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Title: Bayesian Parameter Estimation for Heavy-Duty Vehicles

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 Monte Carlo to generate parameter sets which is fed to a variant of the road load equation. Modeled road load is then compared to 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. Results confirm the method's ability to estimate reasonable parameter sets, and indicates an opportunity to increase the certainty of estimates through careful selection or generation of the test drive cycle.

Authors:
; ;
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V)
OSTI Identifier:
1351941
Report Number(s):
NREL/CP-5400-67699
DOE Contract Number:
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at WCX17: SAE World Congress Experience, 4-6 April 2017, Detroit, Michigan
Country of Publication:
United States
Language:
English
Subject:
33 ADVANCED PROPULSION SYSTEMS; parameter estimation; vehicle system; Bayesian; Markov-chain Monte Carlo; Metropolis-Hastings; dynamometer

Citation Formats

Miller, Eric, Konan, Arnaud, and Duran, Adam. Bayesian Parameter Estimation for Heavy-Duty Vehicles. United States: N. p., 2017. Web. doi:10.4271/2017-01-0528.
Miller, Eric, Konan, Arnaud, & Duran, Adam. Bayesian Parameter Estimation for Heavy-Duty Vehicles. United States. doi:10.4271/2017-01-0528.
Miller, Eric, Konan, Arnaud, and Duran, Adam. Tue . "Bayesian Parameter Estimation for Heavy-Duty Vehicles". United States. doi:10.4271/2017-01-0528. https://www.osti.gov/servlets/purl/1351941.
@article{osti_1351941,
title = {Bayesian Parameter Estimation for Heavy-Duty Vehicles},
author = {Miller, Eric and Konan, Arnaud and Duran, Adam},
abstractNote = {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 Monte Carlo to generate parameter sets which is fed to a variant of the road load equation. Modeled road load is then compared to 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. Results confirm the method's ability to estimate reasonable parameter sets, and indicates an opportunity to increase the certainty of estimates through careful selection or generation of the test drive cycle.},
doi = {10.4271/2017-01-0528},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Mar 28 00:00:00 EDT 2017},
month = {Tue Mar 28 00:00:00 EDT 2017}
}

Conference:
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