@inproceedings{michel_impact_2016,
	title = {Impact of {Connectivity} and {Automation} on {Vehicle} {Energy} {Use}},
	volume = {2016-01-0152},
	url = {https://anl.box.com/s/4ohijjl6qrrep9q7vx8zv90vc16plvsj},
	doi = {10.4271/2016-01-0152},
	abstract = {Connectivity and automation are increasingly being developed for cars and trucks, aiming to provide better safety and better driving experience. As these technologies mature and reach higher adoption rates, they will also have an impact on the energy consumption: Connected and Automated Vehicles (CAVs) may drive more smoothly, stop less often, and move at faster speeds, thanks to overall improvements to traffic flows. These potential impacts are not well studied, and any existing studies tend to focus solely on conventional engine-powered cars, leaving aside electrified vehicles such as Hybrid Electric Vehicles (HEVs) and Battery Electric Vehicles (BEVs).This work intends to address this issue by analyzing the energy impact of various CAV scenarios on different types of electric vehicles using high-fidelity models. The vehicles-all midsize, one HEV, one BEV, and a conventional-are modeled in Autonomie, a high-fidelity, forward-looking vehicle simulation tool. They are simulated on various CAVs scenarios and modeled by variations of the drive cycle.First, a reference fuel consumption value is obtained for steady-state speeds, which estimate an optimal state reached by the highest achievable connectivity degree, in which vehicles never stop and drive at constant speed. Second, Real-World Driving Cycles (RWDCs) are selected from a database of recorded Global Positioning System (GPS) traces in the Chicago area. Energetic criteria are used to select RWDCs representing the average driving style. Different changes to the original speed profiles are then applied to represent the connectivity impact: some stops are removed, speed is smoothed, and strong accelerations are saturated. An overall increase in speed is also investigated to represent improved traffic flow. In each case, the distance remains the same as in the original case, representing the same origin and destination. Finally, a detailed energy analysis is performed, highlighting the close relationship between CAV technologies and powertrain electrification.This work shows the synergies between connected vehicles and electrified vehicles. Conventional vehicles and electrified vehicles both can benefit highly from connectivity because they have an equivalent proportional potential fuel consumption reduction. However, on the one hand, because conventional vehicle fuel consumption is the highest, connectivity has a larger absolute energy impact on conventional vehicles. On the other hand, because the best fuel consumption levels for HEVs and BEVs are more easily reached with connectivity, connected electrified powertrains also have high energy savings potential.},
	urldate = {2016-09-08},
	booktitle = {{SAE} {Technical} {Paper}},
	author = {Michel, Pierre and Karbowski, Dominik and Rousseau, Aymeric},
	month = apr,
	year = {2016},
	keywords = {Connected and Automated Vehicles, Driver Modeling},
}

{"_id":"vzGqLzzzG7B6mch3W","bibbaseid":"michel-karbowski-rousseau-impactofconnectivityandautomationonvehicleenergyuse-2016","authorIDs":[],"author_short":["Michel, P.","Karbowski, D.","Rousseau, A."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","title":"Impact of Connectivity and Automation on Vehicle Energy Use","volume":"2016-01-0152","url":"https://anl.box.com/s/4ohijjl6qrrep9q7vx8zv90vc16plvsj","doi":"10.4271/2016-01-0152","abstract":"Connectivity and automation are increasingly being developed for cars and trucks, aiming to provide better safety and better driving experience. As these technologies mature and reach higher adoption rates, they will also have an impact on the energy consumption: Connected and Automated Vehicles (CAVs) may drive more smoothly, stop less often, and move at faster speeds, thanks to overall improvements to traffic flows. These potential impacts are not well studied, and any existing studies tend to focus solely on conventional engine-powered cars, leaving aside electrified vehicles such as Hybrid Electric Vehicles (HEVs) and Battery Electric Vehicles (BEVs).This work intends to address this issue by analyzing the energy impact of various CAV scenarios on different types of electric vehicles using high-fidelity models. The vehicles-all midsize, one HEV, one BEV, and a conventional-are modeled in Autonomie, a high-fidelity, forward-looking vehicle simulation tool. They are simulated on various CAVs scenarios and modeled by variations of the drive cycle.First, a reference fuel consumption value is obtained for steady-state speeds, which estimate an optimal state reached by the highest achievable connectivity degree, in which vehicles never stop and drive at constant speed. Second, Real-World Driving Cycles (RWDCs) are selected from a database of recorded Global Positioning System (GPS) traces in the Chicago area. Energetic criteria are used to select RWDCs representing the average driving style. Different changes to the original speed profiles are then applied to represent the connectivity impact: some stops are removed, speed is smoothed, and strong accelerations are saturated. An overall increase in speed is also investigated to represent improved traffic flow. In each case, the distance remains the same as in the original case, representing the same origin and destination. Finally, a detailed energy analysis is performed, highlighting the close relationship between CAV technologies and powertrain electrification.This work shows the synergies between connected vehicles and electrified vehicles. Conventional vehicles and electrified vehicles both can benefit highly from connectivity because they have an equivalent proportional potential fuel consumption reduction. However, on the one hand, because conventional vehicle fuel consumption is the highest, connectivity has a larger absolute energy impact on conventional vehicles. On the other hand, because the best fuel consumption levels for HEVs and BEVs are more easily reached with connectivity, connected electrified powertrains also have high energy savings potential.","urldate":"2016-09-08","booktitle":"SAE Technical Paper","author":[{"propositions":[],"lastnames":["Michel"],"firstnames":["Pierre"],"suffixes":[]},{"propositions":[],"lastnames":["Karbowski"],"firstnames":["Dominik"],"suffixes":[]},{"propositions":[],"lastnames":["Rousseau"],"firstnames":["Aymeric"],"suffixes":[]}],"month":"April","year":"2016","keywords":"Connected and Automated Vehicles, Driver Modeling","bibtex":"@inproceedings{michel_impact_2016,\n\ttitle = {Impact of {Connectivity} and {Automation} on {Vehicle} {Energy} {Use}},\n\tvolume = {2016-01-0152},\n\turl = {https://anl.box.com/s/4ohijjl6qrrep9q7vx8zv90vc16plvsj},\n\tdoi = {10.4271/2016-01-0152},\n\tabstract = {Connectivity and automation are increasingly being developed for cars and trucks, aiming to provide better safety and better driving experience. As these technologies mature and reach higher adoption rates, they will also have an impact on the energy consumption: Connected and Automated Vehicles (CAVs) may drive more smoothly, stop less often, and move at faster speeds, thanks to overall improvements to traffic flows. These potential impacts are not well studied, and any existing studies tend to focus solely on conventional engine-powered cars, leaving aside electrified vehicles such as Hybrid Electric Vehicles (HEVs) and Battery Electric Vehicles (BEVs).This work intends to address this issue by analyzing the energy impact of various CAV scenarios on different types of electric vehicles using high-fidelity models. The vehicles-all midsize, one HEV, one BEV, and a conventional-are modeled in Autonomie, a high-fidelity, forward-looking vehicle simulation tool. They are simulated on various CAVs scenarios and modeled by variations of the drive cycle.First, a reference fuel consumption value is obtained for steady-state speeds, which estimate an optimal state reached by the highest achievable connectivity degree, in which vehicles never stop and drive at constant speed. Second, Real-World Driving Cycles (RWDCs) are selected from a database of recorded Global Positioning System (GPS) traces in the Chicago area. Energetic criteria are used to select RWDCs representing the average driving style. Different changes to the original speed profiles are then applied to represent the connectivity impact: some stops are removed, speed is smoothed, and strong accelerations are saturated. An overall increase in speed is also investigated to represent improved traffic flow. In each case, the distance remains the same as in the original case, representing the same origin and destination. Finally, a detailed energy analysis is performed, highlighting the close relationship between CAV technologies and powertrain electrification.This work shows the synergies between connected vehicles and electrified vehicles. Conventional vehicles and electrified vehicles both can benefit highly from connectivity because they have an equivalent proportional potential fuel consumption reduction. However, on the one hand, because conventional vehicle fuel consumption is the highest, connectivity has a larger absolute energy impact on conventional vehicles. On the other hand, because the best fuel consumption levels for HEVs and BEVs are more easily reached with connectivity, connected electrified powertrains also have high energy savings potential.},\n\turldate = {2016-09-08},\n\tbooktitle = {{SAE} {Technical} {Paper}},\n\tauthor = {Michel, Pierre and Karbowski, Dominik and Rousseau, Aymeric},\n\tmonth = apr,\n\tyear = {2016},\n\tkeywords = {Connected and Automated Vehicles, Driver Modeling},\n}\n\n","author_short":["Michel, P.","Karbowski, D.","Rousseau, A."],"key":"michel_impact_2016","id":"michel_impact_2016","bibbaseid":"michel-karbowski-rousseau-impactofconnectivityandautomationonvehicleenergyuse-2016","role":"author","urls":{"Paper":"https://anl.box.com/s/4ohijjl6qrrep9q7vx8zv90vc16plvsj"},"keyword":["Connected and Automated Vehicles","Driver Modeling"],"metadata":{"authorlinks":{}}},"bibtype":"inproceedings","biburl":"https://vms.es.anl.gov/publications/bibtex","creationDate":"2019-06-07T18:27:12.701Z","downloads":0,"keywords":["connected and automated vehicles","driver modeling"],"search_terms":["impact","connectivity","automation","vehicle","energy","use","michel","karbowski","rousseau"],"title":"Impact of Connectivity and Automation on Vehicle Energy Use","year":2016,"dataSources":["WZSd6SdWp6wSKRgj6","zbKe4TQXjm3puEXsu"]}