Investigation of Dead Time Losses in Inverter Switching Leg Operation: GaN FET vs. MOSFET Comparison

Vincenzo Barba, Salvatore Musumeci (), Fausto Stella, Fabio Mandrile and Marco Palma ()
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Vincenzo Barba: Energy Department DENERG, Power Electronics Innovation Center PEIC, Politecnico Torino, 10129 Torino, Italy
Salvatore Musumeci: Energy Department DENERG, Power Electronics Innovation Center PEIC, Politecnico Torino, 10129 Torino, Italy
Fausto Stella: Energy Department DENERG, Power Electronics Innovation Center PEIC, Politecnico Torino, 10129 Torino, Italy
Fabio Mandrile: Energy Department DENERG, Power Electronics Innovation Center PEIC, Politecnico Torino, 10129 Torino, Italy
Marco Palma: Efficient Power Conversion Corporation, 10121 Torino, Italy

Energies, 2024, vol. 17, issue 15, 1-18

Abstract: This paper investigates the commutation transients of MOSFET and GaN FET devices in motor drive applications during hard-switching and soft-switching commutations at dead time operation. This study compares the switching behaviors of MOSFETs and GaN FETs, focusing on their performance during dead time in inverter legs for voltage source inverters. Experimental tests at various phase current levels reveal distinct switching characteristics and energy dissipation patterns. A validated simulation model estimates the experimental energy exchanged and dissipated during switching transients. The results demonstrate that GaN FETs exhibit lower overall losses at shorter dead times compared to MOSFETs, despite higher reverse conduction voltage drops. The study provides a quantitative framework for selecting optimal dead times to minimize energy losses, enhancing the efficiency of GaN FET-based inverters in low-voltage motor drive applications. Finally, a dead time optimization strategy is proposed and described.

Keywords: motor drive; inverter leg; dead time; switching losses; reverse conduction; GaN FET; MOSFET (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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