A Multi-Agent Digital Twin Framework for Health Monitoring of SiC MOSFETs in Power Converters

Abstract

Modeling and monitoring advanced power semi-conductor packages are challenging due to their multiphysics behavior, structural complexity, and the high computational cost associated with detailed models. Conventional high-fidelity approaches, such as finite element models, provide high accuracy but are often unsuitable for applications like health monitoring, while purely data-driven models may lack physical interpretability and robustness under degradation.This paper proposes the concept of a multi-agent digital twin framework to address these challenges by decomposing the overall system into interconnected virtual domains with different roles and fidelity levels. The approach is demonstrated for health monitoring of a SiC MOSFET in a DC-DC buck converter. A three-agent architecture is implemented, consisting of a temporal ANN for system identification, a decision layer for failure detection and localization, and a parametric reduced-order thermal model for failure assessment. Experimental results show that the proposed framework can accurately identify system behavior, detect and localize failures, and quantify parameter variations associated with degradation.