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

Institut für Fahrzeugtechnik (3310)

Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in einem Konferenzband › Begutachtung

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.

Originalsprache	englisch
Titel	Advanced Microsystems for Automotive Applications 2017
Untertitel	Smart Systems Transforming the Automobile
Redakteure/-innen	Carolin Zachäus, Beate Müller, Gereon Meyer
Herausgeber (Verlag)	Springer Verlag
Seiten	87-100
Seitenumfang	14
Auflage	1
ISBN (elektronisch)	978-3-319-66972-4
ISBN (Print)	978-3-319-66971-7
DOIs	https://doi.org/10.1007/978-3-319-66972-4_8
Publikationsstatus	Veröffentlicht - 31 Aug. 2017
Veranstaltung	Advanced Microsystems for Automotive Applications - Berlin, Deutschland Dauer: 25 Sept. 2017 → 26 Sept. 2017

Publikationsreihe

Name	Lecture Notes in Mobility
Herausgeber (Verlag)	Springer
ISSN (Print)	2196-5544
ISSN (elektronisch)	2196-5552

Konferenz

Konferenz	Advanced Microsystems for Automotive Applications
Kurztitel	AMAA 2017
Land/Gebiet	Deutschland
Ort	Berlin
Zeitraum	25/09/17 → 26/09/17

Schlagwörter

Mass estimation
Road grade estimation
Vehicle state estimation
Recursive least squares with forgetting

ASJC Scopus subject areas

Fahrzeugbau
Steuerung und Optimierung

Fields of Expertise

Mobility & Production

Treatment code (Nähere Zuordnung)

Theoretical
Experimental
Application

UN SDGs

Dieser Output leistet einen Beitrag zu folgendem(n) Ziel(en) für nachhaltige Entwicklung

Zugriff auf Dokument

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

1 Abgeschlossen
1 Laufend

DVS: 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., Orucevic, F., Puščul, D., Arefnezhad, S., Karoshi, P., Schöttel, C. E., Pandurevic, A., Harcevic, A., Wellershaus, C., Li, H., Mihalj, T., Kanuric, T., Gu, Z., Wallner, D., De Cristofaro, F., Soboleva, K., Nalic, D., Bernsteiner, S., Kraus, H., Zhao, Y., Bodner, J., Bui, D. T., Hirschberg, W., Plöckinger, M. & Khoshnood Sarabi, N.
1/01/11 → …
Projekt: Arbeitsgebiet
RoadCon - Referenzwertermittlung für Straßenzustand im Realverkehr
Lex, C.
1/09/16 → 31/08/17
Projekt: Forschungsprojekt

Dieses zitieren

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

Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in einem Konferenzband › Begutachtung

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 (Hrsg.), Advanced Microsystems for Automotive Applications 2017: Smart Systems Transforming the Automobile. 1 Aufl., Lecture Notes in Mobility, Springer Verlag, S. 87-100, Advanced Microsystems for Automotive Applications, Berlin, Deutschland, 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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Y1 - 2017/8/31

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.

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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

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

M3 - Conference paper

SN - 978-3-319-66971-7

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A2 - Zachäus, Carolin

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A2 - Meyer, Gereon

PB - Springer Verlag

T2 - Advanced Microsystems for Automotive Applications

Y2 - 25 September 2017 through 26 September 2017

ER -

Robust and Numerically Efficient Estimation of Vehicle Mass and Road Grade

Abstract

Publikationsreihe

Konferenz

Schlagwörter

ASJC Scopus subject areas

Fields of Expertise

Treatment code (Nähere Zuordnung)

UN SDGs

Zugriff auf Dokument

Fingerprint

Projekte

DVS: Vehicle Dynamics

RoadCon - Referenzwertermittlung für Straßenzustand im Realverkehr

Dieses zitieren