The compression of the gas diffusion layer (GDL) greatly affects the electrochemical performance of proton exchange membrane fuel cells (PEMFCs) by means of both the equivalent value and distribution of contact pressure, which depends on the packing manner of the fuel cell. This work develops an intelligent approach for improving the uniformity and equivalent magnitude of contact pressure on GDLs through optimizing the clamping forces and positions on end plates. A finite element (FE) model of a full-size single fuel cell is developed and correlated against a direct measurement of pressure between the GDL and a bipolar plate. Datasets generated by FE simulations based on the optimal Latin hypercube design are used as a driving force for the training of a radial basis function neural network, so-called the agent model. Once the agent model is validated, iterations for optimization of contact pressure on GDLs are carried out without using the complicated physical model anymore. Optimal design of clamping force and position combination is achieved in terms of better contact pressure, with the designed equivalent magnitude and more uniform distribution. Results indicate the proposed agent-based intelligent optimization approach is available for the packing design of fuel cells, stacks in particular, with significantly higher efficiency.

The transient response of proton exchange membrane fuel cells (PEMFCs) is one of the crucial factors for cell performance and automobile application. Even though the transient response of the overall current shows few oscillations, different local transient responses exist. And limited experimental research focuses on the local transient response of PEMFCs, which gives a

For the catalyst layer based on platinum, platinum particles must be sufficiently dispersed and reasonably distributed inside the catalyst layer to ensure its high electrochemical active surface area and catalytic activity. When the fuel cell is operated at various loads, the current density inside the catalyst layer will be uneven, which can accelerate the degradation of the