A deep learning-based cooling system temperature prediction apparatus has an artificial neural network modeled by connecting a plurality of artificial neural network submodels each including an input layer, a hidden layer, and an output layer is used. A pump flow speed, a cooling water flow rate, a battery inlet cooling water temperature, a motor inlet cooling water temperature, a radiator outlet cooling water temperature, a battery temperature, and a motor temperature are predicted by inputting at least one of a predetermined control variable, an environment variable, or a time variable to the plurality of artificial neural network submodels in accordance with a physical causality. A number of the plurality of artificial neural network submodels and the control variables or environment variables that are sequentially input to each submodel depend on divisional control and integral control of the cooling system.


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    Titel :

    Deep learning-based cooling system temperature prediction apparatus according to physical causality and method therefor


    Beteiligte:
    KIM JAE WOONG (Autor:in) / LEE SANG SHIN (Autor:in) / CHUNG SO LA (Autor:in) / OH MAN JU (Autor:in) / KIM YOUNG JIN (Autor:in) / PARK JONG HYUN (Autor:in)

    Erscheinungsdatum :

    2023-10-24


    Medientyp :

    Patent


    Format :

    Elektronische Ressource


    Sprache :

    Englisch


    Klassifikation :

    IPC:    F04D Strömungsarbeitsmaschinen , NON-POSITIVE-DISPLACEMENT PUMPS / B60H ARRANGEMENTS OR ADAPTATIONS OF HEATING, COOLING, VENTILATING, OR OTHER AIR-TREATING DEVICES SPECIALLY FOR PASSENGER OR GOODS SPACES OF VEHICLES , Anordnung oder Einbau der Heizung, Kühlung, Lüftung oder anderer Luftbehandlungsvorrichtungen für die Fahrzeugräume für Reisende oder Fracht / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen



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