Comparative study of two dynamics-model-based estimation algorithms for distributed drive electric vehicles
| dc.contributor.author | Zhang, Xudong | |
| dc.contributor.author | Göhlich, Dietmar | |
| dc.contributor.author | Fu, Chenrui | |
| dc.date.accessioned | 2017-09-06T08:39:31Z | |
| dc.date.available | 2017-09-06T08:39:31Z | |
| dc.date.issued | 2017 | |
| dc.description.abstract | The 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.sponsorship | DFG, 325093850, Open Access Publizieren 2017 - 2018 / Technische Universität Berlin | de |
| dc.identifier.eissn | 2076-3417 | |
| dc.identifier.uri | https://depositonce.tu-berlin.de/handle/11303/6693 | |
| dc.identifier.uri | http://dx.doi.org/10.14279/depositonce-6134 | |
| dc.language.iso | en | en |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject.ddc | 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten | de |
| dc.subject.other | unscented Kalman filter | en |
| dc.subject.other | moving horizon estimation | en |
| dc.subject.other | vehicle state estimation | en |
| dc.subject.other | distributed drive electric vehicle | en |
| dc.title | Comparative study of two dynamics-model-based estimation algorithms for distributed drive electric vehicles | en |
| dc.type | Article | en |
| dc.type.version | publishedVersion | en |
| dcterms.bibliographicCitation.articlenumber | 898 | en |
| dcterms.bibliographicCitation.doi | 10.3390/app7090898 | en |
| dcterms.bibliographicCitation.issue | 7 | en |
| dcterms.bibliographicCitation.journaltitle | Applied Sciences | en |
| dcterms.bibliographicCitation.originalpublishername | MDPI | en |
| dcterms.bibliographicCitation.originalpublisherplace | Basel | en |
| dcterms.bibliographicCitation.volume | 9 | en |
| tub.accessrights.dnb | free | en |
| tub.affiliation | Fak. 5 Verkehrs- und Maschinensysteme::Inst. Maschinenkonstruktion und Systemtechnik::FG Methoden der Produktentwicklung und Mechatronik | de |
| tub.affiliation.faculty | Fak. 5 Verkehrs- und Maschinensysteme | de |
| tub.affiliation.group | FG Methoden der Produktentwicklung und Mechatronik | de |
| tub.affiliation.institute | Inst. Maschinenkonstruktion und Systemtechnik | de |
| tub.publisher.universityorinstitution | Technische Universität Berlin | en |