Complexity and nonlinearity of engines makes precise first principle engine models often difficult to obtain, as for instance for emissions. System identification is a well known possible alternative, successfully used in several automotive applications. In most cases system identification is concerned with the estimation of the unknown parameters of a known set of equations. Unfortunately, for many engine subsystems, there is no sufficiently precise or real time suitable model. This paper presents a sequential algorithm which allows to derive real time suitable models on line by a combination of model structure hypothesis of increasing complexity and an associated optimal input design and selection process. This paper introduces the method and shows its use both for a rather simple and a very difficult engine identification task, a dynamical model of the airpath of a Diesel engine and a dynamical model of nitrogen oxides and particulate matter.


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    Title :

    Sequential Identification of Engine Subsystems by Optimal Input Design


    Additional title:

    Sae Int. J. Engines


    Contributors:

    Conference:

    9th International Conference on Engines and Vehicles ; 2009


    Published in:

    Publication date :

    2009-09-13


    Size :

    8 pages




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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