Multiobjective optimal choice and design of isolated dc-dc power converters

This paper presents a computer-aided design (CAD) tool for the design of isolated dc-dc converters. This tool, developed in Matlab environment, is based on multiobjective optimization (MO) using Genetic Algorithms (GAs). The Elitist Nondominated Sorting Genetic Algorithm (NSGA-II) is used to perform search and optimization whereas analytical models are used to model the power converters. The design problem requires minimizing the weight, losses and cost of the converter while ensuring the satisfaction of a number of constraints. The optimization variables are, as for them, the operating frequency, the current density, the maximum flux density, the transformer dimensions, the wire diameter, the core material, the conductor material, the converter topology (among Flyback, Forward, Push-Pull, half-bridge (HB) and full-bridge (FB) converters), the number of semiconductor devices associated in parallel, the number of cells (each of them corresponding to a topology) associated in serial or parallel as well as the kinds of input and output connections (serial or parallel) of these cells and the semiconductor devices (among diodes, IGBTs and MOSFETs). Finally, a design example is presented and the results show that such tool to design dc-dc power converters presents several advantages. In particular, it proposes to the designer a set of solutions — instead of a single one — so that he can choose a posteriori which solution best fits the application under consideration. Moreover, interesting solutions not considered a priori can be found with this tool.