Data-Driven Digital Twin for Reliability Assessment of DC/DC Buck Converter

In commercial applications, the operation of dc/dc converters significantly impacts overall system performance and long-term reliability. This study introduces a data-driven digital twin (DT) approach for estimating critical degradation parameters of dc/dc buck converter under the steady-state (SS) condition. Initially, a digital model circuit-level ( $\text {DM}_{\text {C}}$ ) is refined against a hardware prototype’s switching model dataset using offline particle swarm optimization (PSO). The optimized digital model’s SS response is then verified with its average model response while varying the duty and load. Subsequently, degradation profiles are imposed on the inductor, capacitor, and MOSFET in the $\text {DM}_{\text {C}}$ . A large dataset is generated from this model, allowing training, validation, and testing of machine learning (ML) models for component health regression tasks. The proposed method employs random forest (RF) ML models, achieving impressive regression results with a squared R value as high as 0.99978 and a root mean square error (RMSE) of $4.2 \times 10^{-6}$ . The method is further validated on a medium power level dc/dc buck prototype with varying load conditions and includes the analysis of MOSFET’son-resistance under degradation conditions. This data-driven DT method shows promise for identifying parasitic degradation and ohmic loss parameters, enhancing converter reliability assessments in a noninvasive, generalized, and computationally efficient manner.