Pareto Efficient Policy for Supervisory Power Management Control

Conference · Thu Jan 01 04:00:00 EST 2015

DOI:https://doi.org/10.1109/ITSC.2015.457· OSTI ID:1186010

Malikopoulos, Andreas ^[1]

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1. ORNL

n this paper we address the problem of online optimization of the supervisory power management control in parallel hybrid electric vehicles (HEVs). We model HEV opera- tion 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.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). National Transportation Research Center (NTRC)
• Sponsoring Organization: ORNL LDRD Seed-Money
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 1186010
• Country of Publication: United States
• Language: English

Related Subjects

Markov chain
Power management control
parallel HEVs
stochastic optimal control