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2018 | OriginalPaper | Buchkapitel

Robust and Numerically Efficient Estimation of Vehicle Mass and Road Grade

verfasst von: Paul Karoshi, Markus Ager, Martin Schabauer, Cornelia Lex

Erschienen in: Advanced Microsystems for Automotive Applications 2017

Verlag: Springer International Publishing

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Abstract

A recursive least squares (RLS) based observer for simultaneous estimation of vehicle mass and road grade, using longitudinal vehicle dynamics, is presented. In order to achieve robustness to unknown disturbances and varying parameters, depth is chosen in a sufficient way. This is done with a sensitivity analysis, identifying parameters with significant influence on the estimation result. The identification of vehicle parameters is presented in detail. The method is validated with an all-electric vehicle (AEV) using natural driving cycles. The results show little deviation between estimation and reference, as well as good convergence in urban areas, providing sufficient excitation. However, on highway roads, environmental influences like wind and slipstream of trucks, worsen the results, especially in combination with little excitation for the observer.
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Metadaten
Titel
Robust and Numerically Efficient Estimation of Vehicle Mass and Road Grade
verfasst von
Paul Karoshi
Markus Ager
Martin Schabauer
Cornelia Lex
Copyright-Jahr
2018
DOI
https://doi.org/10.1007/978-3-319-66972-4_8

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