Integration of multi-physics and machine learning-based surrogate modelling approaches for multi-objective optimization of deformed GDL of PEM fuel cells  

作　　者：Jiankang Wang Hai Jiang Gaojian Chen Huizhi Wang Lu Lu Jianguo Liu Lei Xing 

机构地区：[1]National Laboratory of Solid-State Microstructures,College of Engineering and Applied Sciences,Nanjing University,Nanjing,210093,China [2]School of chemistry and chemical engineering,Jiangsu University,Zhenjiang,212013,China [3]Department of Mechanical Engineering,Imperial College London,London SW72BX,UK [4]Department of Chemical and Biomolecular Engineering,University of Pennsylvania,Philadelphia PA 19104,USA [5]Institute of Energy Power Innovation,North China Electric Power University,Beijing 102206,China [6]Department of Chemical and Process Engineering,University of Surrey,Guildford GU27XH,UK

出　　处：《Energy and AI》2023年第4期159-172,共14页能源与人工智能（英文）

基　　金：The authors acknowledge the financial support from National Natural Science Foundation of China(21978118).

摘　　要：The development of artificial intelligence(AI)greatly boosts scientific and engineering innovation.As one of the promising candidates for transiting the carbon intensive economy to zero emission future,proton exchange membrane(PEM)fuel cells has aroused extensive attentions.The gas diffusion layer(GDL)strongly affects the water and heat management during PEM fuel cells operation,therefore multi-variable optimization,including thickness,porosity,conductivity,channel/rib widths and compression ratio,is essential for the improved cell performance.However,traditional experiment-based optimization is time consuming and economically unaffordable.To break down the obstacles to rapidly optimize GDLs,physics-based simulation and machine-learning-based surrogate modelling are integrated to build a sophisticated M 5 model,in which multi-physics and multi-phase flow simulation,machine-learning-based surrogate modelling,multi-variable and multi-objects optimization are included.Two machine learning methodologies,namely response surface methodol-ogy(RSM)and artificial neural network(ANN)are compared.The M 5 model is proved to be effective and efficient for GDL optimization.After optimization,the current density and standard deviation of oxygen dis-tribution at 0.4 V are improved by 20.8%and 74.6%,respectively.Pareto front is obtained to trade off the cell performance and homogeneity of oxygen distribution,e.g.,20.5%higher current density is achieved when sacrificing the standard deviation of oxygen distribution by 26.0%.

关 键 词：Multi-physics modelling Machine learning Multi-objective optimization Gas diffusion layer Proton exchange membrane fuel cells 

分 类 号：TS2[轻工技术与工程—食品科学与工程]