Design of Resonant Converter with Genetic- Algorithm Optimization

This paper presents the genetic algorithm-based optimal design method of the resonant converters. The developed software finds an optimal parameter set of the resonant converter for achieving the rated output power with the highest power-conversion efficiency. The experimental results showed the validity and effectiveness of the proposed design strategy.

Authors

Institutions

Publication Details

Published
2024-08-19
DOI
https://doi.org/10.1109/isocc62682.2024.10762343
Primary Topic
Advanced DC-DC Converters
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Design of Resonant Converter with Genetic- Algorithm Optimization

Yutaro Komiyama, Hiroo Sekiya, Ayano Komanaka, Akihiro Konishi et al.
Advanced DC-DC Converters
article

Design of Resonant Converter with Genetic- Algorithm Optimization

Yutaro Komiyama, Hiroo Sekiya, Ayano Komanaka, Akihiro Konishi, Kien Nguyen, Yinchen Xie, Rintaro Kaneiso
article en

Abstract

This paper presents the genetic algorithm-based optimal design method of the resonant converters. The developed software finds an optimal parameter set of the resonant converter for achieving the rated output power with the highest power-conversion efficiency. The experimental results showed the validity and effectiveness of the proposed design strategy.

Chiba University (JP)
Openalex Percentile: Top 81%
Advanced DC-DC Converters
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.