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  • 标题:Prediction and optimization of fuel cell performance using a multi-objective genetic algorithm
  • 本地全文:下载
  • 作者:Gustavo Marques Hobold ; Ramesh K. Agarwal
  • 期刊名称:International Journal of Energy and Environment
  • 印刷版ISSN:2076-2895
  • 电子版ISSN:2076-2909
  • 出版年度:2013
  • 卷号:4
  • 期号:5
  • 页码:721-742
  • 出版社:International Energy and Environment Foundation (IEEF)
  • 摘要:The attention that is currently being given to the emission of pollutant gases in the atmosphere has made the fuel cell (FC), an energy conversion device that cleanly converts chemical energy into electrical energy, a good alternative to other technologies that still use carbon-based fuels. The temperature plays an important role on the efficiency of an FC as it influences directly the humidity of the membrane, the reversible thermodynamic potential and the partial pressure of water; therefore the thermal control of the fuel cell is the focus of this paper. We present models for both high and low temperature fuel cells based on the solid-oxide fuel cell (SOFC) and the polymer electrolyte membrane fuel cell (PEMFC). A thermodynamic analysis is performed on the cells and the methods of controlling their temperature are discussed. The cell parameters are optimized for both high and low temperatures using a Java-based multi-objective genetic algorithm, which makes use of the logic of the biological theory of evolution to classify individual parameters based on a fitness function in order to maximize the power of the fuel cell. Applications to high and low temperature fuel cells are discussed.

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