IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i15p6260-d394114.html
   My bibliography  Save this article

Study on a Prediction Model of Superhighway Fuel Consumption Based on the Test of Easy Car Platform

Author

Listed:
  • Yong-Ming He

    (School of Transportation, Northeast Forestry University, 26 Hexing Road, Harbin 150040, China
    School of Civil Engineering, University of Wisconsin-Madison, Madison, WI 57305, USA)

  • Jia Kang

    (School of Transportation, Northeast Forestry University, 26 Hexing Road, Harbin 150040, China
    Transport Research Centre, Northeast Forestry University, 26 Hexing Road, Harbin 150040, China)

  • Yu-Long Pei

    (School of Transportation, Northeast Forestry University, 26 Hexing Road, Harbin 150040, China
    Transport Research Centre, Northeast Forestry University, 26 Hexing Road, Harbin 150040, China)

  • Bin Ran

    (School of Civil Engineering, University of Wisconsin-Madison, Madison, WI 57305, USA
    Southeast University-University of Wisconsin Intelligent Network Transportation Joint Research Institute, 2312 Engineering Hall, 1415 Engineering Drive, Madison, WI 53706, USA)

  • Yu-Ting Song

    (School of Transportation, Northeast Forestry University, 26 Hexing Road, Harbin 150040, China
    Transport Research Centre, Northeast Forestry University, 26 Hexing Road, Harbin 150040, China)

Abstract

To explore the relationship between fuel consumption and speed for a vehicle on a superhighway with a design speed exceeding 120 km/h, the fuel consumption data provided by the Test of Easy Car platform are used to fit the fuel consumption of different models. The fitting results show that the fitting degree of fuel consumption by a cubic curve is the highest, and the correlation coefficient is above 0.95. A fuel consumption cubic curve model of different vehicle types is established by using the fitting parameters to predict the fuel consumption when a vehicle is running at a speed of 130 km/h–180 km/h. The prediction results show that the average fuel consumption of compact vehicles is the lowest when a vehicle is running on a superhighway at speeds of 130 km/h–180 km/h, with values of 8.95 L/100 km–16.26 L/100 km; the average fuel consumption of sport utility vehicles (SUVs) is the highest, with values of 12.65 L/100 km–22.70 L/100 km. The prediction results can be used to estimate the cost of using a superhighway and provide a basis for estimating the feasibility of superhighways.

Suggested Citation

  • Yong-Ming He & Jia Kang & Yu-Long Pei & Bin Ran & Yu-Ting Song, 2020. "Study on a Prediction Model of Superhighway Fuel Consumption Based on the Test of Easy Car Platform," Sustainability, MDPI, vol. 12(15), pages 1-19, August.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:15:p:6260-:d:394114
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/15/6260/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/15/6260/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yu-Long Pei & Yong-Ming He & Bin Ran & Jia Kang & Yu-Ting Song, 2020. "Horizontal Alignment Security Design Theory and Application of Superhighways," Sustainability, MDPI, vol. 12(6), pages 1-14, March.
    2. repec:aph:ajpbhl:10.2105/ajph.2017.303880_5 is not listed on IDEAS
    3. Redelmeier, D.A. & Bhatti, J.A., 2017. "Princess Diana and reduced traffic deaths in France and the United States," American Journal of Public Health, American Public Health Association, vol. 107(8), pages 1246-1248.
    4. Ben Dror, Maya & Qin, Lanzhi & An, Feng, 2019. "The gap between certified and real-world passenger vehicle fuel consumption in China measured using a mobile phone application data," Energy Policy, Elsevier, vol. 128(C), pages 8-16.
    Full references (including those not matched with items on IDEAS)

    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. Yu, Rujie & Ren, Huanhuan & Liu, Yong & Yu, Biying, 2021. "Gap between on-road and official fuel efficiency of passenger vehicles in China," Energy Policy, Elsevier, vol. 152(C).
    2. Wu, Tian & Han, Xiao & Zheng, M. Mocarlo & Ou, Xunmin & Sun, Hongbo & Zhang, Xiong, 2020. "Impact factors of the real-world fuel consumption rate of light duty vehicles in China," Energy, Elsevier, vol. 190(C).
    3. He, Yongming & Kang, Jia & Pei, Yulong & Ran, Bin & Song, Yuting, 2021. "Research on influencing factors of fuel consumption on superhighway based on DEMATEL-ISM model," Energy Policy, Elsevier, vol. 158(C).
    4. Su, Sheng & Ge, Yang & Hou, Pan & Wang, Xin & Wang, Yachao & Lyu, Tao & Luo, Wanyou & Lai, Yitu & Ge, Yunshan & Lyu, Liqun, 2021. "China VI heavy-duty moving average window (MAW) method: Quantitative analysis of the problem, causes, and impacts based on the real driving data," Energy, Elsevier, vol. 225(C).
    5. Fan, Pengfei & Yin, Hang & Lu, Hongyu & Wu, Yizheng & Zhai, Zhiqiang & Yu, Lei & Song, Guohua, 2023. "Which factor contributes more to the fuel consumption gap between in-laboratory vs. real-world driving conditions? An independent component analysis," Energy Policy, Elsevier, vol. 182(C).
    6. Jie Yan & Sheng Zeng & Bijiang Tian & Yuanwen Cao & Wenchen Yang & Feng Zhu, 2023. "Relationship between Highway Geometric Characteristics and Accident Risk: A Multilayer Perceptron Model (MLP) Approach," Sustainability, MDPI, vol. 15(3), pages 1-15, January.
    7. Cui, Yuepeng & Zou, Fumin & Xu, Hao & Chen, Zhihui & Gong, Kuangmin, 2022. "A novel optimization-based method to develop representative driving cycle in various driving conditions," Energy, Elsevier, vol. 247(C).
    8. Yushan Yang & Nuoya Gong & Keying Xie & Qingfei Liu, 2022. "Predicting Gasoline Vehicle Fuel Consumption in Energy and Environmental Impact Based on Machine Learning and Multidimensional Big Data," Energies, MDPI, vol. 15(5), pages 1-17, February.
    9. Harvey, L.D. Danny, 2020. "Rethinking electric vehicle subsidies, rediscovering energy efficiency," Energy Policy, Elsevier, vol. 146(C).

    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:jsusta:v:12:y:2020:i:15:p:6260-:d:394114. 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.