基于磁场的质子交换膜燃料电池故障诊断方法

doi:10.3901/JME.2022.22.106

机械工程学报 ›› 2022, Vol. 58 ›› Issue (22): 106-114.doi: 10.3901/JME.2022.22.106

• 特邀专栏:车载电化学能源系统 • 上一篇下一篇

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基于磁场的质子交换膜燃料电池故障诊断方法

孙誉宁¹, 毛磊^1,2, 黄伟国³, 章恒⁴, 陆守香^2,5

1. 中国科学技术大学精密机械与精密仪器系合肥 230022;
2. 中国科学技术大学先进技术研究院合肥 230094;
3. 苏州大学轨道交通学院苏州 215131;
4. 合肥学院人工智能与大数据学院合肥 230601;
5. 中国科学技术大学火灾科学国家重点实验室合肥 230022

收稿日期:2022-04-05 修回日期:2022-10-12 出版日期:2022-11-20 发布日期:2023-02-07
通讯作者: 毛磊(通信作者),男,1982年出生,博士,教授,博士研究生导师。主要研究方向为氢燃料电池性能感知、故障预警与控制方法。E-mail:leimao82@ustc.edu.cn
作者简介:孙誉宁,女,1997年出生,博士研究生。主要研究方向为氢燃料电池性能退化机理研究。E-mail:syn97@mail.ustc.edu.cn
基金资助:
国家自然科学基金(51975549);安徽省重点研发计划标准化专项(202104h04020006);安徽省自然科学基金(1908085ME161);合肥市自然科学基金(2021022)资助项目

Fault Diagnosis of Proton Exchange Membrane Fuel Cell Using Magnetic Field Data

SUN Yu-ning¹, MAO Lei^1,2, HUANG Wei-guo³, ZHANG Heng⁴, LU Shou-xiang^2,5

1. Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei 230022;
2. Institute of Advanced Technology, University of Science and Technology of China, Hefei 230094;
3. School of Rail Transportation, Soochow University, Suzhou 215131;
4. School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601;
5. State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei 230022

Received:2022-04-05 Revised:2022-10-12 Online:2022-11-20 Published:2023-02-07

摘要/Abstract

摘要： 故障诊断作为解决质子交换膜燃料电池（Proton exchange membrane fuel cell,PEMFC）的安全和寿命问题的重要途径之一，备受研究人员关注。然而在当前PEMFC诊断中，对其早期故障诊断的研究较少，而在亟需早期故障诊断以便及时进行维护控制的PEMFC应用领域，如燃料电池汽车等，在故障发生早期对其进行精确诊断极其重要。该文针对现有PEMFC早期故障诊断方法匮乏问题，提出一种基于磁场的PEMFC故障诊断方法。首先建立PEMFC三维仿真模型，研究燃料电池性能变化与其外部磁场间关联机制，在此基础上搭建燃料电池磁场检测系统，并构建卷积神经网络（Convolutional neural network,CNN）对采集的磁场数据进行分析，验证其在包括水淹、膜干等不同PEMFC故障中的早期诊断效果。结果表明，采用基于磁场数据和卷积神经网络的故障诊断方法，可实现燃料电池不同程度、不同类型故障的在线识别和早期诊断。研究结果验证了磁场数据用于PEMFC故障诊断的可行性，对促进PEMFC故障诊断方法进一步发展、提升PEMFC系统可靠性和耐久性具有重要意义。

关键词: 质子交换膜燃料电池, 早期故障诊断, 磁场, 卷积神经网络

Abstract: Fault diagnosis has been considered as a critical solution for improving the reliability and durability of proton exchange membrane fuel cell（PEMFC）, while researches regarding early fault diagnosis are still at its infancy and urgently needed in applications where timely maintenance and control are required, such as fuel cell vehicle. Considering limited early fault diagnosis methods, a fault diagnosis approach using PEMFC magnetic field data is proposed. A PEMFC numerical model is developed firstly to investigate the correlation between PEMFC states and magnetic field variation. On such basis, a PEMFC magnetic field detection system is built, from which collected magnetic field is analysed by a convolutional neural network（CNN） to verify its early diagnosis effect at various PEMFC faults, including flooding, dehydration at different fault levels. Results indicate that with magnetic field data analysed by CNN, different PEMFC faults at various levels can be on-line identified and early detected. This research verifies the feasibility of using magnetic field data for PEMFC fault diagnosis, which is of vital significance to further perform PEMFC fault diagnosis to improve the reliability and durability of PEMFC.

Key words: proton exchange membrane fuel cell, early fault diagnosis, magnetic field, convolution neural network

中图分类号:

TM911

孙誉宁, 毛磊, 黄伟国, 章恒, 陆守香. 基于磁场的质子交换膜燃料电池故障诊断方法[J]. 机械工程学报, 2022, 58(22): 106-114.

SUN Yu-ning, MAO Lei, HUANG Wei-guo, ZHANG Heng, LU Shou-xiang. Fault Diagnosis of Proton Exchange Membrane Fuel Cell Using Magnetic Field Data[J]. Journal of Mechanical Engineering, 2022, 58(22): 106-114.

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基于磁场的质子交换膜燃料电池故障诊断方法

Fault Diagnosis of Proton Exchange Membrane Fuel Cell Using Magnetic Field Data

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