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Main Title: Comparative study of two dynamics-model-based estimation algorithms for distributed drive electric vehicles
Author(s): Zhang, Xudong
Göhlich, Dietmar
Fu, Chenrui
Type: Article
Language Code: en
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.
Issue Date: 2017
Date Available: 10-Nov-2017
DDC Class: 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
Subject(s): unscented Kalman filter
moving horizon estimation
vehicle state estimation
distributed drive electric vehicle
Sponsor/Funder: DFG, TH 662/19-1, Open Access Publizieren 2017 - 2018 / Technische Universität Berlin
Journal Title: Applied Sciences
Publisher: MDPI
Publisher Place: Basel
Volume: 7
Issue: 9
Article Number: 898
Publisher DOI: 10.3390/app7090898
ISSN: 2076-3417
Appears in Collections:FG Methoden der Produktentwicklung und Mechatronik » Publications

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