Bayesian Parameter Estimation for Heavy-Duty Vehicles

Paper #:
  • 2017-01-0528

Published:
  • 2017-03-28
DOI:
  • 10.4271/2017-01-0528
Citation:
Miller, E., Konan, A., and Duran, A., "Bayesian Parameter Estimation for Heavy-Duty Vehicles," SAE Technical Paper 2017-01-0528, 2017, https://doi.org/10.4271/2017-01-0528.
Pages:
8
Abstract:
Accurate vehicle parameters are valuable for design, modeling, and reporting. Estimating vehicle parameters can be a very time-consuming process requiring tightly-controlled experimentation. This work describes a method to estimate vehicle parameters such as mass, coefficient of drag/frontal area, and rolling resistance using data logged during standard vehicle operation. The method uses a Monte Carlo method to generate parameter sets that are fed to a variant of the road load equation. The modeled road load is then compared to the measured load to evaluate the probability of the parameter set. Acceptance of a proposed parameter set is determined using the probability ratio to the current state, so that the chain history will give a distribution of parameter sets. Compared to a single value, a distribution of possible values provides information on the quality of estimates and the range of possible parameter values. The method is demonstrated by estimating dynamometer parameters. The results confirm the method’s ability to estimate reasonable parameter sets, and indicate an opportunity to increase the certainty of estimates through careful selection or generation of the test drive cycle.
Access
Now
SAE MOBILUS Subscriber? You may already have access.
Buy
Select
Price
List
Download
$27.00
Mail
$27.00
Members save up to 40% off list price.
Share
HTML for Linking to Page
Page URL

Related Items

Training / Education
2010-03-15
Standard
2012-12-07
Article
2016-11-15
Article
2016-11-11
Article
2016-11-11
Technical Paper / Journal Article
2010-09-28
Training / Education
2008-08-26