Proton exchange membrane fuel cell performance prediction using artificial neural network

Tabbi Wilberforce*, A. G. Olabi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Polarization curves remain one of the parameters used to check the performance of fuels in terms of efficiency and durability. This investigation explores the application of artificial neutral network (ANN) to determine the voltage and current from a proton exchange membrane fuel cell having membrane area of 11.46 cm2. Performance predictability for the group method of data handling (GMDH) as well as feed forward back propagation (FFBP) neutral networks were employed for the estimation of the current and voltage obtained from the Proton Exchange Membrane Fuel cell under investigation. The investigation presented models with good predictions even though GMDH neural network performed better than the FFBP neural network. The study therefore proposes the GMDH neural network as the best model for predicting the performance of a Proton Exchange Membrane Fuel cell. It was further deduced that an increase in reactant flow rate has direct effect on the performance of the fuel cell but this is directly proportional to the total irreversibilities in the cell hence to operate fuel cell economically, it is imperative that the hydrogen flow is made lower compare to the oxygen flow rate. This in effect will reduce the pumping power required for the flow of the fuel hence reducing the net loss in the cell.

Original languageEnglish
Pages (from-to)6037-6050
Number of pages14
JournalInternational Journal of Hydrogen Energy
Issue number8
Early online date1 Sept 2020
Publication statusPublished - 29 Jan 2021


  • Artificial neutral network (ANN) analysis
  • Feed forward back propagation (FFBP)
  • Group method of data handling (GMDH)
  • Open circuit voltage (OPCV)
  • Polarization curve


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