@inproceedings{shen_data-driven_2022,
	address = {Columbus, OH, USA},
	title = {Data-{Driven} {Design} of {Model} {Predictive} {Control} for {Powertrain}-{Aware} {Eco}-{Driving} {Considering} {Nonlinearities} {Using} {Koopman} {Analysis}},
	url = {https://anl.box.com/s/z10wanslss63r43ijydihby9izbkfm31},
	doi = {https://doi.org/10.1016/j.ifacol.2022.10.271},
	abstract = {Eco-driving is a highly nonlinear control problem. The
nonlinearities include the complex energy
conversion/dissipation in the powertrain, environmental
influences such as road grade and aerodynamic drag, and
constraints due to traffic signs, safety issues, and
physical limits of the vehicle system. In recent years,
researchers have increasingly revisited the Koopman
operator to linearize nonlinear dynamics so that the lifted
system evolves linearly in a higher dimensional space. This
paper adopts such an approximation technique to construct
the lifted state space in a data-driven procedure that
allows us to incorporate in the cost function the
powertrain nonlinear inefficiency in vehicle speed, and
system perturbations due to the nonlinear road grade
nonlinear in position. In addition, the nonlinear
constraints in states can also be handled linearly. The
resultant formulation of a linearly constrained quadratic
program can be readily applied to design a model predictive
control that enjoys a low computation load as with a linear
dynamic system. Simulation results demonstrate additional
energy saving potential compared to a linear approach.},
	booktitle = {10th {IFAC} {International} {Symposium} on {Advances} in {Automotive} {Control}},
	author = {Shen, Daliang and Han, Jihun and Karbowski, Dominik and Rousseau, Aymeric},
	month = aug,
	year = {2022},
	note = {ANL},
	keywords = {Connected and Automated Vehicles, DOE SMART, RoadRunner, Vehicle control},
}

{"_id":"GRFkrB9cqcbsLtzvT","bibbaseid":"shen-han-karbowski-rousseau-datadrivendesignofmodelpredictivecontrolforpowertrainawareecodrivingconsideringnonlinearitiesusingkoopmananalysis-2022","author_short":["Shen, D.","Han, J.","Karbowski, D.","Rousseau, A."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","address":"Columbus, OH, USA","title":"Data-Driven Design of Model Predictive Control for Powertrain-Aware Eco-Driving Considering Nonlinearities Using Koopman Analysis","url":"https://anl.box.com/s/z10wanslss63r43ijydihby9izbkfm31","doi":"https://doi.org/10.1016/j.ifacol.2022.10.271","abstract":"Eco-driving is a highly nonlinear control problem. The nonlinearities include the complex energy conversion/dissipation in the powertrain, environmental influences such as road grade and aerodynamic drag, and constraints due to traffic signs, safety issues, and physical limits of the vehicle system. In recent years, researchers have increasingly revisited the Koopman operator to linearize nonlinear dynamics so that the lifted system evolves linearly in a higher dimensional space. This paper adopts such an approximation technique to construct the lifted state space in a data-driven procedure that allows us to incorporate in the cost function the powertrain nonlinear inefficiency in vehicle speed, and system perturbations due to the nonlinear road grade nonlinear in position. In addition, the nonlinear constraints in states can also be handled linearly. The resultant formulation of a linearly constrained quadratic program can be readily applied to design a model predictive control that enjoys a low computation load as with a linear dynamic system. Simulation results demonstrate additional energy saving potential compared to a linear approach.","booktitle":"10th IFAC International Symposium on Advances in Automotive Control","author":[{"propositions":[],"lastnames":["Shen"],"firstnames":["Daliang"],"suffixes":[]},{"propositions":[],"lastnames":["Han"],"firstnames":["Jihun"],"suffixes":[]},{"propositions":[],"lastnames":["Karbowski"],"firstnames":["Dominik"],"suffixes":[]},{"propositions":[],"lastnames":["Rousseau"],"firstnames":["Aymeric"],"suffixes":[]}],"month":"August","year":"2022","note":"ANL","keywords":"Connected and Automated Vehicles, DOE SMART, RoadRunner, Vehicle control","bibtex":"@inproceedings{shen_data-driven_2022,\n\taddress = {Columbus, OH, USA},\n\ttitle = {Data-{Driven} {Design} of {Model} {Predictive} {Control} for {Powertrain}-{Aware} {Eco}-{Driving} {Considering} {Nonlinearities} {Using} {Koopman} {Analysis}},\n\turl = {https://anl.box.com/s/z10wanslss63r43ijydihby9izbkfm31},\n\tdoi = {https://doi.org/10.1016/j.ifacol.2022.10.271},\n\tabstract = {Eco-driving is a highly nonlinear control problem. The\nnonlinearities include the complex energy\nconversion/dissipation in the powertrain, environmental\ninfluences such as road grade and aerodynamic drag, and\nconstraints due to traffic signs, safety issues, and\nphysical limits of the vehicle system. In recent years,\nresearchers have increasingly revisited the Koopman\noperator to linearize nonlinear dynamics so that the lifted\nsystem evolves linearly in a higher dimensional space. This\npaper adopts such an approximation technique to construct\nthe lifted state space in a data-driven procedure that\nallows us to incorporate in the cost function the\npowertrain nonlinear inefficiency in vehicle speed, and\nsystem perturbations due to the nonlinear road grade\nnonlinear in position. In addition, the nonlinear\nconstraints in states can also be handled linearly. The\nresultant formulation of a linearly constrained quadratic\nprogram can be readily applied to design a model predictive\ncontrol that enjoys a low computation load as with a linear\ndynamic system. Simulation results demonstrate additional\nenergy saving potential compared to a linear approach.},\n\tbooktitle = {10th {IFAC} {International} {Symposium} on {Advances} in {Automotive} {Control}},\n\tauthor = {Shen, Daliang and Han, Jihun and Karbowski, Dominik and Rousseau, Aymeric},\n\tmonth = aug,\n\tyear = {2022},\n\tnote = {ANL},\n\tkeywords = {Connected and Automated Vehicles, DOE SMART, RoadRunner, Vehicle control},\n}\n\n","author_short":["Shen, D.","Han, J.","Karbowski, D.","Rousseau, A."],"key":"shen_data-driven_2022","id":"shen_data-driven_2022","bibbaseid":"shen-han-karbowski-rousseau-datadrivendesignofmodelpredictivecontrolforpowertrainawareecodrivingconsideringnonlinearitiesusingkoopmananalysis-2022","role":"author","urls":{"Paper":"https://anl.box.com/s/z10wanslss63r43ijydihby9izbkfm31"},"keyword":["Connected and Automated Vehicles","DOE SMART","RoadRunner","Vehicle control"],"metadata":{"authorlinks":{}},"downloads":16},"bibtype":"inproceedings","biburl":"https://api.zotero.org/groups/2254223/items?key=Bbol5AZSx3A5fDcmq91mfOKd&format=bibtex&limit=100","dataSources":["Emhp3E5GZrDSaunEJ","qc6TuRNtLYeLyawhB","cGgmoLgbNTEZGWgPE","YAe24F3YNSozTyd4w","8NTJjnAGyZ5qTfMug"],"keywords":["connected and automated vehicles","doe smart","roadrunner","vehicle control"],"search_terms":["data","driven","design","model","predictive","control","powertrain","aware","eco","driving","considering","nonlinearities","using","koopman","analysis","shen","han","karbowski","rousseau"],"title":"Data-Driven Design of Model Predictive Control for Powertrain-Aware Eco-Driving Considering Nonlinearities Using Koopman Analysis","year":2022,"downloads":16}