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.


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    Title :

    Model predictive control of automotive powertrains - first experimental results


    Contributors:
    Saerens, B. (author) / Diehl, M. (author) / Swevers, J. (author) / Bulck, E. Van den (author)


    Publication date :

    2008


    Size :

    6 Seiten, 17 Quellen




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English






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