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

doi:10.3901/JME.2022.22.106

Abstract

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

CLC Number:

TM911

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.

References

[1] 杨继斌,徐晓惠,张继业,等. 燃料电池有轨电车能量管理策略多目标优化[J]. 机械工程学报, 2018, 54(22):153-159. YANG Jibin, XU Xiaohui, ZHANG Jiye, et al. Multi-objective optimization of energy management strategy for fuel cell tram[J]. Journal of Mechanical Engineering, 2018, 54(22):153-159.
[2] İNCI M, BÜYÜK M, DEMIR M, et al. A review and research on fuel cell electric vehicles:Topologies, power electronic converters, energy management methods, technical challenges, marketing and future aspects[J]. Renewable and Sustainable Energy Reviews, 2021, 137:110648-110675.
[3] DAS V, PADMANABAN S, VENKITUSAMY K, et al. Recent advances and challenges of fuel cell based power system architectures and control-A review[J]. Renewable and Sustainable Energy Reviews, 2017, 73:10-18.
[4] 周伟,朱鑫宁,连云崧,等. 质子交换膜燃料电池的三维流场技术研究进展[J]. 机械工程学报, 2021, 57(8):2-12. ZHOU Wei, ZHU Xinning, LIAN Yunsong, et al. Research progress on three-dimensional flow field[J]. Journal of Mechanical Engineering, 2021, 57(8):2-12.
[5] 陈维荣,刘嘉蔚,李奇,等. 质子交换膜燃料电池故障诊断方法综述及展望[J]. 中国电机工程学报, 2017, 37(16):4712-4721, 4896. CHEN Weirong, LIU Jiawei, LI Qi, et al. Review and prospect of fault diagnosis methods for proton exchange membrane fuel cell[J]. Proceedings of the CSEE, 2017, 37(16):4712-4721, 4896.
[6] PETRONE R, ZHENG Z, HISSEL D, et al. A review on model-based diagnosis methodologies for PEMFCs[J]. International Journal of Hydrogen Energy, 2013, 38(17):7077-7091.
[7] 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. ZHANG Xuexia, JIANG Yu, SUN Tengfei, et al. Review on fault diagnosis for flooding and drying in proton exchange membrane fuel cells[J]. Journal of Southwest Jiaotong University, 2020, 55(4):828-838, 864.
[8] 党翰斌,马睿,周俊杰,等. 基于CNN的质子交换膜燃料电池故障诊断[J]. 电力电子技术, 2020, 54(12):52-55. DANG Hanbin, MA Rui, ZHOU Junjie, et al. Fault diagnosis of proton exchange membrane fuel cell based on CNN[J]. Power Electronics, 2020, 54(12):52-55.
[9] 刘嘉蔚,李奇,陈维荣,等. 基于多分类相关向量机和模糊c均值聚类的有轨电车用燃料电池系统故障诊断方法[J]. 中国电机工程学报, 2018, 38(20):6045-6052. LIU Jiawei, LI Qi, CHEN Weirong, et al. A fault diagnosis method of fuel cell systems for tramways based on the multi-class relevance vector machine and fuzzy c means clustering[J]. Proceedings of the CSEE, 2018, 38(20):6045-6052.
[10] LI Zhongliang, OUTBIB R, GIURGEA S, et al. Fault diagnosis for fuel cell systems:A data-driven approach using high-precise voltage sensors[J]. Renewable Energy, 2019, 135:1435-1444.
[11] MA Rui, DANG Hanbin, XIE Renyou, et al. Online fault diagnosis for open-cathode PEMFC systems based on output voltage measurements and data-driven method[J]. IEEE Transactions on Transportation Electrification, 2021, 8(2):2050-2061.
[12] PAHON E, YOUSFI STEINER N, JEMEI S, et al. A signal-based method for fast PEMFC diagnosis[J]. Applied Energy, 2016, 165:748-758.
[13] PEI Pucheng, LI Yuehua, XU Huachi, et al. A review on water fault diagnosis of PEMFC associated with the pressure drop[J]. Applied Energy, 2016, 173:366-385.
[14] 张少哲,戴海峰,袁浩,等. 质子交换膜燃料电池电化学阻抗谱敏感性研究[J]. 机械工程学报, 2021, 57(14):40-51. ZHANG Shaozhe, DAI Haifeng, YUAN Hao, et al. Sensibility study on electrochemical impedance of proton exchange membrane fuel cell[J]. Journal of Mechanical Engineering, 2021, 57(14):40-51.
[15] 周苏,韩秋玲,胡哲,等. 质子交换膜燃料电池故障诊断的模式识别方法[J]. 同济大学学报, 2017, 45(3):408-412. ZHOU Su, HAN Qiuling, HU Zhe, et al. Pattern recognition method for proton exchange membrane fuel cell[J]. Journal of Tongji University, 2017, 45(3):408-412.
[16] MAO Lei, LIU Zhongyong, LOW D, et al. Evaluation method for feature selection in proton exchange membrane fuel cell fault diagnosis[J]. IEEE Transactions on Industrial Electronics, 2022, 69(5):5277-5286.
[17] DOTELLI G, FERRERO R, STAMPINO P, et al. Combining electrical and pressure measurements for early flooding detection in a PEM fuel cell[J]. IEEE Transactions on Instrumentation and Measurement, 2016, 65(5):1007-1014.
[18] MAO Lei, JACKSON L, DAVIES B. Effectiveness of a novel sensor selection algorithm in PEM fuel cell on-line diagnosis[J]. IEEE Transactions on Industrial Electronics, 2018, 65(9):7301-7310.
[19] HAMAZ T, CADET C, DRUART F, et al. Diagnosis of PEM fuel cell stack based on magnetic fields measurements[C]//IFAC Proceedings Volumes, 2014:11482-11487.
[20] LI Zhongliang, CADET C, OUTBIB R. Diagnosis for PEMFC based on magnetic measurements and data-driven approach[J]. IEEE Transactions on Energy Conversion, 2019, 34(2):964-972.