Proton Exchange Membrane Fuel Cell Prognostics Using Genetic Algorithm and Extreme Learning Machine

Fuel Cells ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 263-271 ◽  
Author(s):  
K. Chen ◽  
S. Laghrouche ◽  
A. Djerdir
Keyword(s):  
Genetic Algorithm ◽  
Fuel Cell ◽  
Extreme Learning Machine ◽  
Proton Exchange Membrane ◽  
Proton Exchange ◽  
Learning Machine ◽  
Exchange Membrane

scholarly journals Optimization of high-temperature proton exchange membrane fuel cell flow channel based on genetic algorithm

2021 ◽  
Vol 7 ◽  
pp. 1374-1384 ◽  
Author(s):  
Taiming Huang ◽  
Wei Wang ◽  
Yao Yuan ◽  
Jie Huang ◽  
Xi Chen ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
High Temperature ◽  
Fuel Cell ◽  
Proton Exchange Membrane ◽  
Flow Channel ◽  
Proton Exchange ◽  
Exchange Membrane

Modeling of Proton Exchange Membrane Fuel Cell (PEMFC) Performance by Using Genetic Algorithm-Polynomial Neural Network (GA-PNN) Hybrid System

2012 ◽  
Author(s):  
Mehdi Mehrabi ◽  
Sajad Rezazadeh ◽  
Mohsen Sharifpur ◽  
Josua P. Meyer
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fuel Cell ◽  
Relative Humidity ◽  
Proton Exchange Membrane ◽  
Proton Exchange ◽  
Cathode Side ◽  
Data Set ◽  
Polynomial Neural Network ◽  
Exchange Membrane

In the present study, a genetic algorithm-polynomial neural network (GA-PNN) was used for modeling proton exchange membrane fuel cell (PEMFC) performance, based on some numerical results which were correlated with experimental data. Thus, the current density was modeled in respect of input (design) variables, i.e., the variation of pressure at the cathode side, voltage, membrane thickness, anode transfer coefficient, relative humidity of inlet fuel and relative humidity of inlet air. The numerical data set for the modeling was divided into train and test sections. The GA-PNN model was introduced with 80% of the numerically-validated data and the remaining data was used for testing the appropriateness of the GA-PNN model by means of two statistical criteria.

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