利用稀疏重型车辆位置数据评价队列行驶的节油潜力

Kuo-Yun Liang, J. Mårtensson, K. Johansson
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引用次数: 53

摘要

对于重型汽车制造商来说,车辆队列非常重要,因为它可以减少跟随车辆的空气动力学阻力,从而降低整体油耗。重型车辆司机意识到这一事实,有时会靠近其他重型车辆行驶。然而,目前还不清楚有多少车辆真正在这种自发排中行驶。本文通过分析欧洲某地区一天内的稀疏车辆位置数据,研究了1800辆重型车辆的排队率。地图匹配和路径推理算法用于确定车辆所走的路径。自发排队率为1.2%,与没有车辆排队相比,相当于总燃油节省0.07%。此外,我们还介绍了几种虚拟协调方案。我们的研究表明,通过对当前出行计划进行微小调整,协调可以将队列行驶率和燃油节约提高十倍。如果允许更高的灵活性,排速和燃油节约将大大提高。
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Fuel-saving potentials of platooning evaluated through sparse heavy-duty vehicle position data
Vehicle platooning is important for heavy-duty vehicle manufacturers, due to the reduced aerodynamic drag for the follower vehicles, which gives an overall lower fuel consumption. Heavy-duty vehicle drivers are aware this fact and sometimes drive close to other heavy-duty vehicles. However, it is not currently well known how many vehicles are actually driving in such spontaneous platoons today. This paper studies the platooning rate of 1,800 heavy-duty vehicles by analyzing sparse vehicle position data from a region in Europe during one day. Map-matching and path-inference algorithms are used to determine which paths the vehicles took. The spontaneous platooning rate is found to be 1.2 %, which corresponds to a total fuel saving of 0.07% compared to if none of the vehicles were platooning. Furthermore, we introduce several virtual coordination schemes. We show that coordinations can increase the platooning rate and fuel saving with a factor of ten with minor adjustments from the current travel schedule. The platooning rate and fuel savings can be significantly greater if higher flexibility is allowed.
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