重庆大学学报2024，Vol.47Issue(1)：84-92,9.DOI:10.11835/j.issn.1000-582X.2022.119
基于金字塔池化网络的质子交换膜燃料电池气体扩散层组分推理方法
Inference method of proton exchange membrane fuel cell gas diffusion layer composition based on pyramid scene parsing network
摘要
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.关键词
质子交换膜燃料电池/气体扩散层制备/扫描电镜/人工智能/金字塔池化网络/多层感知器Key words
proton exchange membrane fuel cell/gas diffusion layer preparation/scanning electron microscope/artificial intelligence/pyramid scene parsing network/multilayer perceptron分类
能源科技引用本文复制引用
王虎,尹泽泉,王雯婕,黄笠舟,方宁宁,隋俊友,张加乐,张锐明,隋邦傑..基于金字塔池化网络的质子交换膜燃料电池气体扩散层组分推理方法[J].重庆大学学报,2024,47(1):84-92,9.基金项目
国家自然科学基金青年项目(12102188) （12102188）
广东省重点领域研发计划项目(2019B090909003) （2019B090909003）
先进能源科学与技术广东省实验室佛山分中心(佛山仙湖实验室)开放基金(XHD2020-004).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). （佛山仙湖实验室）
