The monitoring of temperature distribution is crucial for advanced battery thermal management. This study proposes a data-driven temperature field prediction method for the pouch cell thermal process, a typical distributed parameter system (DPS). First, empirical spatial basis functions (SBFs) that represent underlying spatial modes of the thermal system are extracted from data snapshots collected offline. Then, we apply the obtained SBFs to the time/space (T/S) separation framework and perform online nonlinear modeling using the partial-node feedback data. On this basis, a dynamics reconstruction strategy is designed for full-node temperature prediction. Experimental studies indicate that the proposed method owns encouraging accuracy and allows minimal sensing configuration. In addition, the error source of the proposed method is systematically analyzed.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Data-Driven Real-Time Prediction of Pouch Cell Temperature Field Under Minimal Sensing


    Beteiligte:
    Zhou, Yu (Autor:in) / Deng, Hua (Autor:in) / Li, Han-Xiong (Autor:in) / Xie, Sheng-Li (Autor:in)


    Erscheinungsdatum :

    2023-03-01


    Format / Umfang :

    3722756 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Real-Time Estimation of 2-D Temperature Distribution in Lithium-Ion Pouch Cells

    Sattarzadeh, Sara / Roy, Tanushree / Dey, Satadru | IEEE | 2021



    Data-Driven Real-Time Denied Boarding Prediction in Urban Railway Systems

    Tuncel, Kerem S. / Koutsopoulos, Haris N. / Ma, Zhenliang | Transportation Research Record | 2023

    Freier Zugriff

    Tab cooling for pouch cell

    SCHOENHERR ROBERT J | Europäisches Patentamt | 2020

    Freier Zugriff