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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:

Contribution: The contribution of this paper is a recursive generalized total least-squares (RGTLS) estimator that offers exponential forgetting and treats data with unequally sized and correlated noise.
Application: RGTLS 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.
Results: 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.
Discussion: While RGTLS outperforms RLS with simulation data, the recursive least squares with multiple forgetting (RLSMF) estimator [1] provides superior accuracy and sufficient robustness through orthogonal parameter projection with experimental data.


Volltext §
DOI: 10.5445/IR/1000078927
Veröffentlicht am 15.01.2018
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Fahrzeugsystemtechnik (FAST)
Publikationstyp Poster
Publikationsjahr 2013
Sprache Englisch
Identifikator urn:nbn:de:swb:90-789270
KITopen-ID: 1000078927
Veranstaltung IEEE Intelligent Vehicles Symposium (IV 2013), Gold Coast, Australien, 23.06.2013 – 26.06.2013
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