Robust and Numerically Efficient Estimation of Vehicle Mass and Road Grade

Paul Karoshi, Markus Ager, Martin Schabauer, Cornelia Lex

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
Original languageEnglish
Title of host publicationAdvanced Microsystems for Automotive Applications 2017
Subtitle of host publicationSmart Systems Transforming the Automobile
EditorsCarolin Zachäus, Beate Müller, Gereon Meyer
PublisherSpringer Verlag
Pages87-100
Number of pages14
Edition1
ISBN (Electronic)978-3-319-66972-4
ISBN (Print)978-3-319-66971-7
DOIs
Publication statusPublished - 31 Aug 2017
EventAdvanced Microsystems for Automotive Applications - Berlin, Germany
Duration: 25 Sep 201726 Sep 2017

Publication series

NameLecture Notes in Mobility
PublisherSpringer
ISSN (Print)2196-5544
ISSN (Electronic)2196-5552

Conference

ConferenceAdvanced Microsystems for Automotive Applications
Abbreviated titleAMAA
CountryGermany
CityBerlin
Period25/09/1726/09/17

Keywords

  • Mass estimation
  • Road grade estimation
  • Recursive least squares with forgetting

ASJC Scopus subject areas

  • Automotive Engineering
  • Control and Optimization

Fields of Expertise

  • Mobility & Production

Treatment code (Nähere Zuordnung)

  • Theoretical
  • Experimental
  • Application

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