Robust and Numerically Efficient Estimation of Vehicle Mass and Road Grade

Paul Karoshi; Markus Ager; Martin Schabauer; Cornelia Lex

doi:10.1007/978-3-319-66972-4_8

Robust and Numerically Efficient Estimation of Vehicle Mass and Road Grade

Paul Karoshi, Markus Ager, Martin Schabauer, Cornelia Lex

Institute of Automotive Engineering (3310)

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

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 language	English
Title of host publication	Advanced Microsystems for Automotive Applications 2017
Subtitle of host publication	Smart Systems Transforming the Automobile
Editors	Carolin Zachäus, Beate Müller, Gereon Meyer
Publisher	Springer Verlag
Pages	87-100
Number of pages	14
Edition	1
ISBN (Electronic)	978-3-319-66972-4
ISBN (Print)	978-3-319-66971-7
DOIs	https://doi.org/10.1007/978-3-319-66972-4_8
Publication status	Published - 31 Aug 2017
Event	Advanced Microsystems for Automotive Applications - Berlin, Germany Duration: 25 Sept 2017 → 26 Sept 2017

Publication series

Name	Lecture Notes in Mobility
Publisher	Springer
ISSN (Print)	2196-5544
ISSN (Electronic)	2196-5552

Conference

Conference	Advanced Microsystems for Automotive Applications
Abbreviated title	AMAA 2017
Country/Territory	Germany
City	Berlin
Period	25/09/17 → 26/09/17

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-319-66972-4_8

1 Finished
1 Active

Vehicle Dynamics
Koglbauer, I. V., Lex, C., Shao, L., Semmer, M., Rogic, B., Peer, M., Hackl, A., Sternat, A. S., Schabauer, M., Samiee, S., Eichberger, A., Ager, M., Malić, D., Wohlfahrter, H., Scherndl, C. & Magosi, Z. F.
1/01/11 → …
Project: Research area
RoadCon - Determination of a reference value for tyre-road condition during natural driving
Lex, C.
1/09/16 → 31/08/17
Project: Research project

Cite this

Karoshi, P., Ager, M., Schabauer, M., & Lex, C. (2017). Robust and Numerically Efficient Estimation of Vehicle Mass and Road Grade. In C. Zachäus, B. Müller, & G. Meyer (Eds.), Advanced Microsystems for Automotive Applications 2017: Smart Systems Transforming the Automobile (1 ed., pp. 87-100). (Lecture Notes in Mobility). Springer Verlag. https://doi.org/10.1007/978-3-319-66972-4_8

Robust and Numerically Efficient Estimation of Vehicle Mass and Road Grade. / Karoshi, Paul; Ager, Markus; Schabauer, Martin et al.

Advanced Microsystems for Automotive Applications 2017: Smart Systems Transforming the Automobile. ed. / Carolin Zachäus; Beate Müller; Gereon Meyer. 1. ed. Springer Verlag, 2017. p. 87-100 (Lecture Notes in Mobility).

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Karoshi, P, Ager, M, Schabauer, M & Lex, C 2017, Robust and Numerically Efficient Estimation of Vehicle Mass and Road Grade. in C Zachäus, B Müller & G Meyer (eds), Advanced Microsystems for Automotive Applications 2017: Smart Systems Transforming the Automobile. 1 edn, Lecture Notes in Mobility, Springer Verlag, pp. 87-100, Advanced Microsystems for Automotive Applications, Berlin, Germany, 25/09/17. https://doi.org/10.1007/978-3-319-66972-4_8

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title = "Robust and Numerically Efficient Estimation of Vehicle Mass and Road Grade",

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.",

keywords = "Mass estimation, Road grade estimation, Vehicle state estimation, Recursive least squares with forgetting, Mass estimation, Road grade estimation, Recursive least squares with forgetting",

author = "Paul Karoshi and Markus Ager and Martin Schabauer and Cornelia Lex",

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AU - Ager, Markus

AU - Schabauer, Martin

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N2 - 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.

AB - 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.

KW - Mass estimation

KW - Road grade estimation

KW - Vehicle state estimation

KW - Recursive least squares with forgetting

KW - Mass estimation

KW - Road grade estimation

KW - Recursive least squares with forgetting

U2 - 10.1007/978-3-319-66972-4_8

DO - 10.1007/978-3-319-66972-4_8

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SN - 978-3-319-66971-7

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BT - Advanced Microsystems for Automotive Applications 2017

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Y2 - 25 September 2017 through 26 September 2017

ER -

Robust and Numerically Efficient Estimation of Vehicle Mass and Road Grade

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Fields of Expertise

Treatment code (Nähere Zuordnung)

UN SDGs

Access to Document

Fingerprint

Projects

Vehicle Dynamics

RoadCon - Determination of a reference value for tyre-road condition during natural driving

Cite this