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Online estimation of vehicle driving resistance parameters with recursive least squares and recursive total least squares

Rhode, Stephan; Gauterin, Frank

Abstract:
We introduce a recursive generalized total least-squares (RGTLS) algorithm with exponential forgetting that is used for estimation of vehicle driving resistance parameters. A vehicle longitudinal dynamics model and available control area network (CAN) signals form appropriate estimator inputs and outputs. In particular, we present parameter estimates for the vehicle mass, two coefficients of rolling resistance, and drag coefficient of one test run on public road. Moreover, we compare the results of the proposed RGTLS estimator with two kinds of recursive least-squares (RLS) estimators. While RGTLS outperforms RLS with simulation data, the recursive least squares with multiple forgetting (RLSmf) estimator provides superior accuracy and sufficient robustness through orthogonal parameter projection with experimental data. On the other hand, RLSmf suffers from serious convergence problems when it was used without parameter projection.

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Postprint §
DOI: 10.5445/IR/1000036882
Veröffentlicht am 15.01.2018
Originalveröffentlichung
DOI: 10.1109/IVS.2013.6629481
Scopus
Zitationen: 18
Coverbild
Zugehörige Institution(en) am KIT Institut für Fahrzeugsystemtechnik (FAST)
Publikationstyp Proceedingsbeitrag
Jahr 2013
Sprache Englisch
Identifikator ISBN: 978-1-4673-2754-1
urn:nbn:de:swb:90-368827
KITopen-ID: 1000036882
Erschienen in Intelligent Vehicles Symposium (IV), 2013 IEEE, June 23-26, 2013, Gold coast Australia
Verlag IEEE, New York (NY)
Seiten 269-276
Bemerkung zur Veröffentlichung This is the accepted version., Please find the final version in IEEE Xplore, http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6629481&refinements%3D4279979739%26sortType%3Dasc_p_Sequence%26filter%3DAND%28p_IS_Number%3A6629437%29
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