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)


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.


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


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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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