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High Frequency Design and Efficiency Optimization of LLC Resonant Converter Based on SiC Devices
- 1 Changchun University of Technology, Changchun City, China
* Author to whom correspondence should be addressed.
Abstract
With the rapid development of power electronics technology, the demand for power converters with high efficiency, high power density and high frequency response capability is increasing. In recent years, silicon carbide (SiC) devices have become a key material to meet this demand due to their excellent electrical characteristics. SiC devices not only operate stably at higher voltages and temperatures, but also have higher switching frequencies, making them ideal for high frequency and efficient power converter designs. LLC resonant converters are widely used in high power converters because of their high efficiency, soft switching characteristics and excellent voltage and current control performance. The introduction of SiC devices into the LLC resonant converter can not only improve the overall efficiency of the system, but also further reduce the volume and increase the power density under high frequency conditions. In this paper, the basic characteristics and advantages of SiC devices are analyzed, and the feasibility of their application in LLC resonant converters is described. Secondly, combined with high frequency conditions, the selection strategy of SiC devices is proposed, including voltage resistance, on-resistance, switching speed, thermal performance and other considerations. Finally, aiming at the application of SiC devices in LLC resonant converters, some efficiency optimization schemes are proposed to help improve the overall performance of the system.
Keywords
SiC device, LLC resonant converter, High frequency design, Efficiency optimization
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Cite this article
Chen,B. (2025). High Frequency Design and Efficiency Optimization of LLC Resonant Converter Based on SiC Devices. Applied and Computational Engineering,145,176-181.
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Volume title: Proceedings of the 3rd International Conference on Software Engineering and Machine Learning
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