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Application of a Tractive Energy Analysis to Quantify the Benefits of Advanced Efficiency Technologies for Medium- and Heavy-Duty Trucks Using Characteristic Drive Cycle Data

机译:牵引能分析在使用特征性驱动周期数据量化中型和重型卡车先进效率技术收益中的应用

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摘要

Accurately predicting the fuel savings that can be achieved with the implementation of various technologies developed for fuel efficiency can be very challenging, particularly when considering combinations of technologies. Differences in the usage of highway vehicles can strongly influence the benefits realized with any given technology, which makes generalizations about fuel savings inappropriate for different vehicle applications. A model has been developed to estimate the potential for reducing fuel consumption when advanced efficiency technologies, or combinations of these technologies, are employed on highway vehicles, particularly medium- and heavy-duty trucks. The approach is based on a tractive energy analysis applied to drive cycles representative of the vehicle usage, and the analysis specifically accounts for individual energy loss factors that characterize the technologies of interest. This tractive energy evaluation is demonstrated by analyzing measured drive cycles from a long-haul trucking fleet and the results of an assessment of the fuel savings potential for combinations of technologies are presented. The results of this research will enable more reliable estimates of the fuel savings benefits that can be realized with particular technologies and technology combinations for individual trucking applications so that decision makers can make informed investment decisions for the implementation of advanced efficiency technologies.
机译:准确预测通过实施为提高燃油效率而开发的各种技术可以实现的燃油节省非常困难,特别是在考虑技术组合时。公路车辆使用方法的差异会极大地影响任何给定技术所实现的收益,这使得关于节油的普遍性不适用于不同的车辆应用。已经开发出一种模型来估算在高速公路车辆(特别是中型和重型卡车)上采用先进的效率技术或这些技术的组合时,减少燃油消耗的潜力。该方法基于应用于代表车辆使用的驾驶循环的牵引能量分析,并且该分析专门考虑了表征感兴趣技术的各个能量损失因子。通过分析来自长途卡车车队的测得的驾驶周期,证明了这种牵引能评估,并给出了多种技术组合可节省燃料的评估结果。这项研究的结果将使燃油节省收益的估算更加可靠,而这些节省收益可以通过特定技术和针对单个卡车运输应用的技术组合实现,从而使决策者可以为实施先进效率技术做出明智的投资决策。

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