Neural Network Modeling for Proton Exchange Membrane Fuel Cell (PEMFC)

2010 ◽  
Vol 2 (2) ◽  
pp. 61-66
Author(s):  
M. ELSayed Youssef ◽  
Moataz H.Khalil ◽  
Khairia E.ALNAdi
Keyword(s):  
Neural Network ◽  
Fuel Cell ◽  
Proton Exchange Membrane ◽  
Network Modeling ◽  
Proton Exchange ◽  
Neural Network Modeling ◽  
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.

scholarly journals A Neural Networks based Predictive Voltage-Tracking Controller Design for Proton Exchange Membrane Fuel Cell Model

2019 ◽  
Vol 25 (12) ◽  
pp. 26-48 ◽  
Author(s):  
Ahmed Sabah Al-Araji ◽  
Hayder A. Dhahad ◽  
Essra A. Jaber
Keyword(s):  
Neural Network ◽  
Fuel Cell ◽  
Proton Exchange Membrane ◽  
Cell Model ◽  
Proton Exchange ◽  
Fitness Evaluation ◽  
Fast Tracking ◽  
Neural Network Controller ◽  
On Line ◽  
Exchange Membrane

In this work, a new development of predictive voltage-tracking control algorithm for Proton Exchange Membrane Fuel Cell (PEMFCs) model, using a neural network technique based on-line auto-tuning intelligent algorithm was proposed. The aim of proposed robust feedback nonlinear neural predictive voltage controller is to find precisely and quickly the optimal hydrogen partial pressure action to control the stack terminal voltage of the (PEMFC) model for N-step ahead prediction. The Chaotic Particle Swarm Optimization (CPSO) implemented as a stable and robust on-line auto-tune algorithm to find the optimal weights for the proposed predictive neural network controller to improve system performance in terms of fast-tracking desired voltage and less energy consumption through investigating and comparing under random current variations with the minimum number of fitness evaluation less than 20 iterations.

FAULT DETECTION OF PEM FUEL CELL FOR VEHICLE SYSTEMS USING NEUTRAL NETWORK MODELS

2015 ◽  
Vol 76 (8) ◽  
Author(s):  
Mahanijah Md Kamal ◽  
Dingli Yu
Keyword(s):  
Neural Network ◽  
Fuel Cell ◽  
Fault Detection ◽  
Proton Exchange Membrane ◽  
Network Models ◽  
Open Loop ◽  
Proton Exchange ◽  
Neural Network Modeling ◽  
Data Sets ◽  
Vehicle Systems

This paper presents the neural network modeling method to perform fault detection for proton exchange membrane fuel cell dynamic systems under an open-loop scheme. These methods use a radial basis function neural network and a multilayer perceptron neural network to perform fault identification. Five types of faults which commonly happened in the vehicle systems have been introduced to the modified benchmark model developed by Michigan University. The developed algorithm of RBF and MLP network models are implemented on Matlab/Simulink environment using the healthy data sets and faulty data sets obtained from the simulation. All five simulated faults have been successfully detected where the residual is designed sensitive to fault amplitude as low as +10% of their nominal values. Thus, it is possible to apply the developed algorithm to real dynamics system of vehicles for monitoring and maintenance purposes.

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