production for New York State and New York City," Applied Energy, Elsevier, vol. 187(C), pages 255-271.
    2. Graff Zivin, Joshua S. & Kotchen, Matthew J. & Mansur, Erin T., 2014. "Spatial and temporal heterogeneity of marginal emissions: Implications for electric cars and other electricity-shifting policies," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PA), pages 248-268.
    3. H. Henry Cao & Martin D. D. Evans & Richard K. Lyons, 2017. "Inventory Information," World Scientific Book Chapters, in: Studies in Foreign Exchange Economics, chapter 9, pages 363-413, World Scientific Publishing Co. Pte. Ltd..
    4. Liu, Kai & Wang, Jiangbo & Yamamoto, Toshiyuki & Morikawa, Takayuki, 2016. "Modelling the multilevel structure and mixed effects of the factors influencing the energy consumption of electric vehicles," Applied Energy, Elsevier, vol. 183(C), pages 1351-1360.
    5. Fagnant, Daniel J. & Kockelman, Kara, 2015. "Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations," Transportation Research Part A: Policy and Practice, Elsevier, vol. 77(C), pages 167-181.
    6. Troy R. Hawkins & Bhawna Singh & Guillaume Majeau‐Bettez & Anders Hammer Strømman, 2013. "Comparative Environmental Life Cycle Assessment of Conventional and Electric Vehicles," Journal of Industrial Ecology, Yale University, vol. 17(1), pages 53-64, February.
    7. Fleetwood, J., 2017. "Public health, ethics, and autonomous vehicles," American Journal of Public Health, American Public Health Association, vol. 107(4), pages 532-537.
    8. repec:aph:ajpbhl:10.2105/ajph.2016.303628_6 is not listed on IDEAS
    9. Wadud, Zia & MacKenzie, Don & Leiby, Paul, 2016. "Help or hindrance? The travel, energy and carbon impacts of highly automated vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 86(C), pages 1-18.
    10. Fiori, Chiara & Ahn, Kyoungho & Rakha, Hesham A., 2016. "Power-based electric vehicle energy consumption model: Model development and validation," Applied Energy, Elsevier, vol. 168(C), pages 257-268.
    11. Kyle D.S. Maclean & John G. Wilson & Srini Krishnamoorthy, 2017. "Pricing of excess inventory on Groupon," International Journal of Revenue Management, Inderscience Enterprises Ltd, vol. 10(1), pages 52-74.
    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. Amirgholy, Mahyar & Gao, H. Oliver, 2023. "Optimal traffic operation for maximum energy efficiency in signal-free urban networks: A macroscopic analytical approach," Applied Energy, Elsevier, vol. 329(C).
    2. Wen, Le & Guang, Fengtao & Sharp, Basil, 2021. "Dynamics in Aotearoa New Zealand’s energy consumption between 2006/2007 and 2012/2013," Energy, Elsevier, vol. 225(C).
    3. Witsarut Achariyaviriya & Wongkot Wongsapai & Kittitat Janpoom & Tossapon Katongtung & Yuttana Mona & Nakorn Tippayawong & Pana Suttakul, 2023. "Estimating Energy Consumption of Battery Electric Vehicles Using Vehicle Sensor Data and Machine Learning Approaches," Energies, MDPI, vol. 16(17), pages 1-14, September.
    4. Yoo, Sunbin & Kumagai, Junya & Kawabata, Yuta & Keeley, Alexander & Managi, Shunsuke, 2021. "Willingness to Buy and/or Pay Disparity: Evidence from Fully Autonomous Vehicles," MPRA Paper 108882, University Library of Munich, Germany.
    5. Zhao, Li & Ke, Hanchen & Huo, Weiwei, 2023. "A frequency item mining based energy consumption prediction method for electric bus," Energy, Elsevier, vol. 263(PD).
    6. Li, Pengshun & Zhang, Yi & Zhang, Yi & Zhang, Kai & Jiang, Mengyan, 2021. "The effects of dynamic traffic conditions, route characteristics and environmental conditions on trip-based electricity consumption prediction of electric bus," Energy, Elsevier, vol. 218(C).
    7. Aleksandra Kaczyńska & Piotr Sulikowski & Jarosław Wątróbski & Wojciech Sałabun, 2023. "Enhancing Sustainable Assessment of Electric Vehicles: A Comparative Study of the TOPSIS Technique with Interval Numbers for Uncertainty Management," Energies, MDPI, vol. 16(18), pages 1-17, September.
    8. Xu, Yueru & Zheng, Yuan & Yang, Ying, 2021. "On the movement simulations of electric vehicles: A behavioral model-based approach," Applied Energy, Elsevier, vol. 283(C).
    9. Shu, Xing & Li, Guang & Shen, Jiangwei & Lei, Zhenzhen & Chen, Zheng & Liu, Yonggang, 2020. "An adaptive multi-state estimation algorithm for lithium-ion batteries incorporating temperature compensation," Energy, Elsevier, vol. 207(C).
    10. Yao, Jiwei & You, Fengqi, 2020. "Simulation-based optimization framework for economic operations of autonomous electric taxicab considering battery aging," Applied Energy, Elsevier, vol. 279(C).
    11. 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.
    12. Li, Pengshun & Zhang, Yuhang & Zhang, Yi & Zhang, Yi & Zhang, Kai, 2021. "Prediction of electric bus energy consumption with stochastic speed profile generation modelling and data driven method based on real-world big data," Applied Energy, Elsevier, vol. 298(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. Huang, Hai-chao & He, Hong-di & Peng, Zhong-ren, 2024. "Urban-scale estimation model of carbon emissions for ride-hailing electric vehicles during operational phase," Energy, Elsevier, vol. 293(C).
    2. Li, Dun & Huang, Youlin & Qian, Lixian, 2022. "Potential adoption of robotaxi service: The roles of perceived benefits to multiple stakeholders and environmental awareness," Transport Policy, Elsevier, vol. 126(C), pages 120-135.
    3. Emberger, Guenter & Pfaffenbichler, Paul, 2020. "A quantitative analysis of potential impacts of automated vehicles in Austria using a dynamic integrated land use and transport interaction model," Transport Policy, Elsevier, vol. 98(C), pages 57-67.
    4. Bray, Garrett & Cebon, David, 2022. "Operational speed strategy opportunities for autonomous trucking on highways," Transportation Research Part A: Policy and Practice, Elsevier, vol. 158(C), pages 75-94.
    5. Meyer, Jonas & Becker, Henrik & Bösch, Patrick M. & Axhausen, Kay W., 2017. "Autonomous vehicles: The next jump in accessibilities?," Research in Transportation Economics, Elsevier, vol. 62(C), pages 80-91.
    6. Hudson, John & Orviska, Marta & Hunady, Jan, 2019. "People’s attitudes to autonomous vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 121(C), pages 164-176.
    7. Maxwell Woody & Michael T. Craig & Parth T. Vaishnav & Geoffrey M. Lewis & Gregory A. Keoleian, 2022. "Optimizing future cost and emissions of electric delivery vehicles," Journal of Industrial Ecology, Yale University, vol. 26(3), pages 1108-1122, June.
    8. Pel, Bonno & Raven, Rob & van Est, Rinie, 2020. "Transitions governance with a sense of direction: synchronization challenges in the case of the dutch ‘Driverless Car’ transition," Technological Forecasting and Social Change, Elsevier, vol. 160(C).
    9. Chen, Yuche & Gonder, Jeffrey & Young, Stanley & Wood, Eric, 2019. "Quantifying autonomous vehicles national fuel consumption impacts: A data-rich approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 122(C), pages 134-145.
    10. Jiangbo Wang & Kai Liu & Toshiyuki Yamamoto, 2017. "Improving Electricity Consumption Estimation for Electric Vehicles Based on Sparse GPS Observations," Energies, MDPI, vol. 10(1), pages 1-12, January.
    11. Wadud, Zia & Mattioli, Giulio, 2021. "Fully automated vehicles: A cost-based analysis of the share of ownership and mobility services, and its socio-economic determinants," Transportation Research Part A: Policy and Practice, Elsevier, vol. 151(C), pages 228-244.
    12. Millard-Ball, Adam, 2019. "The autonomous vehicle parking problem," Transport Policy, Elsevier, vol. 75(C), pages 99-108.
    13. Schweitzer, Nicola & Hofmann, Rupert & Meinheit, Andreas, 2019. "Strategic customer foresight: From research to strategic decision-making using the example of highly automated vehicles," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 49-65.
    14. Talebian, Ahmadreza & Mishra, Sabyasachee, 2022. "Unfolding the state of the adoption of connected autonomous trucks by the commercial fleet owner industry," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
    15. McLeay, Fraser & Olya, Hossein & Liu, Hongfei & Jayawardhena, Chanaka & Dennis, Charles, 2022. "A multi-analytical approach to studying customers motivations to use innovative totally autonomous vehicles," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    16. Wadud, Zia, 2017. "Fully automated vehicles: A cost of ownership analysis to inform early adoption," Transportation Research Part A: Policy and Practice, Elsevier, vol. 101(C), pages 163-176.
    17. Du, Manqing & Zhang, Tingru & Liu, Jinting & Xu, Zhigang & Liu, Peng, 2022. "Rumors in the air? Exploring public misconceptions about automated vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 156(C), pages 237-252.
    18. Kassens-Noor, Eva & Dake, Dana & Decaminada, Travis & Kotval-K, Zeenat & Qu, Teresa & Wilson, Mark & Pentland, Brian, 2020. "Sociomobility of the 21st century: Autonomous vehicles, planning, and the future city," Transport Policy, Elsevier, vol. 99(C), pages 329-335.
    19. Shatanawi, Mohamad & Alatawneh, Anas & Mészáros, Ferenc, 2022. "Implications of static and dynamic road pricing strategies in the era of autonomous and shared autonomous vehicles using simulation-based dynamic traffic assignment: The case of Budapest," Research in Transportation Economics, Elsevier, vol. 95(C).
    20. Skeete, Jean-Paul, 2018. "Level 5 autonomy: The new face of disruption in road transport," Technological Forecasting and Social Change, Elsevier, vol. 134(C), pages 22-34.

    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:eee:appene:v:254:y:2019:i:c:s0306261919312711. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.