TY  - JOUR
AB  - Purpose– This paper presents a computer‐aided design (CAD) tool for the design of isolated dc‐dc converters.Design/methodology/approach– This tool, developed in Matlab environment, is based on multiobjective optimization (MO) using genetic algorithms. The Elitist Nondominated Sorting Genetic Algorithm 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 and full‐bridge topologies), the number of semiconductor devices associated in parallel, the number of cells associated in series or parallel as well as the kinds of input and output connections (serial or parallel) of these cells. Finally, the design of an auxiliary railway power supply is presented and discussed.Findings– 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.Originality/value– To the best of the authors’ knowledge, such a CAD tool including a MO procedure taking several topologies into account has not been suggested so far.
VL  - 31
IS  - 2
SN  - 0332-1649
DO  - 10.1108/03321641211200590
UR  - https://doi.org/10.1108/03321641211200590
AU  - Versèle Christophe
AU  - Deblecker Olivier
AU  - Lobry Jacques
ED  - Xose M. Lopez‐Fernandez
PY  - 2012
Y1  - 2012/01/01
TI  - A computer‐aided design tool dedicated to isolated DC‐DC converters based on multiobjective optimization using genetic algorithms
T2  - COMPEL - The international journal for computation and mathematics in electrical and electronic engineering
PB  - Emerald Group Publishing Limited
SP  - 583
EP  - 603
Y2  - 2024/04/18
ER  -