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Using Large Driving Record Samples and a Stochastic Approach for Real-World Driving Cycle Construction: Winnipeg Driving Cycle

Author

Listed:
  • Ali Ashtari

    (Invenia Technical Computing, Winnipeg, Manitoba R3T 6A8, Canada)

  • Eric Bibeau

    (Department of Mechanical Engineering, University of Manitoba, Winnipeg, Manitoba R3T 5V6, Canada)

  • Soheil Shahidinejad

    (Department of Mechanical Engineering, University of Manitoba, Winnipeg, Manitoba R3T 5V6, Canada)

Abstract

The challenges in the development of plug-in electric vehicle (PEV) powertrains are efficient energy management and optimum energy storage, for which the role of driving cycles that represent driver behaviour is instrumental. Discrepancies between standard driving cycles and real driving behaviour stem from insufficient data collection, inaccurate cycle construction methodology, and variations because of geography. In this study, we tackle the first issue by using the collected data from real-world driving of a fleet of 76 cars for more than one year in the city of Winnipeg (Canada), representing more than 44 million data points. The second issue is addressed by a proposed novel stochastic driving cycle construction method. The third issue limits the results to mainly Winnipeg and cities that have similar features, but the methodology can be used anywhere. The methodology develops the driving cycle using snippets extracted from recorded time-stamped speed of the vehicles from the collected database. The proposed Winnipeg Driving Cycle (WPG01) characteristics are compared to eight existing standard driving cycles and are more able to represent aggressive driving, which is critical in PEV design. An attempt is made to isolate how many differences could be attributed to the sample size and the methodology. The proposed construction methodology is flexible to be optimized for any selection of driving parameters and thus can be a recommended approach to develop driving cycles for any drive train topology, including internal combustion engine vehicles, hybrid vehicles, plug-in hybrid, and battery electric vehicles. Characterization of vehicle parking durations and types of parking (home, work, shopping), critical for duty cycles for PEV powertrains, are reported elsewhere. Here, the focus is on the mathematical approach to develop a drive cycle when a large database with high resolution of driving data is available.

Suggested Citation

  • Ali Ashtari & Eric Bibeau & Soheil Shahidinejad, 2014. "Using Large Driving Record Samples and a Stochastic Approach for Real-World Driving Cycle Construction: Winnipeg Driving Cycle," Transportation Science, INFORMS, vol. 48(2), pages 170-183, May.
    • Handle: RePEc:inm:ortrsc:v:48:y:2014:i:2:p:170-183
    DOI: 10.1287/trsc.1120.0447
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    Citations

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    Cited by:

    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. Schücking, Maximilian & Jochem, Patrick, 2021. "Two-stage stochastic program optimizing the cost of electric vehicles in commercial fleets," Applied Energy, Elsevier, vol. 293(C).
    3. Sascha Krysmon & Frank Dorscheidt & Johannes Claßen & Marc Düzgün & Stefan Pischinger, 2021. "Real Driving Emissions—Conception of a Data-Driven Calibration Methodology for Hybrid Powertrains Combining Statistical Analysis and Virtual Calibration Platforms," Energies, MDPI, vol. 14(16), pages 1-27, August.
    4. Dong, Peng & Zhao, Junwei & Liu, Xuewu & Wu, Jian & Xu, Xiangyang & Liu, Yanfang & Wang, Shuhan & Guo, Wei, 2022. "Practical application of energy management strategy for hybrid electric vehicles based on intelligent and connected technologies: Development stages, challenges, and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    5. Hongli Liu & Weiguo Yun & Bin Li & Mengling Dai & Yangyuhang Wang, 2023. "A Quantitative Study on Driving Behavior Economy Based on Big Data from the Pure Electric Bus," Sustainability, MDPI, vol. 15(10), pages 1-16, May.
    6. Cui, Yuepeng & Zou, Fumin & Xu, Hao & Chen, Zhihui & Gong, Kuangmin, 2022. "A novel optimization-based method to develop representative driving cycle in various driving conditions," Energy, Elsevier, vol. 247(C).
    7. Cui, Yuepeng & Xu, Hao & Zou, Fumin & Chen, Zhihui & Gong, Kuangmin, 2021. "Optimization based method to develop representative driving cycle for real-world fuel consumption estimation," Energy, Elsevier, vol. 235(C).
    8. S. M. Ashrafur Rahman & I. M. Rizwanul Fattah & Hwai Chyuan Ong & Fajle Rabbi Ashik & Mohammad Mahmudul Hassan & Md Tausif Murshed & Md Ashraful Imran & Md Hamidur Rahman & Md Akibur Rahman & Mohammad, 2021. "State-of-the-Art of Establishing Test Procedures for Real Driving Gaseous Emissions from Light- and Heavy-Duty Vehicles," Energies, MDPI, vol. 14(14), pages 1-32, July.
    9. Björnsson, Lars-Henrik & Karlsson, Sten, 2015. "Plug-in hybrid electric vehicles: How individual movement patterns affect battery requirements, the potential to replace conventional fuels, and economic viability," Applied Energy, Elsevier, vol. 143(C), pages 336-347.

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