A Semiphysical Semibehavioral Analytical Model for Switching Transient Process of SiC MOSFET Module

A semiphysical semibehavioral analytical model for switching transient of silicon carbide (SiC) MOSFET power module is proposed in this article. The model is developed for the applications in design automation where the tradeoff between accuracy and efficiency is important. The estimation of switchi...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE journal of emerging and selected topics in power electronics 2021-04, Vol.9 (2), p.2258-2270
Hauptverfasser: Sun, Jianning, Yuan, Liqiang, Duan, Renzhi, Lu, Zixian, Zhao, Zhengming
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:A semiphysical semibehavioral analytical model for switching transient of silicon carbide (SiC) MOSFET power module is proposed in this article. The model is developed for the applications in design automation where the tradeoff between accuracy and efficiency is important. The estimation of switching losses and voltage/current overshoots is mainly concerned in the proposed model. The switching transient is divided into subperiods according to the equivalent circuit of SiC MOSFET and the approximate physical mechanism during the process. The influence of the opposite device in the half-bridge is included in the analysis and corresponding derivations are made. Behavioral modeling of the waveforms is used in the subperiods where the mechanisms of the transient process are very complicated. The tradeoff between accuracy and efficiency is well implemented with this modeling approach. The time consumption of the proposed model is less than 50% of those of the previous analytical models and the accuracy is also acceptable. The errors on overshoots are less than 10% on average and the errors on switching losses are around 10%-20%. Experiment results are given and the effectiveness of the proposed model is proven.
ISSN:2168-6777
2168-6785
DOI:10.1109/JESTPE.2020.2992775