Estimation of Starting Voltage of PEMFC by Using Statistical Analysis and Artificial Neural Networks

Abstract

Proton exchange membrane fuel cell (PEMFC) is a substitute energy source that provides electricity from hydrogen and oxygen fuel. The PEMFC has found its application not only in electric power generation but also has founds its use in electric vehicles and aircraft applications. The performance of the PEMFC is affected by the variation in ambient conditions especially at the time of start-up. Both ambient temperature, relative humidity of air and ambient pressure of air have major impact on open-cathode PEMFC efficacy. However, in case of close cathode system where pure oxygen is provided to PEMFC, the ambient air pressure and humidity variations does not affect significantly. In this case ambient temperature remains the only important factor on PEMFC performance. In this work the starting voltage of the PEMFC has been studied at altered loading settings. Various techniques such as statistical analysis and artificial neural networks have been used to predict the starting voltage of PEMFC. Artificial neural feed-forward networks are found to be very effective in determining the start-up voltage of PEMFC at various ambient conditions.