Proton exchange membrane fuel cell performance prediction using artificial neural network

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 cm(2). 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. (c) 2020 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

Automated summary

The study investigates the use of neural networks to predict voltage and current of proton exchange membrane fuel cells, suggesting that the performance of group method data handling neural network is superior to feed forward back propagation neural network. It is proposed that increasing reactant flow rate directly affects cell performance, but should be lower compared to oxygen flow rate for reduced irreversibilities and operating costs.