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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. 

As the widespread adoption of electric vehicles (EVs) keeps increasing, EV charger reliability is becoming critical to provide a satisfactory charging experience for EV users. Wide-bandgap semiconductors such as silicon Carbide (SiC) MOSFETs have been widely deployed in EV chargers for high efficiency, high power density, and thermal capabilities. However, the aging of SiC MOSFETs has not been fully studied with available aging data lacking significantly in the literature. This paper addresses the EV charger reliability problem by developing a new aging test platform for SiC MOSFETs that are commonly used in various EV chargers, collecting aging data with analysis to provide a new understanding of SiC MOSFET aging, and providing new insights into online EV charger health monitoring system design and development.