Inference method of proton exchange membrane fuel cell gas diffusion layer composition based on pyramid scene parsing network
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Abstract:
To rapidly determine the morphology of the gas diffusion layer for proton exchange membrane fuel cell (PEMFC) and improve its fabrication process, a method of gas diffusion layer (GDL) component identification and proportional reasoning based on a combination of pyramid scene parsing network (PSPNet) and multilayer perceptron (MLP) is proposed. First, labeled GDL scanning electron microscope （SEM) images are input into the neural network to obtain a feature extraction map. This map is used in the pyramid pooling module to extract both deep and shallow features of the SEM images. Subsequently, these feature layers are input into the fully convolutional network (FCN) module to produce a predicted image of the same size. Finally, the proportion of pixels for each component is calculated, and the inference of component proportion is achieved by using the MLP. The accuracy of the proposed method is 81.24%, with an accuracy of proportional reasoning reaching 88.89% within a 5% deviation range. The proposed method can be effectively used for gas diffusion layer quality detection, numerical reconstruction, and process improvement.
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Supported by the National Natural Science Foundation of China (12102188), Guangdong Key Areas Research and Development Program (2019B090909003), and the Open-end Funds of Foshan Xianhu Laboratory of the Advanced Energy Science and Technology Guangdong Laboratory (XHD2020-004).
