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Hybrid data-driven modelling methodology for fast and accurate transient simulation of SiC MOSFETs

Yang, Peng, Ming, Wenlong, Liang, Jun, Ludtke, Ingo, Berry, Steve and Floros, Konstantinos 2022. Hybrid data-driven modelling methodology for fast and accurate transient simulation of SiC MOSFETs. IEEE Transactions on Power Electronics 37 (1) , pp. 440-451. 10.1109/TPEL.2021.3101713

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Abstract

To enable fast and accurate models of SiC MOSFETs for transient simulation, a hybrid data-driven modeling methodology of SiC MOSFETs is proposed. Unlike conventional modeling methods that are based on complex nonlinear equations, data-driven Artificial Neural Networks (ANNs) are used in this paper. For model accuracy, the I-V characteristics are measured in the whole operation region to train the ANN. The ANN model is then combined with behavior-based equations to model the cutoff region and to avoid overfitting the ANN. In addition, the C-V characteristics are modeled by ANNs with a logarithmic scale for accuracy. The proposed model is implemented and simulated in SPICE simulator SIMetrix. The simulation results are compared with experimental results from a double-pulse tester to validate the proposed modeling methodology. The model is also compared with the Angelov model created by the Keysight MOSFET modeling software. The comparison results show that the proposed model is more accurate than the Angelov model. Besides, when compared to the Angelov model, the proposed model requires 30% less computation time when simulating a double pulse tester. In addition, the proposed modeling method also has better adaptability to model different types of SiC MOSFETs.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Additional Information: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
Publisher: Institute of Electrical and Electronics Engineers
ISSN: 0885-8993
Date of First Compliant Deposit: 9 September 2021
Date of Acceptance: 16 July 2021
Last Modified: 20 Dec 2021 11:45
URI: https://orca.cardiff.ac.uk/id/eprint/143507

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