An Intelligent System for Automatic Selection of DC-DC Converter Topology with Optimal Design

Song Wang, Yi Murphey, Wencong Su, Mengqi Wang, VAN HAI BUI, Fangyuan Chang, Can Huang, Lingxiao Xue, Felipe Leno da Silva, Ruben Glatt

Keywords: Electrical power converter design, decision tree learning, reinforcement learning, surrogate models, topology recommendation, converter optimization

Abstract: In this paper, we present an intelligent system that has the capabilities of automatically selecting topology classes and optimizing circuit parameters of DC-DC power converters for a given design specification. The system, Machine-Learning-enhanced Automated Circuit Configuration and Evaluation of Power Converters (ML-ACCEPT), uses a hybrid of machine learning technologies, decision tree inference, reinforcement learning and deep neural networks. The system gives high accurate recommendations of design topology classes and computationally efficient results in optimizing power efficiencies in power converter design.