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

Ali Ashtari (), Eric Bibeau () and Soheil Shahidinejad ()
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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

Transportation Science, 2014, vol. 48, issue 2, 170-183

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.

Keywords: driving cycle construction; stochastic modeling; optimization; plug-in electric vehicle (PEV); vehicle emissions (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (9)

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