Abstract
In this paper, a backpropagation neural network is designed to control the temperature of a 5 kW hydrogen fuel cell stack. This controller is initially trained to recognize the pattern of dynamic power demand from a consumption source. The power demand is the driving force in current production from the fuel cell, which in turn increases the temperature of the fuel cell stack. This temperature change is controlled by changing the flow rate of cooling water through the fuel cell stack. The results show that the neural network controller has excellent performance in maintaining the stack temperature at the desired set-point despite significant fluctuations in power demand.