Electrical-Electronics functionality in vehicles is increasing. Higher complexity control systems are needed for managing the new functions and their supervision requirements by means of suitable Energy, Power & Load Management algorithms. Forecasting function's behavior trajectory in state-space is nowadays a powerful tool for high-complex control systems design. An intelligent embedded time-series signal processing approach, based on Artificial Neural Networks (ANN) for vehicle status definition, to implement Energy Power & Load management algorithms is presented. Supported by an ANN algorithm, a time-series prediction strategy is implemented by means of function's control algorithm review. Such a new function control module reports to the master algorithm an intelligent processed frame where on-line function diagnosis, report of short-time probable future function behavior and its associated probability are included. These new intelligently processed inputs allow the system to distinguish clearly short-term function status and thus, self-inducing transient preventive work modes in order to well manage forthcoming functions events.


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    Titel :

    An embedded time-series prediction strategy based on ANN for vehicle status definition for energy, power & load management


    Weitere Titelangaben:

    Fahrzeug-Energiemanagement auf Basis künstlicher neuronale Netze


    Beteiligte:
    Alvarez, I. (Autor:in) / Fontanilles, J. (Autor:in) / Mestre, J. (Autor:in) / Große, R. (Autor:in) / Blume, U. (Autor:in) / Srisurangkul, C. (Autor:in)


    Erscheinungsdatum :

    2003


    Format / Umfang :

    13 Seiten, 6 Bilder, 8 Quellen




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




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    Alvarez, I. / Fontanilles, J. / Mestre, J. et al. | British Library Conference Proceedings | 2003



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