The paper focuses on the experimental identification and validation of recurrent neural networks (RNN) for virtual sensing of NO emissions in internal combustion engines (ICE). Suited training procedures and experimental tests are proposed to improve RNN precision and generalization in predicting NO formation dynamics. The reference Spark Ignition (SI) engine was tested by means of an integrated system of hardware and software tools for engine test automation and control strategies prototyping. A fast response analyzer was used to measure NO emissions at the exhaust valve. The accuracy of the developed RNN model is assessed by comparing simulated and experimental trajectories for a wide range of operating scenarios. The results evidence that RNN-based virtual NO sensor will offer significant opportunities for implementing on-board feedforward and feedback control strategies aimed at improving the performance of after-treatment devices.


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    Title :

    Development of recurrent neural networks for virtual sensing of NOx emissions in internal combustion engines


    Additional title:

    Sae Int. J. Fuels Lubr
    Sae International Journal of Fuels and Lubricants


    Contributors:

    Conference:

    9th International Conference on Engines and Vehicles ; 2009



    Publication date :

    2009-09-13


    Size :

    8 pages




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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