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Real-Time Vehicle Parameters Estimation

[+] Author Affiliations
Jeremy Kolansky, Corina Sandu

Virginia Tech, Blacksburg, VA

Theunis Botha, Schalk Els

University of Pretoria, Hatfield, Pretoria, South Africa

Paper No. DETC2013-12083, pp. V001T01A025; 7 pages
  • ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 1: 15th International Conference on Advanced Vehicle Technologies; 10th International Conference on Design Education; 7th International Conference on Micro- and Nanosystems
  • Portland, Oregon, USA, August 4–7, 2013
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5584-3
  • Copyright © 2013 by ASME


Parameter estimation for vehicle systems is in general a challenging topic from both sensor instrumentation and modeling perspectives. Modeling vehicle systems is a rather complex process, especially considering the numerous unknown effects on the system such as, for example, aerodynamic effects, road grade and bank angles, roll and pitch kinematics, and suspension nonlinearities. This study develops a method that is able to estimate several vehicle parameters with high accuracy for regular driving behavior. The parameter estimations are performed using the polynomial chaos-based extended Kalman filter (gPC-EKF). This method is a computationally efficient, derivative free, iterative, nonlinear regression technique which is able to estimate multiple parameters in real time. The paper presents the results obtained for estimating the location of the CG of the vehicle in the horizontal plane, and the sprung mass of the vehicle using the proposed technique. Real test data have been used for validation purposes.

Copyright © 2013 by ASME
Topics: Vehicles



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