A Multiobjective Optimization Framework for Online Stochastic Optimal Control in Hybrid Electric Vehicles

Malikopoulos, Andreas

doi:10.1109/TCST.2015.2454444

Title: A Multiobjective Optimization Framework for Online Stochastic Optimal Control in Hybrid Electric Vehicles

Abstract

The increasing urgency to extract additional efficiency from hybrid propulsion systems has led to the development of advanced power management control algorithms. For this research, 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 and show that the control policy yielding the Pareto optimal solution minimizes online the long-run expected average cost per unit time criterion. The effectiveness of the proposed solution is validated through simulation and compared with the solution derived with dynamic programming using the average cost criterion. Finally, both solutions achieved the same cumulative fuel consumption demonstrating that the online Pareto control policy is an optimal control policy.

Authors:

Malikopoulos, Andreas ^[1]

ORNL

Publication Date:: Thu Jan 01 00:00:00 EST 2015

Research Org.:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). National Transportation Research Center (NTRC)

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE); USDOE Laboratory Directed Research and Development (LDRD) Program

OSTI Identifier:: 1190741

Alternate Identifier(s):: OSTI ID: 1311247

Grant/Contract Number:: AC05-00OR22725

Resource Type:: Accepted Manuscript

Journal Name:: IEEE Transactions on Control Systems Technology

Additional Journal Information:: Journal Volume: TBD; Journal Issue: TBD

Country of Publication:: United States

Language:: English

Subject:: 33 ADVANCED PROPULSION SYSTEMS; stochastic optimal control; Markov chain; parallel hybrid electric vehicle; Pareto optimal solution; Pareto control policy; 32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; 42 ENGINEERING; Hybrid electric vehicles (HEVs); multiobjective optimization; power management control

Citation Formats


                    Malikopoulos, Andreas. A Multiobjective Optimization Framework for Online Stochastic Optimal Control in Hybrid Electric Vehicles.  United States: N. p., 2015. 
Web.  doi:10.1109/TCST.2015.2454444.

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                    Malikopoulos, Andreas. A Multiobjective Optimization Framework for Online Stochastic Optimal Control in Hybrid Electric Vehicles.  United States.  https://doi.org/10.1109/TCST.2015.2454444

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                    Malikopoulos, Andreas. Thu .  
"A Multiobjective Optimization Framework for Online Stochastic Optimal Control in Hybrid Electric Vehicles".  United States.  https://doi.org/10.1109/TCST.2015.2454444.  https://www.osti.gov/servlets/purl/1190741.

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@article{osti_1190741,

  title        = {A Multiobjective Optimization Framework for Online Stochastic Optimal Control in Hybrid Electric Vehicles},

  author       = {Malikopoulos, Andreas},

  abstractNote = {The increasing urgency to extract additional efficiency from hybrid propulsion systems has led to the development of advanced power management control algorithms. For this research, 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 and show that the control policy yielding the Pareto optimal solution minimizes online the long-run expected average cost per unit time criterion. The effectiveness of the proposed solution is validated through simulation and compared with the solution derived with dynamic programming using the average cost criterion. Finally, both solutions achieved the same cumulative fuel consumption demonstrating that the online Pareto control policy is an optimal control policy.},

  doi          = {10.1109/TCST.2015.2454444},

  journal      = {IEEE Transactions on Control Systems Technology},

  number       = TBD,

  volume       = TBD,

  place        = {United States},

  year         = {Thu Jan 01 00:00:00 EST 2015},

  month        = {Thu Jan 01 00:00:00 EST 2015}

}

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https://doi.org/10.1109/TCST.2015.2454444

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Works referencing / citing this record:

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