AERODYNAMIC AND ROLLING RESISTANCES OF HEAVY DUTY VEHICLE. SIMULATION OF ENERGY CONSUMPTION
Łukasz GRABOWSKI
l.grabowski@pollub.plLublin University of Technology (Poland)
https://orcid.org/0000-0003-3069-8860
Arkadiusz DROZD
(Poland)
Mateusz KARABELA
(Poland)
https://orcid.org/0009-0003-0215-6252
Wojciech KARPIUK
Poznan University of Technology (Poland)
https://orcid.org/0000-0002-3690-4605
Abstract
The main objective of the work was to develop a comprehensive model of energy consumption simulation of heavy duty vehicles using the VECTO simulation tool. The research issue was the impact of aerodynamic drag and rolling resistance on fuel consumption and emissions under various driving conditions described in four driving cycles: Urban Delivery, Regional Delivery, Urban, and Suburban. Each cycle differed in driving time, distance and average speed to represent different operational scenarios. The methodology involved defining vehicle parameters such as weight, aerodynamic coefficients and tyre rolling resistance. The main findings show that the impact of both aerodynamic drag and rolling resistance on fuel consumption can be efficiently modelled. It has been proven that the proposed modifications to aerodynamic drag and rolling resistance can reduce fuel consumption by more than 8%. The lowest fuel consumption was achieved in the Regional Delivery cycle, while the Urban cycle had the highest fuel consumption due to frequent vehicle stops. The results show that optimization of vehicle design and its performance can significantly improve energy efficiency and reduce emissions. A computational modelling tool such as VECTO can contribute to sustainable transport solutions and improve the efficiency of heavy duty vehicle.
Keywords:
vehicle, energy, efficiency, VECTOReferences
Bajerlein, M., Karpiuk, W., Kurc, B., Smolec, R., & Waligórski, M. (2024). Refining combustion dynamics: Dissolved hydrogen in diesel fuel within turbulent-flow environments. Energies, 17(11), 2446. https://doi.org/10.3390/en17112446
Google Scholar
Basma, H., Rodríguez, F., Hildermeier, J., Jahn, A., & Project, R. A. (2022). Electrifying last-mile delivery: A total cost of ownership comparison of battery-electric and diesel lorries in Europe. The International Council on Clean Transportation. https://theicct.org/wp-content/uploads/2022/06/tco-battery-diesel-delivery-trucks-jun2022.pdf
Google Scholar
Bayındırlı, C., Akansu, Y. E., & Salman, M. S. (2016). The determination of aerodynamic drag coefficient of truck and trailer model by wind tunnel tests. International Journal of Automotive Engineering and Technologies, 5(2), 53-60. https://doi.org/10.18245/ijaet.11754
Google Scholar
Broekaert, S., Grigoratos, T., Savvidis, D., & Fontaras, G. (2021). Assessment of waste heat recovery for heavy-duty vehicles during on-road operation. Applied Thermal Engineering, 191, 116891. https://doi.org/10.1016/J.APPLTHERMALENG.2021.116891
Google Scholar
Colucci, G., Lerede, D., Nicoli, M., & Savoldi, L. (2023). A dynamic accounting method for CO2 emissions to assess the penetration of low-carbon fuels: application to the TEMOA-Italy energy system optimisation model. Applied Energy, 352, 121951. https://doi.org/10.1016/J.APENERGY.2023.121951
Google Scholar
Curry, T., Liberman, I., Hoffman-Andrews, L., & Lowell, D. (2021, October 15). Reducing aerodynamic drag and rolling resistance from heavy-duty trucks. International Council on Clean Transportation. https://theicct.org/publication/reducing-aerodynamic-drag-and-rolling-resistance-from-heavy-duty-truck
Google Scholar
Czyż, Z., Karpiński, P., Gęca, M., & Ulibarrena Diaz, J. (2018a). The air flow influence on the drag force of a sports car. Advances in Science and Technology Research Journal, 12(2), 121-127. https://doi.org/10.12913/22998624/86213
Google Scholar
Czyż, Z., Karpiński, P., & Sevdim, T. (2018b). Numerical analysis of the drag coefficient of a motorcycle helmet. Applied Computer Science, 14(1), 16-26. https://doi.org/10.23743/acs-2018-02
Google Scholar
De Robbio, R., Cameretti, M. C., & Mancaruso, E. (2022). Investigation by modelling of a plug-in hybrid electric commercial vehicle with diesel engine on WLTC. Fuel, 317, 123519. https://doi.org/10.1016/J.FUEL.2022.123519
Google Scholar
Di Pierro, G., Bitsanis, E., Tansini, A., Bonato, C., Martini, G., & Fontaras, G. (2024). Fuel cell electric vehicle characterisation under laboratory and in-use operation. Energy Reports, 11, 611-623. https://doi.org/10.1016/J.EGYR.2023.12.013
Google Scholar
Eswaranathan, K., Sivakumar, T., De Silva, M. M., & Kumarage, A. S. (2024). Modelling the factors influencing carbon efficiency of consumer choice to promote energy-efficient vehicles. Transport Economics and Management, 2, 130-142 https://doi.org/10.1016/j.team.2024.05.002
Google Scholar
European Commission: Joint Research Centre, Zacharof, N., Broekaert, S., & Fontaras, G. (2021). Future CO2 reducing technologies in VECTO : VECTO technology coverage and market uptake. Publications Office. https://data.europa.eu/doi/10.2760/985739
Google Scholar
Fontaras, G., Rexeis, M., Dilara, P., Hausberger, S., & Anagnostopoulos, K. (2013). The development of a simulation tool for monitoring heavy-duty vehicle CO2 emissions and fuel consumption in Europe. SAE Technical Papers, 6, 2013-24-0150. https://doi.org/10.4271/2013-24-0150
Google Scholar
Grabowski, L. (2021). Modelling research of city bus fuel consumption for different driving cycles. Journal of Physics: Conference Series, 2130, 012001. https://doi.org/10.1088/1742-6596/2130/1/012001
Google Scholar
Khosravi, M., Mosaddeghi, F., Oveisi, M., & Khodayari, A. (2015). Aerodynamic drag reduction of heavy vehicles using append devices by CFD analysis. Journal of Central South University, 22, 4645-4652. https://doi.org/10.1007/s11771-015-3015-7
Google Scholar
Krause, J., Arcidiacono, V., Maineri, L., Broekaert, S., & Fontaras, G. (2023). Calculating heavy-duty vehicle CO2 emission reduction costs for Green Deal scenarios: Extension of the DIONE model. Transportation Research Procedia, 72, 2597-2603. https://doi.org/10.1016/J.TRPRO.2023.11.788
Google Scholar
Na, X., & Cebon, D. (2022). Quantifying fuel-saving benefit of low-rolling-resistance tyres from heavy goods vehicle in-service operations. Transportation Research Part D: Transport and Environment, 113, 103501. https://doi.org/10.1016/J.TRD.2022.103501
Google Scholar
Qiu, Y., Song, S., & Calstart, R. M. (2022, May). Zero-emission lorry real-world performance in us and europe and implications for china. https://globaldrivetozero.org/publication/zero-emission-truck-real-world-performance-in-us-and-europe-and-implications-for-china/
Google Scholar
Seo, J., & Park, S. (2023). Developing an official programme to calculate heavy-duty vehicles CO2 emissions in Korea. Transportation Research Part D: Transport and Environment, 120, 103774. https://doi.org/10.1016/J.TRD.2023.103774
Google Scholar
Tong, F., Wolfson, D., Jenn, A., Scown, C. D., & Auffhammer, M. (2021). Energy consumption and charging load profiles from long-haul lorry electrification in the United States. Environmental Research Infrastructure and Sustainability, 1, 025007. https://doi.org/10.1088/2634-4505/ac186a
Google Scholar
Wahono, B., Santos, W. B., Nur, A., & Amin. (2015). Analysis of range extender electric vehicle performance using vehicle simulator. Energy Procedure, 68, 409-418. https://doi.org/10.1016/j.egypro.2015.03.272
Google Scholar
Zhang, C., Shen, K., Yang, F., & Yuan, C. (2019). Multiphysics modelling of energy intensity and energy efficiency of electric vehicle operation. CIRP procedure, 80, 322-327. https://doi.org/10.1016/j.procir.2019.01.058
Google Scholar
Authors
Łukasz GRABOWSKIl.grabowski@pollub.pl
Lublin University of Technology Poland
https://orcid.org/0000-0003-3069-8860
Authors
Arkadiusz DROZDPoland
Authors
Wojciech KARPIUKPoznan University of Technology Poland
https://orcid.org/0000-0002-3690-4605
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