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

Journal Article · Thu Jan 01 00:00:00 EST 2015 · IEEE Transactions on Control Systems Technology

DOI:https://doi.org/10.1109/TCST.2015.2454444· OSTI ID:1190741

Malikopoulos, Andreas ^[1]

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1. ORNL

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.

• Research Organization: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). National Transportation Research Center (NTRC)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE); USDOE Laboratory Directed Research and Development (LDRD) Program
• Grant/Contract Number: AC05-00OR22725
• OSTI ID: 1190741
• Alternate ID(s): OSTI ID: 1311247
• Journal Information: IEEE Transactions on Control Systems Technology, Vol. TBD, Issue TBD
• Country of Publication: United States
• Language: English

Related Subjects

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