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...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Energy conversion and management Ročník 236; s. 114063
Hlavní autoři:	Li, Hongwei, Xu, Boshi, Lu, Guolong, Du, Changhe, Huang, Na
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Oxford Elsevier Ltd 15.05.2021 Elsevier Science Ltd
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!