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Bayesian Parameter Estimation for Heavy-Duty Vehicles
Technical Paper
2017-01-0528
ISSN: 0148-7191, e-ISSN: 2688-3627
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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.
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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.Data Sets - Support Documents
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References
- SAE International Surface Vehicle Recommended Practice Road Load Measurement Using On-board Anemometry and Coastdown Techniques SAE Standard J2263 2008
- McAuliffe , B. and Chuang , D. Track-Based Aerodynamic Testing of a Heavy-Duty Vehicle: Coast-Down Measurements SAE Int. J. Commer. Veh. 9 2 381 396 2016 10.4271/2016-01-8152
- Brooker , A. , Ward , J. , and Wang , L. Lightweighting Impacts on Fuel Economy, Cost, and Component Losses SAE Technical Paper 2013-01-0381 2013 10.4271/2013-01-0381
- Carlson , R. , Lohse-Busch , H. , Diez , J. , and Gibbs , J. The Measured Impact of Vehicle Mass on Road Load Forces and Energy Consumption for a BEV, HEV, and ICE Vehicle SAE Int. J. Alt. Power. 2 1 105 114 2013 10.4271/2013-01-1457
- Ryu , J. State and Parameter Estimation For Vehicle Dynamics Control Using GPS Ph.D. dissertation Stanford University
- Zhang , D. , Ivanco , A. , and Filipi , Z. Model-Based Estimation of Vehicle Aerodynamic Drag and Rolling Resistance SAE Int. J. Commer. Veh. 8 2 433 439 2015 10.4271/2015-01-2776
- Vahidi , A. , Stefanopoulou , A. , and Peng , H. Recursive Least Squares With Forgetting For Online Estimation of Vehicle Mass and Road Grade: Theory and Experiments Vehicle System Dynamics 43 2005 10.1080/00423110412331290446
- Winstead , V. and Kolmanovsky , I. Estimation of Road Grade and Vehicle Mass via Model Predictive Control Toronto, Canada August 28 31 1588 1593 10.1109/CCA.2005.1507359
- Lingman , P. and Schmidtbauer , B. Road Slope and Vehicle Mass Estimation Using Kalman Filtering Vehicle System Dynamics 2002 10.1080/00423114.2002.11666217
- Reina , G. , Paiano , M. , and Blanco-Claraco , J.-L. Vehicle parameter estimation using a model-based estimator Mechanical Systems and Signal Processing 2016 http://dx.doi.org/10.1016/j.ymssp.2016.06.038
- Crews , J. H. and Smith , R. C. Modeling and Bayesian parameter estimation for shape memory alloy bending actuators Goulbourne , N. C. and Ounaies , Z. International Society for Optics and Photonics apr 2012 83421N 10.1117/12.914792
- Wiecki , T. V. , Sofer , I. , and Frank , M. J. HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python Frontiers in Neuroinformatics 7 14 2013 10.3389/fninf.2013.00014
- Lauret , P. , Boland , J. , and Ridley , B. Bayesian Statistical Analysis Applied to Solar Radiation Modelling Renewable Energy 49 124 127 2013 10.1016/j.renene.2012.01.049
- Shotwell , M. S. and Slate , E. H. Bayesian Outlier Detection with Dirichlet Process Mixtures Bayesian Analysis 6 4 665 690 2011 10.1214/11-BA625
- Solonen , A. Monte carlo methods in parameter estimation of nonlinear models Master’s thesis Lappeenrante University of Technology 2006
- Eydgahi , C.-e. Hoda , a. Properties of Cell Death Models Calibrated and Compared using Bayesian Approaches Molecular systems biology 9 1 644 2013 10.1038/msb.2012.69
- 40CFR.86 Subpart B; Emission Regulations for 1977 and Later Model Year New Light-Duty Vehicles and New Light-Duty Trucks and New Otto-Cycle Complete Heavy-Duty Vehicles; Test Procedures 2016
- Gelman , A. and Rubin , D. Influence from Iterative Simulation Using Multiple Sequences Statistical Science 7 4 457 511 1992
- NIST/SEMATEC Handbook of Statistical Methods http://www.itl.nist.gov/div898/handbook/
- Sivia , D. Data Analysis: A Bayesian Tutorial Oxford University Press 1997
- Gearhart , C. and Wang , B. Bayesian metrics for comparing response surface models of data with uncertainty Structural and Mulitdisciplinary Optimization 22 2000
- Kelly , K. , Prohaska , R. , Ragatz , A. , and Konan , A. NREL DriveCAT - Chassis Dynamometer Test Cycles www.nrel.gov/transportation/drive-cycle-tool 2016