Improvement of Blocked Long-Straight Flow Channels in Proton Exchange Membrane Fuel Cells Using CFD Modeling, Artificial Neural Network, and Genetic Algorithm

Abstract

To further improve the performance of the Proton Exchange Membrane Fuel Cell (PEMFC), in this paper, we designed a blocked flow channel with trapezoidal baffles, and geometric parameters of the baffle were optimized based on CFD simulation, Artificial Neural Network (ANN), and single-objective optimization methods. The analysis of velocity, pressure, and oxygen distribution in the cathode flow channel shows that the optimized trapezoidal baffle can improve oxygen transport during the reaction. The comparison of the optimization model with the straight flow channel model and the rectangular baffle model shows that the power density of the optimized model is 4.0% higher than that of the straight flow channel model at a voltage of 0.3 V, and the pressure drop is only 37.83% of that of the rectangular baffle model. For on-road PEMFC with a voltage of 0.6 V, the influence of pump power is significant, and the optimized trapezoidal baffle model has a net power increase of 1.47% compared to the rectangular baffle model at 50% pump efficiency and 3.94% at 30% pump efficiency.