Bayesian Parameter Estimation for Heavy-Duty Vehicles

2017-01-0528

03/28/2017

Features
Event
WCX™ 17: SAE World Congress Experience
Authors Abstract
Content
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.
Meta TagsDetails
DOI
https://doi.org/10.4271/2017-01-0528
Pages
8
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.
Additional Details
Publisher
Published
Mar 28, 2017
Product Code
2017-01-0528
Content Type
Technical Paper
Language
English