A Multiobjective Optimization Framework for Online Stochastic Optimal Control in Hybrid Electric Vehicles [electronic resource].
- Washington, D.C. : United States. Dept. of Energy, 2015.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy
- Physical Description:
- pages 440-450 : digital, PDF file
- Additional Creators:
- Oak Ridge National Laboratory
United States. Department of Energy
United States. Department of Energy. Office of Scientific and Technical Information
- 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.
- Published through SciTech Connect.
IEEE Transactions on Control Systems Technology 24 2 ISSN 1063-6536 AM
Andreas A. Malikopoulos.
- Funding Information:
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