期刊
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
卷 67, 期 2, 页码 1615-1625出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2019.2891466
关键词
eGaN HEMTs (enhancement-mode gallium nitride high electron mobility transistors); parameter influence; switching analytical model; synchronous buck converter
Low-voltage enhancement-mode gallium nitride high electron mobility transistors (eGaN HEMTs) are a kind of promising devices to realize high-efficiency and high-power-density conversion because of their fast switching speed, low conduction, and switching losses. Since the reverse conduction loss of eGaN HEMTs accounts for a large part of switching losses, it is of great importance to improve the traditional switching analytical model to predict the reverse conduction loss. In this paper, a complete and accurate switching analytical model of low-voltage eGaN HEMTs is presented, which considers the effects of low-parasitic inductances, nonlinear junction capacitances and nonlinear transconductances. The turn-oN and turn-oFF switching processes are described in detail, and the resulting equations are solved by using mathematical software such as MATLAB to predict the switching waveforms during the switching periods. And an accurate loss calculation method based on the proposed model is proposed including the reverse conduction loss. The accuracy of the proposed model is validated by comparing the predicted switching waveforms and the efficiency of a synchronous buck converter with the experimental results, respectively. Moreover, the proposed model is compared with the traditional model, which proves the advantages of the proposed model. In the end, according to the proposed complete model, the effect of the top switch (TS) gate resistance, the TS drain-source capacitance, power loop inductance, current ripple, the bottom switch (BS) gate resistance, the BS drain-source capacitance, and dead time on the loss breakdown of the synchronous buck converter is explored and some qualitative suggestions are given to improve the conversion efficiency based on these analyzing results.
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