Study of cell voltage uniformity of proton exchange membrane fuel cell stack with an optimized artificial neural network model

The cell voltage uniformity of the proton exchange membrane fuel cell stack, which may consist of tens or hundreds of cells in series, plays a significant role in the stack's lifetime and performance. But it is challenging to predict the multi-cell voltages and the uniformity with a physics-based model due to complex stack geometry and huge computation efforts. In this work, we develop an artificial neural network model to estimate the steady-state cell voltage distributions of a 60 kW 140-cell stack. The optimized and well-trained model can efficiently reproduce the 140-cell voltages at different operating conditions with the error of less than 2 mV. The increased cathode gas pressure improves the cell voltage consistency and stack performance, while the voltage uniformity worsens with ascending load current. The efficient model prediction of cell voltages is beneficial for accurate evaluation of fuel cell performance, health state, and reliability.