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The paper uses artificial neural networks to model the dynamic electrical and thermal performance of a proton exchange membrane fuel cell (PEMFC) stack under various operating conditions.

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What they did

System
MFC

What worked

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Abstract

A proton exchange membrane fuel cell (PEMFC) is a more environmentally friendly alternative to deliver electric power in various applications, including in the transportation industry. As PEMFC performance characteristics are inherently nonlinear and involved, the prediction of the performance in a given application for different operating conditions is important in order to optimize the efficiency of the system. Thus, modelling using artificial neural networks (ANNs) to predict its performance can significantly improve the capabilities of handling the multi-variable nonlinear performance of the PEMFC. However, further investigation is needed to develop a dynamic model using ANNs to predict the transient behavior of a PEMFC. This paper predicts the dynamic electrical and thermal performance of a PEMFC stack under various operating conditions. The input variables of the PEMFC stack for the analysis consist of the cathode inlet temperature, anode inlet pressure, anode and cathode inlet flow rates, and stack current. The performances of the ANN models using three different learning algorithms are determined based on the stack voltage and temperature, which have been shown to be consistently predicted by most of these models. Almost all models with varying hidden neurons have coefficients of determination of 0.9 or higher and mean squared errors of less than 5. Thus, the results show promise for dynamic modelling approaches using ANNs for the development of optimal operation of a PEMFC in various system applications.

Key findings

  • ANN models using three different learning algorithms can accurately predict the stack voltage and temperature.
  • The input variables of the PEMFC stack, including cathode inlet temperature, anode inlet pressure, anode and cathode inlet flow rates, and stack current, significantly impact the performance.
  • The use of ANNs can improve the capabilities of handling the multi-variable nonlinear performance of the PEMFC.

Keywords

Stack (abstract data type)Proton exchange membrane fuel cellArtificial neural networkNonlinear systemAnodeVoltage

Identifiers

DOI
10.3390/en15155587
Journal
Energies
Year
2022