基于开普勒算法优化卷积神经网络的燃料电池故障诊断技术

施永; 黄宁; 谢缔; 汪亮亮; 姚继刚

doi:doi:10.19912/j.0254-0096.tynxb.2024-1777

English Version Journal Login

您当前的位置：

首页 >

文章列表页 >

基于开普勒算法优化卷积神经网络的燃料电池故障诊断技术

更新时间：2026-04-08

- 基于开普勒算法优化卷积神经网络的燃料电池故障诊断技术
- Vol. 47, Issue 3, Pages: 556-563(2026)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1777
  CLC： TM911
- Online First：07 April 2026，
  
  Published：2026
- 稿件说明：
移动端阅览
施永, 黄宁, 谢缔, et al. 基于开普勒算法优化卷积神经网络的燃料电池故障诊断技术[J]. 2026, 47(3): 556-563.
DOI：

施永, 黄宁, 谢缔, et al. 基于开普勒算法优化卷积神经网络的燃料电池故障诊断技术[J]. 2026, 47(3): 556-563. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1777.

摘要

提出一种基于等效电路和基于开普勒算法优化的卷积神经网络（KOA-CNN）框架的诊断技术

通过使用电化学阻抗谱（EIS）获得质子交换膜燃料电池（PEMFC）阻抗谱信息

并使用等效电路进行参数辨识

使用拟合得到的电路参数作为诊断算法的训练数据

利用卷积神经网络对故障特征进行提取

利用开普勒优化算法收敛速度快、全局搜索能力强、参数少的特点去优化卷积神经网络的超参数

得到一个最佳的卷积神经网络参数

可显著提高燃料电池故障诊断的精度。经验证

该方法在水淹、膜干、氧气饥饿的故障诊断中准确率达到99.75%。

Abstract

Fault diagnosis techniques play a crucial role in the normal operation of proton exchange membrane fuel cells. In this paper

we propose a diagnostic technique based on equivalent circuit and KOA-CNN framework

which obtains the PEMFC impedance spectrum information by using electrochemical impedance spectroscopy （EIS） and uses the equivalent circuit for parameter identification

and uses the fitted circuit parameters as the training data for the diagnostic algorithm

and extracts the fault features by using a convolutional neural network

which can significantly improve the accuracy of PEMFC fault diagnosis. The Kepler optimization algorithm convergence speed

strong global search ability

and few parameters to optimize the hyperparameters of the convolutional neural network

to get an optimal convolutional neural network parameters

which can significantly improve the accuracy of fuel cell fault diagnosis. It is verified that the accuracy of this method reaches 99.75% in the fault diagnosis of water flooding

membrane drying and oxygen starvation.

关键词

Keywords

references

施永, 黄宁, 谢缔, 等. 基于自注意力机制和CNN融合的燃料电池故障诊断技术[J]. 太阳能学报, 2025, 46(5): 53-61.

张雪霞, 蒋宇, 孙腾飞, 等. 质子交换膜燃料电池水淹和膜干故障诊断研究综述[J]. 西南交通大学学报, 2020, 55(4): 828-838, 864.

雍加望, 赵倩倩, 冯能莲. 基于非线性动态模型的质子交换膜燃料电池故障诊断[J]. 化工学报, 2022, 73(9): 3983-3993.

周苏, 任宏伟, 裴冯来. 基于诊断用COMSOL模型的PEMFC故障诊断方法研究[J]. 化学工业与工程, 2015, 32(4): 56-62.

RUBIO M A, URQUIA A, DORMIDO S.Diagnosis of performance degradation phenomena in PEM fuel cells[J]. International journal of hydrogen energy, 2010, 35(7): 2586-2590.

ROTONDO D, FERNANDEZ-CANTI R M, TORNIL-SIN S, et al. Robust fault diagnosis of proton exchange membrane fuel cells using a Takagi-Sugeno interval observer approach[J]. International journal of hydrogen energy, 2016, 41(4): 2875-2886.

AKITOMO F, SASABE T, YOSHIDA T, et al.Investigation of effects of high temperature and pressure on a polymer electrolyte fuel cell with polarization analysis and X-ray imaging of liquid water[J]. Journal of power sources, 2019, 431: 205-209.

孙誉宁, 毛磊, 黄伟国, 等. 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.

刘嘉蔚, 李奇, 陈维荣, 等. 基于在线序列超限学习机和主成分分析的蒸汽冷却型燃料电池系统快速故障诊断方法[J]. 电工技术学报, 2019, 34(18): 3949-3960.

刘相万, 杨扬, 朱文超, 等. 基于二阶RQ-RLC模型的质子交换膜燃料电池水管理故障诊断[J]. 中国电机工程学报, 2022, 42(21): 7893-7905.

张领先, 刘斌, 邓琳, 等. 基于改进TSO优化Xception的PEMFC故障诊断[J]. 化工学报, 2024, 75(3): 945-955.

DU R B, WEI X Z, WANG X Y, et al.A fault diagnosis model for proton exchange membrane fuel cell based on impedance identification with differential evolution algorithm[J]. International journal of hydrogen energy, 2021, 46(78): 38795-38808.

LIU Z Y, SUN Y N, TANG X W, et al.Enabling unsupervised fault diagnosis of proton exchange membrane fuel cell stack:knowledge transfer from single-cell to stack[J]. Applied energy, 2024, 360: 122814.

WANG Z H, GAO Y, YU J, et al.Data-driven fault diagnosis of PEMFC water management with segmented cell and deep learning technologies[J]. International journal of hydrogen energy, 2024, 67: 715-727.

贺飞, 张雪霞, 陈维荣. 基于P-L双重特征提取的PEMFC系统故障诊断方法[J]. 太阳能学报, 2024, 45(1): 492-499.

赵波, 刘相万, 章雷其, 等. 基于GoogLeNet与迁移学习的质子交换膜燃料电池集成系统故障诊断[J]. 中国电机工程学报, 2024, 44(13): 5147-5158.

杨怡菲, 高岩. 基于线性判别分析的电力负荷预测[J]. 电子设计工程, 2023, 31(11): 102-106.

ABDEL-BASSET M, MOHAMED R, ABDEL AZEEM S A, et al. Kepler optimization algorithm: a new metaheuristic algorithm inspired by Kepler’s laws of planetary motion[J]. Knowledge-based systems, 2023, 268: 110454.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

BROADBAND EIS TEST OF FUEL CELL BASED ON BINARY SEQUENCES

FAULT DIAGNOSIS METHOD OF WIND TURBINE BEARING BASED ON AM AND CNN MULTI-LEVEL FEATURE FUSION

基于ISSA-RF算法的光伏阵列故障诊断研究

基于VMD-COMTBO-BiLSTM光伏阵列故障诊断研究

基于振动和转子电流深度融合的风电机组转频估计及故障诊断方法

Related Author

Jin Zhihan

Su Jianhui

Shi Yong

Wang Haining

Wang Jinhua

Han Jinyu

Cao Jie

Wang Yali

Related Institution

Photovoltaic System Engineering Research Center of the Ministry of Education（Hefei University of Technology）

School of Electrical Engineering and Automation, Hefei University of Technology

Engineering Research Center of Manufacturing Information of Gansu Province

College of Electrical & Engineering, Lanzhou University of Technology

School of Information Engineering, Lanzhou City University

AI问答

⁰