Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-6134
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dc.contributor.authorZhang, Xudong-
dc.contributor.authorGöhlich, Dietmar-
dc.contributor.authorFu, Chenrui-
dc.date.accessioned2017-09-06T08:39:31Z-
dc.date.available2017-09-06T08:39:31Z-
dc.date.issued2017-
dc.identifier.urihttp://depositonce.tu-berlin.de/handle/11303/6693-
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-6134-
dc.description.abstractThe effect of vehicle active safety systems is subject to the accurate knowledge of vehicle states. Therefore, it is of great importance to develop a precise and robust estimation approach so as to deal with nonlinear vehicle dynamics systems. In this paper, a planar vehicle model with a simplified tire model is established first. Two advanced model-based estimation algorithms, an unscented Kalman filter and a moving horizon estimation, are developed for distributed drive electric vehicles. Using the proposed algorithms, vehicle longitudinal velocity, lateral velocity, yaw rate as well as lateral tire forces are estimated based on information fusion of standard sensors in today’s typical vehicle and feedback signals from electric motors. Computer simulations are implemented in the environment of CarSim combined with Matlab/Simulink. The performance of both estimators regarding convergence, accuracy, and robustness against an incorrect initial estimate of longitudinal velocity is compared in detail. The comparison results demonstrate that both estimation approaches have favourable coincidence with the corresponding reference values, while the moving horizon estimation is more accurate and robust, and owns faster convergence.en
dc.description.sponsorshipDFG, TH 662/19-1, Open Access Publizieren 2017 - 2018 / Technische Universität Berlinen
dc.language.isoenen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subject.ddcDDC::600 Technik, Medizin, angewandte Wissenschaften::620 Ingenieurwissenschaften::620 Ingenieurwissenschaften und zugeordnete Tätigkeitenen
dc.subject.otherunscented Kalman filteren
dc.subject.othermoving horizon estimationen
dc.subject.othervehicle state estimationen
dc.subject.otherdistributed drive electric vehicleen
dc.titleComparative study of two dynamics-model-based estimation algorithms for distributed drive electric vehiclesen
dc.typeArticleen
tub.publisher.universityorinstitutionTechnische Universität Berlinen
dc.identifier.eissn2076-3417-
dc.type.versionpublishedVersionen
dcterms.bibliographicCitation.doi10.3390/app7090898en
dcterms.bibliographicCitation.journaltitleApplied Sciencesen
dcterms.bibliographicCitation.originalpublisherplaceBaselen
dcterms.bibliographicCitation.volume9en
dcterms.bibliographicCitation.originalpublishernameMDPIen
dcterms.bibliographicCitation.issue7en
dcterms.bibliographicCitation.articlenumber898en
Appears in Collections:Technische Universität Berlin » Fakultäten & Zentralinstitute » Fakultät 5 Verkehrs- und Maschinensysteme » Institut für Konstruktion, Mikro- und Medizintechnik » Fachgebiet Methoden der Produktentwicklung und Mechatronik » Publications

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