LED driver application asks for a high power level operating at a high switching frequency. This feature is achievable by adopting gallium nitride transistors (eGaN FETs) in the DC-DC converter. In the paper, a Synchronous Buck Converter (SBC) with high current density GaN FETs is investigated and modelled to design a high brightness LED driver. At first, the GaN FET model is described and implemented in LTspice simulation software. Then, the synchronous Buck operation is considered to investigate the proposed model in actual application. The experimental waveforms of a half-bridge evaluation board in SBC configuration and a comparison with the simulation results are presented and discussed to validate the modelling approach.

Modelling and Experimental Validation of GaN Based Power Converter for LED Driver / Barba, V.; Solimene, L.; Palma, M.; Musumeci, S.; Ragusa, C. S.; Bojoi, R.. - ELETTRONICO. - (2022), pp. 1-6. (Intervento presentato al convegno 2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)) [10.1109/EEEIC/ICPSEurope54979.2022.9854660].

Modelling and Experimental Validation of GaN Based Power Converter for LED Driver

Barba, V.;Solimene, L.;Musumeci, S.;Ragusa, C. S.;Bojoi, R.
2022

Abstract

LED driver application asks for a high power level operating at a high switching frequency. This feature is achievable by adopting gallium nitride transistors (eGaN FETs) in the DC-DC converter. In the paper, a Synchronous Buck Converter (SBC) with high current density GaN FETs is investigated and modelled to design a high brightness LED driver. At first, the GaN FET model is described and implemented in LTspice simulation software. Then, the synchronous Buck operation is considered to investigate the proposed model in actual application. The experimental waveforms of a half-bridge evaluation board in SBC configuration and a comparison with the simulation results are presented and discussed to validate the modelling approach.
2022
978-1-6654-8537-1
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2979978
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