000905801 001__ 905801
000905801 005__ 20240712112911.0
000905801 0247_ $$2doi$$a10.1109/TITS.2021.3131298
000905801 0247_ $$2ISSN$$a1524-9050
000905801 0247_ $$2ISSN$$a1558-0016
000905801 0247_ $$2Handle$$a2128/31847
000905801 0247_ $$2WOS$$aWOS:000732220300001
000905801 037__ $$aFZJ-2022-01021
000905801 082__ $$a620
000905801 1001_ $$0P:(DE-HGF)0$$aCaspari, Adrian$$b0
000905801 245__ $$aOptimal Eco-Routing for Hybrid Vehicles With Powertrain Model Embedded
000905801 260__ $$aNew York, NY$$bInst. of Electrical and Electronics Engineers$$c2022
000905801 3367_ $$2DRIVER$$aarticle
000905801 3367_ $$2DataCite$$aOutput Types/Journal article
000905801 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1663233983_4692
000905801 3367_ $$2BibTeX$$aARTICLE
000905801 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000905801 3367_ $$00$$2EndNote$$aJournal Article
000905801 520__ $$aExploiting the full potential of hybrid electric vehicles (HEVs) requires suitable (i) route selection and (ii) power management. Due to coupling of the two subproblems, an integrated optimization problem is desired, i.e., optimizing simultaneously the route selection and the split between combustion engine and electric motor over the entire route selection. The resulting optimal route and vehicle operation can be used as a basis for a subordinate vehicle controller. We present an eco-routing approach that embeds a hybrid (mechanistic/data-driven) model of the HEV powertrain in an integrated routing and power management optimization problem. Formulating the integrated routing problem with the hybrid model yields a mixed-integer bilinear program which we reformulate and solve a mixed-integer linear program using a state-of-the-art solver. The results show the validity of the developed hybrid powertrain model and demonstrate that the eco routing approach with the powertrain model embedded can be applied to large-scale problems. We consider optimization for minimal travel time and minimum fuel consumption. The latter results in fuel demand reductions up to 70 %. Alternatively, we minimize the fuel consumption while constraining the travel time to a maximum value resulting in up to 50 % fuel demand reductions. The highest fuel demand reductions are achieved in urban environments. The entire framework is written in python and provided as an open-source version (MIT License) under https://git.rwth-aachen.de/avt-svt/public/optimal-routing that can readily be applied.
000905801 536__ $$0G:(DE-HGF)POF4-899$$a899 - ohne Topic (POF4-899)$$cPOF4-899$$fPOF IV$$x0
000905801 588__ $$aDataset connected to CrossRef, Journals: juser.fz-juelich.de
000905801 7001_ $$0P:(DE-HGF)0$$aFahr, Steffen$$b1
000905801 7001_ $$0P:(DE-Juel1)172025$$aMitsos, Alexander$$b2$$eCorresponding author$$ufzj
000905801 773__ $$0PERI:(DE-600)2034300-0$$a10.1109/TITS.2021.3131298$$gp. 1 - 17$$n9$$p14632 - 14648$$tIEEE transactions on intelligent transportation systems$$v23$$x1524-9050$$y2022
000905801 8564_ $$uhttps://juser.fz-juelich.de/record/905801/files/Optimal_Eco-Routing_for_Hybrid_Vehicles_With_Powertrain_Model_Embedded.pdf$$yRestricted
000905801 8564_ $$uhttps://juser.fz-juelich.de/record/905801/files/mitsos_21_ecorouting_postreferee.pdf$$yOpenAccess
000905801 909CO $$ooai:juser.fz-juelich.de:905801$$pdnbdelivery$$pdriver$$pVDB$$popen_access$$popenaire
000905801 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-HGF)0$$aRWTH Aachen$$b0$$kRWTH
000905801 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-HGF)0$$aRWTH Aachen$$b1$$kRWTH
000905801 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-Juel1)172025$$aRWTH Aachen$$b2$$kRWTH
000905801 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)172025$$aForschungszentrum Jülich$$b2$$kFZJ
000905801 9131_ $$0G:(DE-HGF)POF4-899$$1G:(DE-HGF)POF4-890$$2G:(DE-HGF)POF4-800$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$aDE-HGF$$bProgrammungebundene Forschung$$lohne Programm$$vohne Topic$$x0
000905801 9141_ $$y2022
000905801 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2021-01-27
000905801 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000905801 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2021-01-27
000905801 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bIEEE T INTELL TRANSP : 2021$$d2022-11-25
000905801 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2022-11-25
000905801 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2022-11-25
000905801 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2022-11-25
000905801 915__ $$0StatID:(DE-HGF)1160$$2StatID$$aDBCoverage$$bCurrent Contents - Engineering, Computing and Technology$$d2022-11-25
000905801 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2022-11-25
000905801 915__ $$0StatID:(DE-HGF)9905$$2StatID$$aIF >= 5$$bIEEE T INTELL TRANSP : 2021$$d2022-11-25
000905801 920__ $$lyes
000905801 9201_ $$0I:(DE-Juel1)IEK-10-20170217$$kIEK-10$$lModellierung von Energiesystemen$$x0
000905801 9801_ $$aFullTexts
000905801 980__ $$ajournal
000905801 980__ $$aVDB
000905801 980__ $$aUNRESTRICTED
000905801 980__ $$aI:(DE-Juel1)IEK-10-20170217
000905801 981__ $$aI:(DE-Juel1)ICE-1-20170217