Bayesian Parameter Estimation for Heavy-Duty Vehicles National Renewable Energy Laboratory

Format:

Conference/Event

Author/Creator:

Miller, Miller, author.

Contributor:

Duran, Adam

Konan, Arnaud

Conference Name:

WCX 17: SAE World Congress Experience (2017-04-04 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2017

Summary:

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

Notes:

Vendor supplied data

Publisher Number:

2017-01-0528

Access Restriction:

Restricted for use by site license