Abstract

This paper proposes an active learning framework to address a longstanding research question: how much data is needed for data-driven power electronics designs. The proposed method can automatically explore the optimal scenario using minimal data while achieving the desired accuracy. To demonstrate the proposed method, the reliability-oriented design (ROD) for a traction converter is used as a case study. While traditional methods achieving ROD necessitate extensive simulation and experiments, the proposed method uses surrogate models and active learning to achieve the desired accuracy with the minimal computational requirements. The performance of the proposed method has been verified by experimental measurements. The computation cost is reduced by 62.5% compared to the traditional ROD method.