Water transport in PEMFC with metal foam flow fields: Visualization based on AI image recognition

Water transport is an essential process in proton exchange membrane fuel cells (PEMFC). For the novel metal foam flow fields, water transport is difficult to be visualized and still remains inadequately understood. In this work, for the first time, the two-phase flows in the metal foam flow fields of a transparent PEMFC was visualized by using an artificial intelligence (AI) image recognition method. In order to distinguish water from the metal foam ligaments, a scoring system was developed, which effectively solved the problem of inconsistent reflectance of liquid water between the ligament and the gas diffusion layer. According to the state of liquid water, the scoring system categorized the local images into three labels of waterless state, wet state, and slug state for AI image recognition network training. The trained AI image recognition model was then used to visualize the liquid water state in the metal foam flow fields under different operating conditions of relative humidity (RH), temperature, and inlet flow rate. Results show that, since the porous structure of the metal foam provides flexible transport paths, the PEMFC can operate effectively in a wide range of wet states. Even a 25% difference in the wet -state percentage between different operating conditions yields an almost consistent voltage. The increased slugs in the flow fields are the primary reason for the performance degradation of the PEMFC. At a high current density of 2 A/cm 2 , the voltage is as low as 0.03 V when the slug state occupies 35% of the flow fields (100% RH, 60 degrees C, 1.0 SLPM cathode inlet flow rate), and as high as 0.56 V when the slug state is absent in the flow fields (100% RH, 60 degrees C, 2.0 SLPM cathode inlet flow rate). The present work provides a novel approach for the studies of water transport in PEMFC.