{"title":"A Modified Multi-group DNA Genetic Algorithm for Parameter Estimation of Proton Exchange Membrane Fuel Cell Model","authors":"Huizhen Lv, Duan Zhang","doi":"10.1109/ISCID.2014.100","DOIUrl":null,"url":null,"abstract":"The accurate electrochemical model is of great significance for the simulation and design of fuel cell power systems. In order to estimate parameters of the proton exchange membrane fuel cell (PEMFC) model, a modified multi-group DNA genetic algorithm (MMDNA-GA) which is inspired by the mechanism of biological DNA is proposed. In MMDNA-GA, three new crossover operators and three adaptive mutation operators are developed for improving the global searching ability. To enhance population diversity and overcome premature convergence of the algorithm, the multi-group inter-generational integration evolutionary strategy is adopted. The experimental results in different search ranges and validate strategies reveal that MMDNA-GA is a helpful and reliable technique for parameter estimation problem of PEMFC.","PeriodicalId":385391,"journal":{"name":"2014 Seventh International Symposium on Computational Intelligence and Design","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 Seventh International Symposium on Computational Intelligence and Design","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCID.2014.100","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The accurate electrochemical model is of great significance for the simulation and design of fuel cell power systems. In order to estimate parameters of the proton exchange membrane fuel cell (PEMFC) model, a modified multi-group DNA genetic algorithm (MMDNA-GA) which is inspired by the mechanism of biological DNA is proposed. In MMDNA-GA, three new crossover operators and three adaptive mutation operators are developed for improving the global searching ability. To enhance population diversity and overcome premature convergence of the algorithm, the multi-group inter-generational integration evolutionary strategy is adopted. The experimental results in different search ranges and validate strategies reveal that MMDNA-GA is a helpful and reliable technique for parameter estimation problem of PEMFC.
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