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Driving Cycles Based on Fuel Consumption

Author

Listed:
  • José I. Huertas

    (Energy and Climate Change Research Group, Tecnológico de Monterrey, Eugenio Garza Sada 2501, Monterrey 64849, Nuevo León, Mexico)

  • Michael Giraldo

    (Energy and Climate Change Research Group, Tecnológico de Monterrey, Eugenio Garza Sada 2501, Monterrey 64849, Nuevo León, Mexico)

  • Luis F. Quirama

    (Grupo de Investigación en Gestión Energética, Universidad Tecnológica de Pereira, Cl. 27 #10-02, Pereira 660003, Risaralda, Colombia)

  • Jenny Díaz

    (Universidad de Monterrey, Av. Morones Prieto 4500 Pte., San Pedro Garza García 66238, Nuevo León, Mexico)

Abstract

Type-approval driving cycles currently available, such as the Federal Test Procedure (FTP) and the Worldwide harmonized Light vehicles Test Cycle (WLTC), cannot be used to estimate real fuel consumption nor emissions from vehicles in a region of interest because they do not describe its local driving pattern. We defined a driving cycle ( DC ) as the time series of speeds that when reproduced by a vehicle, the resulting fuel consumption and emissions are similar to the average fuel consumption and emissions of all vehicles of the same technology driven in that region. We also declared that the driving pattern can be described by a set of characteristic parameters (CPs) such as mean speed, positive kinetic energy and percentage of idling time. Then, we proposed a method to construct those local DC that use fuel consumption as criterion. We hypothesized that by using this criterion, the resulting DC describes, implicitly, the driving pattern in that region. Aiming to demonstrate this hypothesis, we monitored the location, speed, altitude, and fuel consumption of a fleet of 15 vehicles of similar technology, during 8 months of normal operation, in four regions with diverse topography, traveling on roads with diverse level of service. In every region, we considered 1000 instances of samples made of m trips, where m varied from 4 to 40. We found that the CPs of the local driving cycle constructed using the fuel-based method exhibit small relative differences (<15%) with respect to the CPs that describe the driving patterns in that region. This result demonstrates the hypothesis that using the fuel based method the resulting local DC exhibits CPs similar to the CPs that describe the driving pattern of the region under study.

Suggested Citation

  • José I. Huertas & Michael Giraldo & Luis F. Quirama & Jenny Díaz, 2018. "Driving Cycles Based on Fuel Consumption," Energies, MDPI, vol. 11(11), pages 1-13, November.
  • Handle: RePEc:gam:jeners:v:11:y:2018:i:11:p:3064-:d:181222
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    References listed on IDEAS

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    Cited by:

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    2. Zvonimir Dabčević & Branimir Škugor & Jakov Topić & Joško Deur, 2022. "Synthesis of Driving Cycles Based on Low-Sampling-Rate Vehicle-Tracking Data and Markov Chain Methodology," Energies, MDPI, vol. 15(11), pages 1-21, June.
    3. Zhuowu Zhang & Emrah Demir & Robert Mason & Carla Cairano-Gilfedder, 2023. "Understanding freight drivers' behavior and the impact on vehicles' fuel consumption and CO2e emissions," Operational Research, Springer, vol. 23(4), pages 1-35, December.
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    5. Ghaffarpasand, Omid & Pope, Francis D., 2024. "Telematics data for geospatial and temporal mapping of urban mobility: New insights into travel characteristics and vehicle specific power," Journal of Transport Geography, Elsevier, vol. 115(C).
    6. Jakov Topić & Branimir Škugor & Joško Deur, 2021. "Synthesis and Feature Selection-Supported Validation of Multidimensional Driving Cycles," Sustainability, MDPI, vol. 13(9), pages 1-21, April.
    7. Li Zhao & Kun Li & Wu Zhao & Han-Chen Ke & Zhen Wang, 2022. "A Sticky Sampling and Markov State Transition Matrix Based Driving Cycle Construction Method for EV," Energies, MDPI, vol. 15(3), pages 1-19, January.
    8. José Ignacio Huertas & Luis Felipe Quirama & Michael Giraldo & Jenny Díaz, 2019. "Comparison of Three Methods for Constructing Real Driving Cycles," Energies, MDPI, vol. 12(4), pages 1-15, February.
    9. Ross Milligan & Saioa Etxebarria & Tariq Muneer & Eulalia Jadraque Gago, 2019. "Driven Performance of Electric Vehicles in Edinburgh and Its Environs," Energies, MDPI, vol. 12(16), pages 1-22, August.
    10. Karol Tucki & Andrzej Wasiak & Olga Orynycz & Remigiusz Mruk, 2020. "Computer Simulation as a Tool for Managing the Technical Development of Methods for Diagnosing the Technical Condition of a Vehicle," Energies, MDPI, vol. 13(11), pages 1-24, June.
    11. Tianming Gao & Vasilii Erokhin & Aleksandr Arskiy, 2019. "Dynamic Optimization of Fuel and Logistics Costs as a Tool in Pursuing Economic Sustainability of a Farm," Sustainability, MDPI, vol. 11(19), pages 1-16, October.

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