Energy�Impact�of�Connected�and�Automated�Vehicle�Technologies. Final report [electronic resource]

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Tác giả:

Ngôn ngữ: eng

Ký hiệu phân loại: 653.8 Shorthand

Thông tin xuất bản: Washington, D.C. : Oak Ridge, Tenn. : United States. Dept. of Energy. Office of Energy Efficiency and Renewable Energy ; Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy, 2020

Mô tả vật lý: Size: 127 p. : , digital, PDF file.

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ID: 266210

 The overarching goal of this project is to understand the potential impact of connected and automated vehicles. The goal was achieved through data collection, model development, algorithm designs, simulations, and limited field tests. The main outcomes from this project include: (1) we collected energy consumption and GPS data from 500 vehicles over one year, with a total mileage of 8 million miles
  (2) Based on the collected data and other datasets collected at the University of Michigan, we developed a calibrated Ann Arbor model in Polaris (model developed by ANL), and the fuel economy accuracy was found to be around 3.9% by comparison with field collected data
  (3) An open-source SUMO model of Ann Arbor was developed
  (4) Eco-Routing algorithms in Ann Arbor using the SUMO model shows 6% fuel saving potential
  (5) Experiments conducted at the Mcity test facility shows that human drivers roughly follow the Eco-driving suggestions roughly 70% of the time
  (6) Based in the Ann Arbor travel patterns, we found that each shared automated vehicle can replace around 4 individually owned vehicles
  and (7) Adaptive Traffic Signal Control Algorithm developed through this project has been validated both in simulations and preliminary test results. For connected and automated vehicles, on average the performance is 13% delay reduction, and 10% fuel reduction. While connected and automated vehicles are in their early stage of deployment, the results from this project confirm that there is significant potential for energy saving if the technologies are developed and used properly. The three main technologies studied in this project include eco-routing, shared autonomous rides, and adaptive traffic signal controls. The data collected and model developed through the project can be used to study many other connected and automated vehicle technologies.
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