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Title: Model predictive control of automotive powertrains - First Experimental results
Authors: Saerens, Bart
Van den Bulck, Eric
Swevers, Jan
Diehl, Moritz
Issue Date: Dec-2008
Host Document: Proceedings of the 47th IEEE Conference on Decision and Control pages:5692-5697
Conference: 47th IEEE Conference on Decision and Control location:Cancun, Mexico date:9-11 December 2008
Abstract: This paper illustrates the capabilities of model predictive control for the control of automotive powertrains. We consider the minimization of the fuel consumption of a gasoline engine through dynamic optimization. The minimization uses a mean value model of the powertrain and vehicle. This model has two state variables: the pressure in the engine manifold and the engine speed. The control input is the throttle valve angle. The model is identified on a universal dynamometer. Optimal state and control trajectories are calculated using Bock’s direct multiple shooting method implemented in the software MUSCOD-II. The developed approach is illustrated both in simulation and experimentally for a test case where a vehicle accelerates from 1100 rpm to 3700 rpm in 30 s. The optimized trajectories yield minimal fuel consumption. The experiments show that the optimal engine speed trajectory yields a reduction of the fuel consumption of 12% when compared to a linear trajectory. Thus, it is shown that, even with a simple model, a significant amount of fuel can be saved without loss of the fun-to-drive.
ISBN: 978-1-4244-3124-3
ISSN: 0191-2216
Publication status: published
KU Leuven publication type: IC
Appears in Collections:Applied Mechanics and Energy Conversion Section
Production Engineering, Machine Design and Automation (PMA) Section
Electrical Engineering - miscellaneous

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