Image Recognition of Gas Diffusion Layer Structural Features Based on Artificial Intelligence

Gas diffusion layer (GDL), as a critical constituent of the proton exchange membrane fuel cell (PEMFC), plays a key role in mass, heat, electron, and species transport. GDL generally has two distinct layers: a macro-porous substrate (MPS) and a micro-porous layer (MPL). The fibers in MPS and the cracks formed during the deposition process on the surface of MPL change the overall transport capacity and effect the output performance of PEMFC. In this paper, methods of identifying the structural features of fibers and cracks in GDL images based on artificial intelligence are proposed. The block probabilistic Hough transform and the quadric voting based on the weighted K-means algorithm are programmed to realize the fiber feature extraction, and the crack feature extraction is realized by the regional connectivity algorithm and the geometric feature calculation based on the circumscribed graph of the crack region. Besides, the scanning electron microscope (SEM) images of GDL are analyzed to validate the feasibility and accuracy of the algorithm. Results can prove that the fiber feature recognition accuracy can reach more than 90% and the usage of various characteristic parameters to quantify the crack is necessary. The image processing technology based on artificial intelligence can capture the microstructural features of GDL images and extract feature parameters, which provides a reliable tool for GDL image analysis and has guiding significance for further research on GDL.