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Title: Simulation of Heavy-Duty Vehicles in Platooning Scenarios

Abstract

Connectivity and automation provide the potential to use information about the environment and future driving to minimize energy consumption. In order to achieve this goal, the designers of control strategies need to simulate a wide range of driving situations that can interact with other vehicles and the infrastructure to account for the specific powertrain characteristics of each automated vehicle. We present here a simulation framework called RoadRunner, which aims to facilitate the design of powertrain-aware eco-driving algorithms and a better understanding of the interactions between automation and powertrain technology. RoadRunner allows users to simulate both powertrain and longitudinal dynamics within a simulated environment. The user defines the scenario to be simulated by providing a route profile, intersection control types, number of vehicles, vehicle class, powertrain technology, connectivity, and automation level. RoadRunner then builds a Simulink diagram of the scenario, including the information flows between vehicles, and between vehicles and the road. After the simulation, the user can analyze the driving, component operations, and detailed energy consumption rates for each simulated vehicle. We present a case study on heavy-duty vehicles platooning. Using RoadRunner and detailed road data from digital maps, we quantify the impact of gap setting on fuel consumption, formore » a real-world route.« less

Authors:
; ; ;
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE) - Office of Vehicle Technology
OSTI Identifier:
1494836
DOE Contract Number:  
AC02-06CH11357
Resource Type:
Conference
Resource Relation:
Conference: 21st IEEE International Conference on Intelligent Transportation Systems, 11/04/18 - 11/07/18, Maui, HI, US
Country of Publication:
United States
Language:
English

Citation Formats

Kim, Namdoo, Karbowski, Dominik, Jeong, Jongryeol, and Rousseau, Aymeric. Simulation of Heavy-Duty Vehicles in Platooning Scenarios. United States: N. p., 2018. Web. doi:10.1109/ITSC.2018.8569275.
Kim, Namdoo, Karbowski, Dominik, Jeong, Jongryeol, & Rousseau, Aymeric. Simulation of Heavy-Duty Vehicles in Platooning Scenarios. United States. doi:10.1109/ITSC.2018.8569275.
Kim, Namdoo, Karbowski, Dominik, Jeong, Jongryeol, and Rousseau, Aymeric. Thu . "Simulation of Heavy-Duty Vehicles in Platooning Scenarios". United States. doi:10.1109/ITSC.2018.8569275.
@article{osti_1494836,
title = {Simulation of Heavy-Duty Vehicles in Platooning Scenarios},
author = {Kim, Namdoo and Karbowski, Dominik and Jeong, Jongryeol and Rousseau, Aymeric},
abstractNote = {Connectivity and automation provide the potential to use information about the environment and future driving to minimize energy consumption. In order to achieve this goal, the designers of control strategies need to simulate a wide range of driving situations that can interact with other vehicles and the infrastructure to account for the specific powertrain characteristics of each automated vehicle. We present here a simulation framework called RoadRunner, which aims to facilitate the design of powertrain-aware eco-driving algorithms and a better understanding of the interactions between automation and powertrain technology. RoadRunner allows users to simulate both powertrain and longitudinal dynamics within a simulated environment. The user defines the scenario to be simulated by providing a route profile, intersection control types, number of vehicles, vehicle class, powertrain technology, connectivity, and automation level. RoadRunner then builds a Simulink diagram of the scenario, including the information flows between vehicles, and between vehicles and the road. After the simulation, the user can analyze the driving, component operations, and detailed energy consumption rates for each simulated vehicle. We present a case study on heavy-duty vehicles platooning. Using RoadRunner and detailed road data from digital maps, we quantify the impact of gap setting on fuel consumption, for a real-world route.},
doi = {10.1109/ITSC.2018.8569275},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {11}
}

Conference:
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