Artificial Intelligence-Aided Thermal Model Considering Cross-Coupling Effects

Yi Zhang, Zhongxu Wang, Huai Wang, Frede Blaabjerg

    Research output: Contribution to journalArticlepeer-review

    33 Scopus citations

    Abstract

    This letter proposes an artificial intelligence-aided thermal model for power electronic devices/systems considering thermal cross-coupling effects. Since multiple heat sources can be applied simultaneously in the thermal system, the proposed method is able to characterize model parameters more conveniently compared to existing methods where only single heat source is allowed at a time. By employing simultaneous cooling curves, linear-to-logarithmic data re-sampling, and differentiated power losses, the proposed artificial neural network-based thermal model can be trained with better data richness and diversity while using fewer measurements. Finally, experimental verifications are conducted to validate the model capabilities.

    Original languageBritish English
    Article number9034112
    Pages (from-to)9998-10002
    Number of pages5
    JournalIEEE Transactions on Power Electronics
    Volume35
    Issue number10
    DOIs
    StatePublished - Oct 2020

    Keywords

    • Artificial intelligence
    • power electronic devices and systems
    • thermal cross-coupling effects
    • thermal modeling

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