A Comprehensive Analytical Switching Transients and Loss Modeling Approach with Accurate Parasitic Parameters for Enhancement-Mode Gallium Nitride Transistors

Features
Authors Abstract
Content
To design better power converters with enhancement-mode Gallium Nitride high-electron-mobility transistor (eGaN HEMT) for emerging applications such as Electric Vehicles (EV), it is essential to model their switching transients and loss accurately. Analytical modeling has proved to be an effective approach to study the transistor’s dynamic behaviors and analyze the switching energy loss during the turn-on and turn-off transients. Furthermore, it helps to understand the essential factors that influence the switching transients and loss calculation. The accuracy of the analytical model mainly depends on the equivalent circuits and the parasitic parameters inside the transistor packaging and external circuits under different switching stages. It is always challenging to extract the parasitic parameters accurately due to its natural character of nonlinearity and complex correlation during the switching transients. In this article, a comprehensive analytical model is proposed considering both transistors in the same bridge-leg and all necessary parameters that potentially affect the switching transients, especially when the unique reverse conduction of eGaN HEMT happens. New parasitic extraction methods are utilized and evaluated within the proposed model. Detailed stages of turn-on and off transients are also presented and verified against simulation program with integrated circuit emphasis (SPICE) simulation and experiment. In the end, the proposed model is applied for accurate switching loss calculations.
Meta TagsDetails
DOI
https://doi.org/10.4271/14-11-01-0010
Pages
13
Citation
Tian, J., Lai, C., Luo, Y., Turco, S. et al., "A Comprehensive Analytical Switching Transients and Loss Modeling Approach with Accurate Parasitic Parameters for Enhancement-Mode Gallium Nitride Transistors," SAE Int. J. Elec. Veh. 11(1):133-146, 2022, https://doi.org/10.4271/14-11-01-0010.
Additional Details
Publisher
Published
Sep 27, 2021
Product Code
14-11-01-0010
Content Type
Journal Article
Language
English