质子交换膜燃料电池
洪水(心理学)
堆栈(抽象数据类型)
阴极
阳极
人工神经网络
电压
计算机科学
工程类
化学
化学工程
人工智能
燃料电池
电气工程
电极
心理学
物理化学
心理治疗师
程序设计语言
作者
Haibo Huo,Jiajie Chen,Ke Wang,Fang Wang,Guangzhe Jin,Fengxiang Chen
出处
期刊:Sustainability
[MDPI AG]
日期:2023-06-05
卷期号:15 (11): 9094-9094
被引量:1
摘要
Too high or too low water content in the proton exchange membrane (PEM) will affect the output performance of the proton exchange membrane fuel cell (PEMFC) and shorten its service life. In this paper, the mathematical mechanisms of cathode mass flow, anode mass flow, water content in the PEM and stack voltage of the PEMFC are deeply studied. Furthermore, the dynamic output characteristics of the PEMFC under the conditions of flooding and drying membrane are reported, and the influence of water content in PEM on output performance of the PEMFC is analyzed. To effectively diagnose membrane drying and flooding faults, prolong their lifespan and thus to improve operation performance, this paper proposes the state assessment of water content in the PEM based on BP neural network optimized by genetic algorithm (GA). Simulation results show that compared with LS-SVM, GA-BP neural network has higher estimation accuracy, which lays a foundation for the fault diagnosis, life extension and control scheme design of the PEMFC.
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