Prediction of local current distribution in polymer electrolyte membrane fuel cell with artificial neural network

In the development process of a fuel cell, understanding the local current distribution is essentially required to achieve better performance and durability. Therefore, many developers apply a segmented fuel cell to observe current distribution under various operating conditions. With the application, experimental data is collected. This study suggests a utilization method for this collected data to develop a local current prediction model. The details of this neural network-based prediction model are introduced, including the pretreatment of the data. In the pretreatment process, current residual values are used for better prediction performance. As a result, the model predicted local current values with a 2.98% error. With the model, the effects of pressure, temperature, cathode relative humidity, and cathode flow rate on local current distribution trends are analyzed. Since the non-uniform current distribution of a fuel cell often leads to low performances or fast local degradation, the optimal operating condition to achieve current uniformity is acquired with an additional model. This model is developed by switching inputs and outputs of the local current prediction model. With the model application, the uniform current distribution is achieved with a standard deviation of 0.039 A/cm² under the current load at 1 Acm^?2.