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Comparison of Three Methods for Constructing Real Driving Cycles

Author

Listed:
  • José Ignacio Huertas

    (Energy and Climate Change Research Group, Tecnológico de Monterrey, Monterrey 64849, Mexico)

  • Luis Felipe Quirama

    (Grupo de Investigación en Gestión Energética, Universidad Tecnológica de Pereira, Pereira 660003, Colombia)

  • Michael Giraldo

    (Energy and Climate Change Research Group, Tecnológico de Monterrey, Monterrey 64849, Mexico)

  • Jenny Díaz

    (Department of Engineering, Universidad de Monterrey, Monterrey 66238, Mexico)

Abstract

This work compares the Micro-trips (MT), Markov chains–Monte Carlo (MCMC) and Fuel-based (FB) methods in their ability of constructing driving cycles (DC) that: (i) describe the real driving patterns of a given region and (ii) reproduce the real fuel consumption and emissions exhibited by the vehicles in that region. To that end, we selected four regions and monitored simultaneously the speed, fuel consumption and emissions of CO 2 , CO and NO x from a fleet of 15 buses of the same technology during eight months of normal operation. The driving patterns exhibited by drivers in each region were described in terms of 23 characteristic parameters (CPs) such as average speed and average positive kinetic energy. Then, for each region, we constructed their DC using the MT method and evaluated how close it describes the observed driving pattern in each region. We repeated the process using the MCMC and FB methods. Given the stochastic nature of MT and MCMC methods, the DCs obtained changed every time the methods were applied. Hence, we repeated the process of constructing the DCs up to 1000 times and reported their average relative differences and dispersion. We observed that the FB method exhibited the best performance producing DCs that describe the observed driving patterns. In all the regions considered in this study, the DCs produced by this method showed average relative differences smaller than 20% for all the CPs considered. A similar performance was observed for the case of fuel consumption and emission of pollutants.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:4:p:665-:d:207136
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    References listed on IDEAS

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    1. 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.
    2. Brady, John & O’Mahony, Margaret, 2016. "Development of a driving cycle to evaluate the energy economy of electric vehicles in urban areas," Applied Energy, Elsevier, vol. 177(C), pages 165-178.
    3. Ntziachristos, L. & Mellios, G. & Tsokolis, D. & Keller, M. & Hausberger, S. & Ligterink, N.E. & Dilara, P., 2014. "In-use vs. type-approval fuel consumption of current passenger cars in Europe," Energy Policy, Elsevier, vol. 67(C), pages 403-411.
    4. Tietge, Uwe & Mock, Peter & Franco, Vicente & Zacharof, Nikiforos, 2017. "From laboratory to road: Modeling the divergence between official and real-world fuel consumption and CO2 emission values in the German passenger car market for the years 2001–2014," Energy Policy, Elsevier, vol. 103(C), pages 212-222.
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    Cited by:

    1. Gao, Sichen & Zong, Yuhua & Ju, Fei & Wang, Qun & Huo, Weiwei & Wang, Liangmo & Wang, Tao, 2024. "Scenario-oriented adaptive ECMS using speed prediction for fuel cell vehicles in real-world driving," Energy, Elsevier, vol. 304(C).
    2. 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.
    3. Guilherme Medeiros Soares de Andrade & Fernando Wesley Cavalcanti de Araújo & Maurício Pereira Magalhães de Novaes Santos & Fabio Santana Magnani, 2020. "Standardized Comparison of 40 Local Driving Cycles: Energy and Kinematics," Energies, MDPI, vol. 13(20), pages 1-20, October.

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