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Precision parameter estimation in Proton Exchange Membrane Fuel Cells using depth information enhanced Differential Evolution

Abstract Proton Exchange Membrane Fuel Cell (PEMFC) models require parameter tuning for their design and performance improvement. In this study, Depth Information-Based Differential Evolution (Di-DE) algorithm, a novel and efficient metaheuristic approach, is applied to the complex, nonlinear optimi...

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Principais autores: Pradeep Jangir, Absalom E. Ezugwu, Arpita, Sunilkumar P. Agrawal, Sundaram B. Pandya, Anil Parmar, G. Gulothungan, Laith Abualigah
Format: Artigo
Sprog:Inglês
Udgivet: Nature Portfolio 2024-11-01
Serier:Scientific Reports
Fag:
Parameter estimation
Proton Exchange Membrane Fuel Cell
PEMFC
Differential Evolution
Optimization
Online adgang:https://doi.org/10.1038/s41598-024-81160-0
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