Multi-objective optimization of gas diffusion layer structure parameters for proton exchange membrane fuel cell

As an important component of a proton exchange membrane fuel cell, the structure of gas diffusion layer affects the transmission of materials, thus affecting the heat and current density. However, the current researches on gas diffusion layer are mostly about homogeneous structure, and additionally it is unknown to the performance influenced by the structure parameters. Firstly, a gas diffusion layer (GDL) model reflecting complex structure is constructed based on a random parameter method, and a micro-element model of fuel cell is established. Secondly, a Latin hypercube sampling is used to obtain sample points and an extreme learning machine (ELM) model is formulated to fit simulation data. The ELM model is verified by comparing with the actual results. Finally, the micro-element model performance affected by the structure variables is analyzed, and the optimal structural variables are obtained. The results show that the ELM model can accurately predict the fuel cell micro-element model performance and its prediction accuracy is better than that of the back propagation (BP) neural network model. The multi-objective optimal algorithm model can obtain the optimal GDL structure parameters corresponding to the minimum temperature, maximum current density, and good oxygen concentration uniformity.