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Title: Parametric Optimization Problem Formulation for Connected Hybrid Electric Vehicles using Neural Network Based Equivalent Model

Abstract

The dynamics of powertrain control systems are complicated and involve both nonlinear plant model and control functionalities, albeit they are well defined and formulated using first principle approaches. This constitutes difficulties in exploring implementable optimal tuning rules for some selected control parameters using vehicle-to-vehicle (V2V) communications. This paper presents a way to use neural networks (NN) to represent the problem of parameter tuning for optimizing fuel consumption. For this purpose, physical modelling and validation have been firstly performed for the closed loop powertrain system of the concerned vehicle for some given driving cycles. This is then followed by the sensitivity analysis that selects most influential control parameters to optimize. Using the data generated from the obtained physical models, an equivalent NN formulation has finally been obtained that gives simple yet unified objectives and constraints ready to be used to solve the optimization problem that produces optimal tuning rules for the selected control parameters to minimize fuel consumption.

Authors:
ORCiD logo [1];  [2];  [1]
  1. ORNL
  2. Pacific Northwest National Laboratory (PNNL)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1566975
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: 2nd IEEE Connected and Automated Vehicles Symposium (IEEE CAVS 2019) - Honolulu, Hawaii, United States of America - 9/22/2019 12:00:00 PM-9/23/2019 12:00:00 PM
Country of Publication:
United States
Language:
English

Citation Formats

Hong, Wanshi, Chakraborty, Indrasis, and Wang, Hong. Parametric Optimization Problem Formulation for Connected Hybrid Electric Vehicles using Neural Network Based Equivalent Model. United States: N. p., 2019. Web.
Hong, Wanshi, Chakraborty, Indrasis, & Wang, Hong. Parametric Optimization Problem Formulation for Connected Hybrid Electric Vehicles using Neural Network Based Equivalent Model. United States.
Hong, Wanshi, Chakraborty, Indrasis, and Wang, Hong. Sun . "Parametric Optimization Problem Formulation for Connected Hybrid Electric Vehicles using Neural Network Based Equivalent Model". United States. https://www.osti.gov/servlets/purl/1566975.
@article{osti_1566975,
title = {Parametric Optimization Problem Formulation for Connected Hybrid Electric Vehicles using Neural Network Based Equivalent Model},
author = {Hong, Wanshi and Chakraborty, Indrasis and Wang, Hong},
abstractNote = {The dynamics of powertrain control systems are complicated and involve both nonlinear plant model and control functionalities, albeit they are well defined and formulated using first principle approaches. This constitutes difficulties in exploring implementable optimal tuning rules for some selected control parameters using vehicle-to-vehicle (V2V) communications. This paper presents a way to use neural networks (NN) to represent the problem of parameter tuning for optimizing fuel consumption. For this purpose, physical modelling and validation have been firstly performed for the closed loop powertrain system of the concerned vehicle for some given driving cycles. This is then followed by the sensitivity analysis that selects most influential control parameters to optimize. Using the data generated from the obtained physical models, an equivalent NN formulation has finally been obtained that gives simple yet unified objectives and constraints ready to be used to solve the optimization problem that produces optimal tuning rules for the selected control parameters to minimize fuel consumption.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {9}
}

Conference:
Other availability
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