Abstract
The present chapter focuses on the results of a numerical investigation carried out to improve the performance of proton exchange membrane fuel cells (PEMFC) by employing various operating conditions. The numerical model used in the present study is three-dimensional, laminar, steady state, single phase and non-isothermal. The results are validated with the experiment conducted on a serpentine channel flow channel of a PEMFC having 50 cm2 active area with channel to landing ratio as unity. As the input parameters of the fuel cell has a significant impact on the mass transfer which in turn influences the performance of fuel cells, various operating conditions (mass flow rate of the fuel and the oxidizer, cell voltage, temperature) are considered in the present case. In order to maximize the performance of the PEMFC, an artificial neural network (ANN) technique is used. A current density of 0.9924 A/cm2 for hydrogen and oxygen flow rates of 600 and 1200 standard cubic centimetres per minute (SCCM) respectively with a cell voltage 0.46 V and temperature of 66.5 °C, is the best result predicted by the network.
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Tenson, T.J., Baby, R. (2023). Predicting the Performance Enhancement of Proton Exchange Membrane Fuel Cell at Various Operating Conditions by Artificial Neural Network. In: Mathew, V.K., Hotta, T.K., Ali, H.M., Sundaram, S. (eds) Energy Storage Systems. Engineering Optimization: Methods and Applications. Springer, Singapore. https://doi.org/10.1007/978-981-19-4502-1_14
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DOI: https://doi.org/10.1007/978-981-19-4502-1_14
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