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1. Modeling and Characterization of 10-kV SiC MOSFET Modules for Medium-Voltage Distribution Systems

   https://doi.org/10.1109/PEDG48541.2020.9244411  

   Oggier, German G.; Jimenez, Roderick Gomez; Zhao, Yue; Balda, Juan Carlos (September 2020, 2020 IEEE 11th International Symposium on Power Electronics for Distributed Generation Systems (PEDG))

   This work presents the modeling and characterization of 10-kV SiC MOSFET modules used for medium-voltage distribution system applications. In addition to the nonlinear junction capacitances of the devices, the model includes the non-linearities present at steady-state like transfer characteristics and the behavior in the Ohmic region, which allows to increase the accuracy of the SiC MOSFET model. Furthermore, the parasitic inductances in the circuit (such as the source inductance shared by the power stage and driver loop and the drain inductance) are considered in the model since it has been demonstrated previously that it influences the total losses. By using the proposed model, the calculated voltage and current transients show a good match with the experimental results. 

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2. Modeling and Characterization of 10-kV SiC MOSFET Modules for Medium- Voltage Distribution Systems

   Oggiert, German G. (September 2020, IEEE International Symposium on Power Electronics for Distributed Generation Systems)

   null (Ed.)

   This work presents the modeling and characterization of 10-kV SiC MOSFET modules used for medium-voltage distribution system applications. In addition to the nonlinear junction capacitances of the devices, the model includes the nonlinearities present at steady-state like transfer characteristics and the behavior in the Ohmic region, which allows to increase the accuracy of the SiC MOSFET model. Furthermore, the parasitic inductances in the circuit (such as the source inductance shared by the power stage and driver loop and the drain Inductance) are considered in the model since it has been demonstrated previously that it influences the total losses. By using the proposed model, the calculated voltage and current transients show a good match with the experimental results. 

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3. A Wireless Power Transfer based Gate Driver Design for Medium Voltage SiC MOSFETs

   https://doi.org/10.1109/IFEEC53238.2021.9661739  

   Wei, Yuqi; Du, Liyang; Du, Xia; Machireddy, Venkata Samhitha; Mantooth, Alan (November 2021, IEEE International Future Energy Electronics Conference (IFEEC))

   Wide band gap (WBG) devices have been widely adopted in numerous industrial applications. In medium voltage applications, multi-level converters are necessary to reduce the voltage stress on power devices, which increases the system control complexity and reduces power density and reliability. High voltage silicon carbide (SiC) MOSFET enables the medium voltage applications with less voltage level, simple control strategy and high power density. Nevertheless, great challenges have been posed on the gate driver design for high voltage SiC MOSFET. Wireless power transfer (WPT) can achieve power conversion with large airgap, which can satisfy the system isolation requirement. Thus, in this article, a WPT based gate driver is designed for the medium voltage SiC MOSFET. The coil is optimized by considering voltage isolation, coupling capacitance, size, and efficiency. Experimental prototype was built and tested to validate the effectiveness of the proposed WPT based gate driver. 

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4. Novel Approach of Determining and Predicting SiC MOSFET’s On Resistance From Device Case Temperature Using Machine Learning

   https://doi.org/10.1109/APEC48143.2025.10977206  

   Bradford, Paul; Deppe, Conner; Wang, Hongjie (March 2025, IEEE)

   Silicon-carbide (SiC) MOSFET devices are increasing in popularity in high-power converter applications. Device on-resistance (Rdson) is an important indicator for SiC MOSFET health status. Increments in Rdson over device lifetime indicate imminent device failure and result in decreased system efficiency. Direct and accurate measurement of SiC MOSFET device Rdson in high-power applications is difficult. Another approach is to estimate/predict the device Rdson from other, more easily measurable quantities, however, little work has been done on this approach in the literature. This leaves a significant technical gap in measuring/predicting device Rdson and slows down the device health status monitoring and power converter reliability research. To address the technical gaps, this work proposes a novel approach to predicting device Rdson from thermal cycle count and instantaneous temperature using machine learning regression models. The actual hardware data collected from accelerated lifetime tests of high-power SiC MOSFETs are used to train, test, and validate the proposed machine-learning regression models. The developed models, coupled with cycle counting algorithms, and device case thermal measurements, provide accurate live estimates of Rdson and can be used to predict changes in Rdson over expected mission profiles during power converter design. 

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5. A High-Accuracy, Low-Order Thermal Model of SiC MOSFET Power Modules Extracted from Finite Element Analysis via Model Order Reduction

   https://doi.org/10.1109/ECCE.2019.8912839  

   Entzminger, Cameron; Qiao, Wei; Qu, Liyan; Hudgins, Jerry L. (September 2019, 2019 IEEE Energy Conversion Congress and Exposition (ECCE))

   Silicon carbide (SiC) MOSFET power modules are being used for high power applications because of their superior thermal characteristics and high blocking voltage capabilities over traditional silicon power modules. This paper explores modeling the thermal process of a SiC MOSFET power module through a high-order finite element analysis (FEA) based thermal model and then reducing the order of the FEA thermal model using a Krylov subspace method. The low-order thermal model has a significantly reduced computation cost compared to the FEA model while preserving the accuracy of the model. The proposed method is applied to generate low-order thermal models for a SiC MOSFET, which are validated by computer simulations with respect to the FEA thermal model. 

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