Machine Learning-Based Optimization of Synchronous Rectification Low-Inductance Current Secondary Boost Converter (SLIC-QBC)

For recycling waste batteries, high-gain DC-DC provides a great solution. In this article, the design and optimization of converter–synchronous rectification low-inductance current secondary boost converter (SLIC-QBC) is studied. The objective article to propose an automatic method for passive components switching frequency improve efficiency power density. A machine learning-integrated proposed minimize mass loss converter. method, first, component-based model built with embedded SLIC-QBC simulation. Then, series schemes generated within large parameter range, training data learning are collected. Support vector (SVM) artificial neural network (ANN) used validate scheme, where ANN establishes mapping relationship from parameters objectives. After optimization, experimental prototype verification. simulation results verify practicability method.