Modeling and experimental verification of a 25W fabricated PEM fuel cell by parametric and GMDH-type neural network

Abstract

In this paper two artificial intelligence techniques to predict and control behavior of a 25W fabricated proton exchange membrane (PEM) fuel cell, have been investigated. These approaches are: "Parametric Neural Network (PNN)" and "Group Method of Data Handling (GMDH)" for the first time. A PNN model is developed by introducing a "p" parameter in the activation function of the neural network. PNN model with its specific tangent hyperbolic transfer function have the ability to be with different nonlinearity degrees of input data. To develop GMDH network, quadratic polynomial was utilized. To determine proper weights of GMDH network, back propagation algorithm has been used. The input layer consists of gas pressure, fuel cell temperature and input current experimental data, to predict the output voltage. The results show that both generalized Parametric and GMDH-type neural networks are reliable tools to predict the output voltage of PEM fuel cell with high coefficient of determination values of 0.96 and 0.98.

How to Build Simple Models of PEM Fuel Cells for Fast Computation

“…To spare computation time due to computing of multiphysic fuel cell models, artificial intelligence (AI) techniques are useful as alternate approaches to conventional multiphysic modeling: e.g., artificial neural network (ANN) simulator could be employed to predict the fuel cell behavior [9][10][11][12]. The ANN could be trained with a reduced amount of data generated by a validated cell model [13].…”

Development of Simple-To-Use Predictive Models to Determine Thermal Properties of Fe2O3/Water-Ethylene Glycol Nanofluid

“…Many correlations are proposed to predict these properties based on experimental data and physical analysis [15,16,23,24]. However, performing numerous experiments to measure the properties under different conditions is costly especially when many operational parameters, such as size, shape, and concentration of the nanoparticles, as well as the working temperature have a considerable effect on the results [25][26][27][28]. On the other hand, the accuracy of the analytical models and the data-driven correlations may not be sufficient when the operational conditions are significantly changing compared to the basic assumptions.…”

Application GMDH artificial neural network for modeling of Al2O3/water and Al2O3/Ethylene glycol thermal conductivity

“…Based on literature review, artificial neural network modeling is an appropriate tool to model and predict thermal conductivity of nanofluids. Most of the conducted studies have considered temperature and concentration as influential parameters and input variables [53][54][55][56][57]; however, the size of nano particles affect thermal conductivity of nanofluids. In this study, group method of data handling (GMDH) artificial neural network is applied in order to model thermal conductivity ratio of 2 3 /water and 2 3 /EG because Al2O3 nanofluid is a usual nanofluid.…”