# An Intelligent Approach for Contact Pressure Optimization of PEM Fuel Cell Gas Diffusion Layers
## 1 Introduction  
Proton exchange membrane fuel cells (PEMFCs) are considered a clean and portable power source, which is already being applied to cars, buses, and heavy trucks. The packing is very important for either stacks or a single cell to keep layers together and avoid over deformation. Proper packing design can ensure that fuel cell performances are not limited by structural mechanics [1; 2; 3; 4]. Contact pressure on the gas diffusion layer (GDL) can affect the electrochemical performance of a fuel cell through contact resistance and porosity. [PERSON] [5] investigated the effect of clamping torque on the polarization curve of fuel cells with different GDLs experimentally. It was found that the influence of the clamping torque on voltage and power density of the fuel cell depends on the GDL materials. [PERSON] [6] also obtained similar conclusions that the compression ratio of GDLs indeed affects the fuel cell performance, i.e., the polarization curve and current density at different operating conditions. [PERSON] [7] measured the contact resistance between GDLs and bipolar plates (BPPs) under various contact pressures. It was found that the effect of contact pressure on the fuel cell performance also correlates to the magnitude of the contact pressure. At a lower range of contact pressure, the increasing contact pressure can adequately reduce the contact resistance between layers so as to enhance theelectrochemical performance. For a higher range of contact pressure, the increase in the contact pressure decreases the mass transport from gas channels to the catalyst layer, which has an adverse effect on the current density, despite that the ohmic resistance is decreased at the same time. It is also found that the contact pressure of GDLs can improve the thermal and electrical conductivities by decreasing contact resistance [8; 9]. Leakage is another problem we must consider in the clamping design of fuel cells. Sometimes it is the pressure on the gasket that prevents the leakage, which may conflict with the contact pressure on the GDL. However, in most cases, low pressure on the gasket can be used as a constraint in the clamping design. References [10; 11; 12] also suggest that there should be an optimal clamping force for the fuel cell to reach better performance and avoid leakage at the same time. A proper magnitude of clamping force or torque can result in a proper magnitude of nominal contact pressure on the GDL, which leads to a good nominal electrochemical performance of fuel cells. Apart from that, the pressure distribution on the GDL also affects the mass transport in channels and the contact resistance at the ribs. [PERSON] [13] proved that nonuniformly distributed contact pressure has a great impact on current density distribution and adjusting the clamping can achieve a uniform distribution of the current density. Increasing the stiffness of the end plate (EP) can improve the pressure distribution on GDLs by means of increasing the thickness or using stiffer materials [14; 15]. Hydraulic aided EPs [16; 17] and composite EPs [18] were also proposed to get a better clamping pressure distribution. A compromise between contact resistance, porosity, and leakage pressure should be achieved by optimizing the clamping of the fuel cell. In addition, an evenly distributed contact pressure on the GDL is preferable and benefits fuel cell performance. The traditional physical model-based optimization process is precise but time-consuming. It would be unimaginable that a large design of experiments or long iterations of optimization can be done by a 3D full-size fuel cell model in computational fluid dynamics (CFD) or finite element (FE) analysis. An agent model, which can fit the physical model well, would be useful and powerful for prediction and optimization [19; 20; 21]. While an agent model is developed and validated, the design of experiments can be done on a large scale, as well as the following optimization iterations, using artificial intelligence-based algorithms [22; 23; 24; 25; 26; 27].