Thermo-Electrical Digital Twin-Assisted Aging Assessment of SiC MOSFETs in Switching-Cell-Array Power Converters

Accurate aging assessment of switching power semiconductors in power converters is essential due to their critical influence on converter reliability and operational lifespan. Traditional aging evaluation methods rely on intrusive sensors embedded within the power devices, which complicate electromagnetic interference management and limit the practicality of real-time monitoring. This paper proposes a comprehensive digital-twin framework and a simple algorithm to assess the state of health (SOH) of SiC power devices in switching-cell-array based power converters. The digital twin replicates the system electro-thermal behavior from the existing converter control current and voltage measurements and the modulation parameters. The algorithm estimates the power devices on-state resistance through the digital twin outputs and easy-to-integrate temperature sensors within the converter leg printed-circuit board. The on-state resistance provides a direct estimation of the power devices SOH. This framework delivers real-time insights into the converter SOH and degradation patterns, allowing predictive maintenance strategies. The proposed health assessment strategy is validated through simulations.