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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Veröffentlicht in:	Energy conversion and management 2021-05, Vol.236, p.114063, Article 114063
Hauptverfasser:	Li, Hongwei, Xu, Boshi, Lu, Guolong, Du, Changhe, Huang, Na
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Catalysts Computational fluid dynamics Computer applications Decision analysis Ensemble learning model Fluid dynamics Fuel cells Fuel technology Genetic algorithms Hydrodynamics Mathematical models Multi-objective optimization Multiple objective analysis NSGA-II Optimization Parameters Pareto optimization Performance indices Proton exchange membrane fuel cell (PEMFC) Proton exchange membrane fuel cells Significant variables recognition Sorting algorithms Variance analysis
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