IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i2p974-d482897.html
   My bibliography  Save this article

In Use Determination of Aerodynamic and Rolling Resistances of Heavy-Duty Vehicles

Author

Listed:
  • Dimitrios Komnos

    (FINCONS Group, 20871 Vimercate, Italy
    Mechanical Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece)

  • Stijn Broekaert

    (European Commission Joint Research, 21027 Ispra, Italy)

  • Theodoros Grigoratos

    (European Commission Joint Research, 21027 Ispra, Italy)

  • Leonidas Ntziachristos

    (Mechanical Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece)

  • Georgios Fontaras

    (European Commission Joint Research, 21027 Ispra, Italy)

Abstract

A vehicle’s air drag coefficient (Cd) and rolling resistance coefficient ( RRC ) have a significant impact on its fuel consumption. Consequently, these properties are required as input for the certification of the vehicle’s fuel consumption and Carbon Dioxide emissions, regardless of whether the certification is done via simulation or chassis dyno testing. They can be determined through dedicated measurements, such as a drum test for the tire’s rolling resistance coefficient and constant speed test (EU) or coast down test (US) for the body’s air Cd. In this paper, a methodology that allows determining the vehicle’s Cd · A (the product of Cd and frontal area of the vehicle) from on-road tests is presented. The possibility to measure these properties during an on-road test, without the need for a test track, enables third parties to verify the certified vehicle properties in order to preselect vehicle for further regulatory testing. On-road tests were performed with three heavy-duty vehicles, two lorries, and a coach, over different routes. Vehicles were instrumented with wheel torque sensors, wheel speed sensors, a GPS device, and a fuel flow sensor. Cd · A of each vehicle is determined from the test data with the proposed methodology and validated against their certified value. The methodology presents satisfactory repeatability with the error ranging from −21 to 5% and averaging approximately −6.8%. A sensitivity analysis demonstrates the possibility of using the tire energy efficiency label instead of the measured RRC to determine the air drag coefficient. Finally, on-road tests were simulated in the Vehicle Energy Consumption Calculation Tool with the obtained parameters, and the average difference in fuel consumption was found to be 2%.

Suggested Citation

  • Dimitrios Komnos & Stijn Broekaert & Theodoros Grigoratos & Leonidas Ntziachristos & Georgios Fontaras, 2021. "In Use Determination of Aerodynamic and Rolling Resistances of Heavy-Duty Vehicles," Sustainability, MDPI, vol. 13(2), pages 1-22, January.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:2:p:974-:d:482897
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/2/974/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/2/974/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Charyung Kim & Hyunwoo Lee & Yongsung Park & Cha-Lee Myung & Simsoo Park, 2016. "Study on the Criteria for the Determination of the Road Load Correlation for Automobiles and an Analysis of Key Factors," Energies, MDPI, vol. 9(8), pages 1-17, July.
    2. Mullen, Katharine M., 2014. "Continuous Global Optimization in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 60(i06).
    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. Daniel Chindamo & Marco Gadola & Emanuele Bonera & Paolo Magri, 2021. "Sensitivity of Racing Tire Sliding Energy to Major Setup Changes: An Estimate Based on Standard Sensors," Energies, MDPI, vol. 14(16), pages 1-14, August.
    2. Barouch Giechaskiel & Dimitrios Komnos & Georgios Fontaras, 2021. "Impacts of Extreme Ambient Temperatures and Road Gradient on Energy Consumption and CO 2 Emissions of a Euro 6d-Temp Gasoline Vehicle," Energies, MDPI, vol. 14(19), pages 1-20, September.
    3. Zacharof, Nikiforos & Özener, Orkun & Broekaert, Stijn & Özkan, Muammer & Samaras, Zissis & Fontaras, Georgios, 2023. "The impact of bus passenger occupancy, heating ventilation and air conditioning systems on energy consumption and CO2 emissions," Energy, Elsevier, vol. 272(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. Laha, A. K. & Rathi, Poonam, 2017. "Are the temperature of Indian cities Increasing?: Some Insights Using Change Point Analysis with Functional Data," IIMA Working Papers WP 2017-08-03, Indian Institute of Management Ahmedabad, Research and Publication Department.
    2. Sameh Abdulah & Yuxiao Li & Jian Cao & Hatem Ltaief & David E. Keyes & Marc G. Genton & Ying Sun, 2023. "Large‐scale environmental data science with ExaGeoStatR," Environmetrics, John Wiley & Sons, Ltd., vol. 34(1), February.
    3. Christian Engström & Per Öberg & Georgios Fontaras & Barouch Giechaskiel, 2022. "Considerations for Achieving Equivalence between Hub- and Roller-Type Dynamometers for Vehicle Exhaust Emissions," Energies, MDPI, vol. 15(20), pages 1-23, October.
    4. Laha, A. K. & Rathi, Poonam, 2017. "New Approaches to Prediction using Functional Data Analysis," IIMA Working Papers WP 2017-08-02, Indian Institute of Management Ahmedabad, Research and Publication Department.
    5. Won, Joong-Ho & Wu, Xiao & Lee, Sang Han & Lu, Ying, 2017. "Cross-sectional design with a short-term follow-up for prognostic imaging biomarkers," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 154-176.
    6. Xiao-Dong Zhou & Yun-Juan Wang & Rong-Xian Yue, 2021. "Optimal designs for discrete-time survival models with random effects," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(2), pages 300-332, April.
    7. Siriorn Pitanuwat & Hirofumi Aoki & Satoru Iizuka & Takayuki Morikawa, 2020. "Development of Hybrid-Vehicle Energy-Consumption Model for Transportation Applications—Part I: Driving-Power Equation Development and Coefficient Calibration," Energies, MDPI, vol. 13(2), pages 1-20, January.
    8. Becker, Martin & Klößner, Stefan, 2018. "Fast and reliable computation of generalized synthetic controls," Econometrics and Statistics, Elsevier, vol. 5(C), pages 1-19.
    9. Kangjin Kim & Wonyong Chung & Myungsoo Kim & Charyung Kim & Cha-Lee Myung & Simsoo Park, 2020. "Inspection of PN, CO 2 , and Regulated Gaseous Emissions Characteristics from a GDI Vehicle under Various Real-World Vehicle Test Modes," Energies, MDPI, vol. 13(10), pages 1-17, May.
    10. Bergmeir, Christoph & Molina, Daniel & Benítez, José M., 2016. "Memetic Algorithms with Local Search Chains in R: The Rmalschains Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 75(i04).
    11. Kevin Burke & Frank Eriksson & C. B. Pipper, 2020. "Semiparametric multiparameter regression survival modeling," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(2), pages 555-571, June.

    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:13:y:2021:i:2:p:974-:d:482897. 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.