Multi-objective optimization of PEM fuel cell by coupled significant variables recognition, surrogate models and a multi-objective genetic algorithm

•A fast and systematic multi-objective optimization framework is proposed.•Power density, efficiency and cathode O2 uniformity are optimized simultaneously.•Decision variables used for optimization are determined by variance analysis method.•Data-driven surrogate model and multi-optimization algorit...

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Bibliographic Details
Published in:	Energy conversion and management Vol. 236; p. 114063
Main Authors:	Li, Hongwei, Xu, Boshi, Lu, Guolong, Du, Changhe, Huang, Na
Format:	Journal Article
Language:	English
Published:	Oxford Elsevier Ltd 15.05.2021 Elsevier Science Ltd
Subjects:	administrative management Algorithms Catalysts cathodes Computational fluid dynamics Computer applications Decision analysis energy conversion Ensemble learning model Fluid dynamics fluid mechanics Fuel cells Fuel technology Genetic algorithms Hydrodynamics Mathematical models Multi-objective optimization Multiple objective analysis NSGA-II Optimization oxygen Parameters Pareto optimization Performance indices Proton exchange membrane fuel cell (PEMFC) Proton exchange membrane fuel cells Significant variables recognition Sorting algorithms variance Variance analysis NSGA-II Multi-objective optimization Proton exchange membrane fuel cell (PEMFC) Ensemble learning model Significant variables recognition
ISSN:	0196-8904, 1879-2227
Online Access:	Get full text
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