Energy Prediction of the Chicago Metropolitan Area Using Distributed Transportation MBSE Framework. Freyermuth, V.; Auld, J.; Moawad, A.; Pagerit, S.; Rousseau, A.; and Karbowski, D. In IEEE Vehicle Power and Propulsion Conference (VPPC), 2019.
Energy Prediction of the Chicago Metropolitan Area Using Distributed Transportation MBSE Framework [link]Paper  abstract   bibtex   1 download  
Energy consumption impact of vehicle powertrain is traditionally assessed using standard and regulatory drive cycles. In this paper, energy consumption is determined by studying all the trips occurring in the Chicago Metropolitan Area in a 24 hour period. The data is generated by combining Polaris, an agent based traffic flow model with SVTrip, a stochastic model that generates 1 Hz vehicle speed profiles and Autonomie, a detailed vehicle level modeling tool used to predict fuel consumption. Different scenarios are constructed to define trips for different timeframes and assumptions. Vehicle fleet distribution, vehicle technologies and automation levels are applied to represent an appropriate vehicle fleet for each scenario. Analysis of energy consumption is then discussed with a particular emphasis on the impact of powertrain technology and vehicle types.
@inproceedings{freyermuth_energy_2019,
	title = {Energy {Prediction} of the {Chicago} {Metropolitan} {Area} {Using} {Distributed} {Transportation} {MBSE} {Framework}},
	url = {https://anl.box.com/s/65eh53yho6i4y41ndwdijptj14bwjshf},
	abstract = {Energy consumption impact of vehicle powertrain is traditionally assessed using standard and regulatory drive cycles. In this paper, energy consumption is determined by studying all the trips occurring in the Chicago Metropolitan Area in a 24 hour period. The data is generated by combining Polaris, an agent based traffic flow model with SVTrip, a stochastic model that generates 1 Hz vehicle speed profiles and Autonomie, a detailed vehicle level modeling tool used to predict fuel consumption. Different scenarios are constructed to define trips for different timeframes and assumptions. Vehicle fleet distribution, vehicle technologies and automation levels are applied to represent an appropriate vehicle fleet for each scenario. Analysis of energy consumption is then discussed with a particular emphasis on the impact of powertrain technology and vehicle types.},
	booktitle = {{IEEE} {Vehicle} {Power} and {Propulsion} {Conference} ({VPPC})},
	author = {Freyermuth, Vincent and Auld, Joshua and Moawad, Ayman and Pagerit, Sylvain and Rousseau, Aymeric and Karbowski, Dominik},
	year = {2019},
	keywords = {AMBER, Autonomie, DOE SMART, POLARIS, SVTRIP, Transportation systems},
}
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