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Robust and Numerically Efficient Estimation of Vehicle Mass and Road Grade

  • Paul KaroshiEmail author
  • Markus Ager
  • Martin Schabauer
  • Cornelia Lex
Conference paper
Part of the Lecture Notes in Mobility book series (LNMOB)

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 

References

  1. Bauer R (2007) Zustandschätzung und Filterung. Institut für Regelungs- und Automatisierungstechnik, Graz Technical University, TextbookGoogle Scholar
  2. Fortescue TR, Kershenbaum LS, Ydstie BE (1981) Implementation of self-tuning regulators with variable forgetting factors. Automatica 17:831–835CrossRefGoogle Scholar
  3. Grewal MS, Andrews AP (2001) Kalman filtering: theory and practice using MATLAB. Wiley, New YorkzbMATHGoogle Scholar
  4. Heißing B, Ersoy M (2011) Chassis handbook-fundamentals, driving dynamics, components, mechatronics, perspectives. Springer, Wiesbaden, GermanyGoogle Scholar
  5. Hirschberg W, Waser HM (2012) Kraftfahrzeugtechnik. Institute of Automotive Engineering, Graz Technical University, TextbookGoogle Scholar
  6. Huh K, Lim S, Jung J, Hong D, Han S, Han K (2007) Vehicle mass estimator for adaptive roll stability control. SAE World CongressGoogle Scholar
  7. Kidambi N, Harne RL, Fujii Y, Pietron GM, Wang KW (2014) Methods in vehicle mass and road grade estimation. SAE Int Passeng Cars Mech Syst 7Google Scholar
  8. Kohlhuber F, Lienkamp M (2013) Online estimation of physical vehicle parameters with ESC sensors for adaptive vehicle dynamics controllers 13. Internationales Stuttgarter Symposium Automobil-und Motorentechnik, 157–175Google Scholar
  9. Mahyuddin MN (2014) Adaptive observer-based parameter estimation with application to road gradient and vehicle mass estimation. IEEE Trans Ind ElectronGoogle Scholar
  10. Massachustetts Institute of Technology (2008) Signal processing: continuous and discrete, Introduction to Recursive-Least-Square (RLS) adaptive filters. MIT OpenCourseWare. http://mit.edu
  11. McIntyre ML, Ghotikar T, Vahidi A, Song X, Dawson DM (2009) A two-stage lyapunov-based estimator for estimation of vehicle mass and road grade. IEEE Trans Veh Technol 58:3177–3185CrossRefGoogle Scholar
  12. Parkum JE, Poulsen NK, Holst J (1992) Recursive forgetting algorithms. Int J Control 109–128Google Scholar
  13. Rhode S, Hong S, Hedrick J, Gauterin F (2015) Vehicle tractive force prediction with robust and windup-stable Kalman filters. Control Eng Pract 46:37–50CrossRefGoogle Scholar
  14. Rill G (2011) Road vehicle dynamics-fundamentals and modelling, 1st edn. CRC Press-Taylor and Francis Group, p 61Google Scholar
  15. Rozyn M, Zhang N (2010) A method for estimation of vehicle inertial parameters. Veh Syst Dyn 48Google Scholar
  16. SAE International (2014) SAE J3016: Taxonomy and definitions for terms related to on-road motor vehicle automated driving systems. StandardGoogle Scholar
  17. Vahidi A, Stefanopoulou A, Peng H (2005) Recursive least squares with forgetting for online estimation of vehicle mass and road grade: theory and experiments. Int J Veh Mech Mobil 45:31–55Google Scholar
  18. Winner H, Hakuli S, Wolf G (2009) Handbuch Fahrerassistenzsysteme Grundlagen, Komponenten und Systeme für aktive Sicherheit und Komfort, 1st edn. Vieweg + Teubner, WiesbadenGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Paul Karoshi
    • 1
    Email author
  • Markus Ager
    • 1
  • Martin Schabauer
    • 1
  • Cornelia Lex
    • 1
  1. 1.Institute of Automotive Engineering, Graz University of TechnologyGrazAustria

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