Title: Vehicle sprung mass estimation for rough terrain

Authors: Benjamin Pence; Joseph Hays; Hosam K. Fathy; Corina Sandu; Jeffrey Stein

Addresses: Ford Motor Company, USA ' U.S. Naval Research Laboratory, USA ' Pennsylvania State University, USA ' Virginia Polytechnic Institute and State University, USA ' University of Michigan, USA

Abstract: This paper provides methods and experimental results for recursively estimating the sprung mass of a vehicle driving on rough terrain. A base-excitation model of vertical ride dynamics treats the unsprung vertical accelerations, instead of the terrain profile, as the input to ride dynamics. Recently developed methods based on polynomial chaos and maximum likelihood theory estimate the most likely value of the vehicle sprung mass. The polynomial chaos estimator is compared to least squares and Kalman filtering approaches. An experimental study suggests that the proposed approach provides accurate outputs and is less sensitive to tuning parameters than the benchmark algorithms.

Keywords: vehicle mass estimation; rough terrain; maximum likelihood estimation; extended Kalman filtering; unscented Kalman filtering; polynomial chaos; vehicle design; vehicle sprung mass; vertical ride dynamics; unsprung vertical acceleration.

DOI: 10.1504/IJVD.2013.050837

International Journal of Vehicle Design, 2013 Vol.61 No.1/2/3/4, pp.3 - 26

Available online: 05 Dec 2012 *

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