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Standardized Comparison of 40 Local Driving Cycles: Energy and Kinematics

Author

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  • Guilherme Medeiros Soares de Andrade

    (Center of Technologies and Geosciences, Department of Mechanical Engineering, Federal University of Pernambuco, Recife 50740-550, Brazil)

  • Fernando Wesley Cavalcanti de Araújo

    (Center of Technologies and Geosciences, Department of Mechanical Engineering, Federal University of Pernambuco, Recife 50740-550, Brazil)

  • Maurício Pereira Magalhães de Novaes Santos

    (Center of Technologies and Geosciences, Department of Mechanical Engineering, Federal University of Pernambuco, Recife 50740-550, Brazil)

  • Fabio Santana Magnani

    (Center of Technologies and Geosciences, Department of Mechanical Engineering, Federal University of Pernambuco, Recife 50740-550, Brazil)

Abstract

Local driving cycles (LDCs) capture local traffic characteristics, while standard driving cycles (SDCs) compare vehicles in distinct regions. There is a plethora of LDCs, raising the question as to how distinct they are. To quantify it, we first organized a collection of 77 LDCs. From the speed—time images, it was possible to extract numerical vectors of 40 cycles in a standardized way. Comparing the LDCs developed for cars, we found that their parameters fluctuate significantly: the average speed varies from 14.7 to 44.7 km/h, and the fuel economy varies from 10.8 to 20.5 km/L. Comparing the LDCs with FTP-75 cycle, the difference in speed is 7 km/h, and in fuel economy is 1.5 km/L. For WLTC, the difference is 19.4 km/h and 3 km/L, respectively. Thus, given the deviations found between the analyzed LDCs, and between LDCs and SDCs, the numerical results reinforce the relevance of using LDCs for each region.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:20:p:5434-:d:430727
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    References listed on IDEAS

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    1. Michael Ben-Chaim & Efraim Shmerling & Alon Kuperman, 2013. "Analytic Modeling of Vehicle Fuel Consumption," Energies, MDPI, vol. 6(1), pages 1-11, January.
    2. Claudio Cubito & Federico Millo & Giulio Boccardo & Giuseppe Di Pierro & Biagio Ciuffo & Georgios Fontaras & Simone Serra & Marcos Otura Garcia & Germana Trentadue, 2017. "Impact of Different Driving Cycles and Operating Conditions on CO 2 Emissions and Energy Management Strategies of a Euro-6 Hybrid Electric Vehicle," Energies, MDPI, vol. 10(10), pages 1-18, October.
    3. 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.
    4. Gong, Huiming & Zou, Yuan & Yang, Qingkai & Fan, Jie & Sun, Fengchun & Goehlich, Dietmar, 2018. "Generation of a driving cycle for battery electric vehicles：A case study of Beijing," Energy, Elsevier, vol. 150(C), pages 901-912.
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    1. 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.
    2. Emilia M. Szumska & Rafał S. Jurecki, 2021. "Parameters Influencing on Electric Vehicle Range," Energies, MDPI, vol. 14(16), pages 1-23, August.

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