Parametric Analysis of Proton Exchange Membrane Fuel Cell Performance under Different Operation Condition by Using a Neural Network

The neural network can construct relationships between the control factors and responses in the PEMFC. This study proposes a feed forward neural network modeling for parametric analysis ofproton exchange membrane fuel cell (PEMFC) performance. The neural network trained withLederberg-Marquardt algorithm. Numerous parameters affecting the maximum power density of PEMFC are analyzed, such as fuel cell operating temperatures, cathode and anode humidification temperatures and operating pressures. Experimental results are presented for identifying theproposed approach, which is useful to get deep knowledge of PEMFC behavior

Keywords:

PEM fuel cell , Maximum Power density , Operating condition , Artificial neural network