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Title: Centralized Stochastic Optimal Control of Complex Systems

Abstract

In this paper we address the problem of online optimization of the supervisory power management control in parallel hybrid electric vehicles (HEVs). We model HEV operation as a controlled Markov chain using the long-run expected average cost per unit time criterion, and we show that the control policy yielding the Pareto optimal solution minimizes the average cost criterion online. The effectiveness of the proposed solution is validated through simulation and compared to the solution derived with dynamic programming using the average cost criterion.

Authors:
 [1]
  1. ORNL
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). National Transportation Research Center (NTRC)
Sponsoring Org.:
USDOE Laboratory Directed Research and Development (LDRD) Program
OSTI Identifier:
1185343
DOE Contract Number:
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: 2015 European Control Conference, Linz, IL, Austria, 20150715, 20150717
Country of Publication:
United States
Language:
English
Subject:
complex systems; stochastic optimal control; Markov chain

Citation Formats

Malikopoulos, Andreas. Centralized Stochastic Optimal Control of Complex Systems. United States: N. p., 2015. Web.
Malikopoulos, Andreas. Centralized Stochastic Optimal Control of Complex Systems. United States.
Malikopoulos, Andreas. Thu . "Centralized Stochastic Optimal Control of Complex Systems". United States. doi:. https://www.osti.gov/servlets/purl/1185343.
@article{osti_1185343,
title = {Centralized Stochastic Optimal Control of Complex Systems},
author = {Malikopoulos, Andreas},
abstractNote = {In this paper we address the problem of online optimization of the supervisory power management control in parallel hybrid electric vehicles (HEVs). We model HEV operation as a controlled Markov chain using the long-run expected average cost per unit time criterion, and we show that the control policy yielding the Pareto optimal solution minimizes the average cost criterion online. The effectiveness of the proposed solution is validated through simulation and compared to the solution derived with dynamic programming using the average cost criterion.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Jan 01 00:00:00 EST 2015},
month = {Thu Jan 01 00:00:00 EST 2015}
}

Conference:
Other availability
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