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首页> 外文期刊>Transportation Science >Using Large Driving Record Samples and a Stochastic Approach for Real-World Driving Cycle Construction: Winnipeg Driving Cycle
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Using Large Driving Record Samples and a Stochastic Approach for Real-World Driving Cycle Construction: Winnipeg Driving Cycle

机译：使用大型行驶记录样本和随机方法构建实际的驾驶循环：温尼伯驾驶循环

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摘要

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.
机译：插电式电动汽车（PEV）动力总成的发展面临的挑战是有效的能量管理和最佳的能量存储，这对于代表驾驶员行为的驾驶循环至关重要。标准驾驶周期与实际驾驶行为之间的差异源于数据收集不足，周期构造方法不正确以及地理位置所致的变化。在这项研究中，我们通过使用来自温尼伯市（加拿大）超过76年的真实世界中驾驶76辆汽车的车队收集的数据来解决第一个问题，该数据代表了4,400万个数据点。第二个问题是通过提出的新颖的随机驾驶循环构建方法解决的。第三个问题将结果限制在主要是温尼伯和具有类似特征的城市，但是该方法可在任何地方使用。该方法利用从收集的数据库中记录的车辆时间戳速度提取的摘要来开发驾驶周期。将拟议中的温尼伯行驶周期（WPG01）特性与现有的八个标准行驶周期进行比较，并且更能够代表激进驾驶，这在PEV设计中至关重要。试图隔离出多少差异可以归因于样本量和方法。拟议的构造方法可以灵活地针对任何驾驶参数选择进行优化，因此可以成为开发任何动力传动系统拓扑（包括内燃机车辆，混合动力车辆，插电式混合动力和电池电动车辆）的驾驶周期的推荐方法。电动汽车动力总成的占空比至关重要的车辆停车时间和停车类型（家庭，工作，购物）的特性在其他地方已有报道。在此，当具有高分辨率的行驶数据的大型数据库可用时，重点在于开发行驶周期的数学方法。

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