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Driving cycles that reproduce driving patterns, energy consumptions and tailpipe emissions
Transportation Research Part D: Transport and Environment ( IF 7.6 ) Pub Date : 2020-03-13 , DOI: 10.1016/j.trd.2020.102294
Luis F. Quirama , Michael Giraldo , José I. Huertas , Miguel Jaller

This study presents the Energy Based Micro-trip (EBMT) method, which is a new method to construct driving cycles that represent local driving patterns and reproduce the real energy consumption and tailpipe emissions from vehicles in a given region. It uses data of specific energy consumption, speed, and percentage of idling time as criteria of acceptable representativeness. To study the performance of the EBMT, we used a database of speed, fuel consumption, and tailpipe emissions (CO2, CO, and NOx), which was obtained monitoring at 1 Hz, the operation of 15 heavy-duty vehicles when they operated within different traffic conditions, during eight months. The speed vs. time data contained in this database defined the local driving pattern, which was described by 19 characteristic parameters (CPs). Using this database, we ran the EBMT and described the resulting driving cycle by 19 characteristics parameters (CPs*). The relative differences between CPs and CPs* quantified how close the obtained driving cycle represented the driving pattern. To observe tendencies of our results, we repeated the process 1000 times and reported the average relative difference (ARD) and the interquartile range (IQR) of those differences for each CP.. We repeated the process for the case of a traditional Micro-trip method and compared to previous results. The driving cycles constructed by the EBMT method showed the lowest values of ARDs and IQRs, meaning that it produces driving cycles with the highest representativeness of the driving patterns, and the best reproduction of energy consumption, and tailpipe emissions.



中文翻译:

重现驾驶模式,能耗和尾气排放的驾驶循环

这项研究提出了基于能量的微行程(EBMT)方法,这是一种构建代表局部驾驶模式的驾驶循环并重现给定区域中车辆的实际能耗和尾气排放的新方法。它使用特定能耗,速度和空转时间百分比的数据作为可接受代表性的标准。为了研究EBMT的性能,我们使用了速度,燃油消耗和尾气排放(CO 2,CO和NO x)(以1 Hz的频率监测),这15辆重型车辆在八个月内在不同的交通状况下运行。该数据库中包含的速度与时间数据定义了本地驾驶模式,该模式由19个特征参数(CP)进行描述。使用该数据库,我们运行了EBMT,并通过19个特征参数(CPs *)描述了由此产生的驾驶周期。CP和CPs *之间的相对差异量化了所获得的驾驶周期代表驾驶模式的接近程度。为了观察结果的趋势,我们重复了该过程1000次,并报告了每个CP的平均相对差异(ARD)和这些差异的四分位间距(IQR)。对于传统的微行程,我们重复该过程方法并与以前的结果进行比较。

更新日期:2020-03-16
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