IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i14p3643-d384736.html
   My bibliography  Save this article

Rapid Fault Diagnosis of PEM Fuel Cells through Optimal Electrochemical Impedance Spectroscopy Tests

Author

Listed:
  • Behzad Najafi

    (Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy)

  • Paolo Bonomi

    (Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy)

  • Andrea Casalegno

    (Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy)

  • Fabio Rinaldi

    (Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy)

  • Andrea Baricci

    (Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy)

Abstract

The present paper is focused on proposing and implementing a methodology for robust and rapid diagnosis of PEM fuel cells’ faults using Electrochemical Impedance Spectroscopy (EIS). Accordingly, EIS tests have been first conducted on four identical fresh PEM fuel cells along with an aged PEMFC at different current density levels and operating conditions. A label, which represents the presence of a type of fault (flooding or dehydration) or the regular operation, is then assigned to each test based on the expert knowledge employing the cell’s spectrum on the Nyquist plot. Since the time required to generate the spectrum should be minimized and considering the notable difference in the time needed for carrying out EIS tests at different frequency ranges, the frequencies have been categorized into four clusters (based on the corresponding order of magnitude: >1 kHz, >100 Hz, >10 Hz, >1 Hz). Next, for each frequency cluster and each specific current density, while utilizing a classification algorithm, a feature selection procedure is implemented in order to find the combination of EIS frequencies utilizing which results in the highest fault diagnosis accuracy and requires the lowest EIS testing time. For the case of fresh cells, employing the cluster of frequencies with f > 10 Hz, an accuracy of 98.5 % is obtained, whereas once the EIS tests from degraded cells are added to the dataset, the achieved accuracy is reduced to 89.2 % . It is also demonstrated that, while utilizing the selected pipelines, the required time for conducting the EIS test is less than one second, an advantage that facilitates real-time in-operando diagnosis of water management issues.

Suggested Citation

  • Behzad Najafi & Paolo Bonomi & Andrea Casalegno & Fabio Rinaldi & Andrea Baricci, 2020. "Rapid Fault Diagnosis of PEM Fuel Cells through Optimal Electrochemical Impedance Spectroscopy Tests," Energies, MDPI, vol. 13(14), pages 1-19, July.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:14:p:3643-:d:384736
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/14/3643/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/13/14/3643/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Pahon, E. & Yousfi Steiner, N. & Jemei, S. & Hissel, D. & Moçoteguy, P., 2016. "A signal-based method for fast PEMFC diagnosis," Applied Energy, Elsevier, vol. 165(C), pages 748-758.
    2. Manoj Manivannan & Behzad Najafi & Fabio Rinaldi, 2017. "Machine Learning-Based Short-Term Prediction of Air-Conditioning Load through Smart Meter Analytics," Energies, MDPI, vol. 10(11), pages 1-17, November.
    3. Sutharssan, Thamo & Montalvao, Diogo & Chen, Yong Kang & Wang, Wen-Chung & Pisac, Claudia & Elemara, Hakim, 2017. "A review on prognostics and health monitoring of proton exchange membrane fuel cell," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 440-450.
    4. Najafi, Behzad & Haghighat Mamaghani, Alireza & Rinaldi, Fabio & Casalegno, Andrea, 2015. "Long-term performance analysis of an HT-PEM fuel cell based micro-CHP system: Operational strategies," Applied Energy, Elsevier, vol. 147(C), pages 582-592.
    5. Haghighat Mamaghani, Alireza & Najafi, Behzad & Casalegno, Andrea & Rinaldi, Fabio, 2017. "Predictive modelling and adaptive long-term performance optimization of an HT-PEM fuel cell based micro combined heat and power (CHP) plant," Applied Energy, Elsevier, vol. 192(C), pages 519-529.
    6. Zhang, Zehan & Li, Shuanghong & Xiao, Yawen & Yang, Yupu, 2019. "Intelligent simultaneous fault diagnosis for solid oxide fuel cell system based on deep learning," Applied Energy, Elsevier, vol. 233, pages 930-942.
    7. Wang, Yun & Chen, Ken S. & Mishler, Jeffrey & Cho, Sung Chan & Adroher, Xavier Cordobes, 2011. "A review of polymer electrolyte membrane fuel cells: Technology, applications, and needs on fundamental research," Applied Energy, Elsevier, vol. 88(4), pages 981-1007, April.
    8. Li, Zhongliang & Outbib, Rachid & Giurgea, Stefan & Hissel, Daniel & Jemei, Samir & Giraud, Alain & Rosini, Sebastien, 2016. "Online implementation of SVM based fault diagnosis strategy for PEMFC systems," Applied Energy, Elsevier, vol. 164(C), pages 284-293.
    9. Polverino, Pierpaolo & Sorrentino, Marco & Pianese, Cesare, 2017. "A model-based diagnostic technique to enhance faults isolability in Solid Oxide Fuel Cell systems," Applied Energy, Elsevier, vol. 204(C), pages 1198-1214.
    10. Ren, Peng & Pei, Pucheng & Li, Yuehua & Wu, Ziyao & Chen, Dongfang & Huang, Shangwei & Jia, Xiaoning, 2019. "Diagnosis of water failures in proton exchange membrane fuel cell with zero-phase ohmic resistance and fixed-low-frequency impedance," Applied Energy, Elsevier, vol. 239(C), pages 785-792.
    11. Li, Zhongliang & Outbib, Rachid & Giurgea, Stefan & Hissel, Daniel & Li, Yongdong, 2015. "Fault detection and isolation for Polymer Electrolyte Membrane Fuel Cell systems by analyzing cell voltage generated space," Applied Energy, Elsevier, vol. 148(C), pages 260-272.
    12. Sharaf, Omar Z. & Orhan, Mehmet F., 2014. "An overview of fuel cell technology: Fundamentals and applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 810-853.
    13. Pei, Pucheng & Li, Yuehua & Xu, Huachi & Wu, Ziyao, 2016. "A review on water fault diagnosis of PEMFC associated with the pressure drop," Applied Energy, Elsevier, vol. 173(C), pages 366-385.
    14. Wu, Xiao-long & Xu, Yuan-Wu & Xue, Tao & Zhao, Dong-qi & Jiang, Jianhua & Deng, Zhonghua & Fu, Xiaowei & Li, Xi, 2019. "Health state prediction and analysis of SOFC system based on the data-driven entire stage experiment," Applied Energy, Elsevier, vol. 248(C), pages 126-140.
    15. Uday K. Chakraborty, 2019. "Proton Exchange Membrane Fuel Cell Stack Design Optimization Using an Improved Jaya Algorithm," Energies, MDPI, vol. 12(16), pages 1-26, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Khadijeh Hooshyari & Bahman Amini Horri & Hamid Abdoli & Mohsen Fallah Vostakola & Parvaneh Kakavand & Parisa Salarizadeh, 2021. "A Review of Recent Developments and Advanced Applications of High-Temperature Polymer Electrolyte Membranes for PEM Fuel Cells," Energies, MDPI, vol. 14(17), pages 1-38, September.
    2. Guarino, Antonio & Trinchero, Riccardo & Canavero, Flavio & Spagnuolo, Giovanni, 2022. "A fast fuel cell parametric identification approach based on machine learning inverse models," Energy, Elsevier, vol. 239(PC).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Young Park, Jin & Seop Lim, In & Ho Lee, Yeong & Lee, Won-Yong & Oh, Hwanyeong & Soo Kim, Min, 2023. "Severity-based fault diagnostic method for polymer electrolyte membrane fuel cell systems," Applied Energy, Elsevier, vol. 332(C).
    2. Mingfei Li & Zhengpeng Chen & Jiangbo Dong & Kai Xiong & Chuangting Chen & Mumin Rao & Zhiping Peng & Xi Li & Jingxuan Peng, 2022. "A Data-Driven Fault Diagnosis Method for Solid Oxide Fuel Cell Systems," Energies, MDPI, vol. 15(7), pages 1-16, March.
    3. Xu, Yuan-wu & Wu, Xiao-long & Zhong, Xiao-bo & Zhao, Dong-qi & Sorrentino, Marco & Jiang, Jianhua & Jiang, Chang & Fu, Xiaowei & Li, Xi, 2021. "Mechanism model-based and data-driven approach for the diagnosis of solid oxide fuel cell stack leakage," Applied Energy, Elsevier, vol. 286(C).
    4. Zhang, S. & Reimer, U. & Beale, S.B. & Lehnert, W. & Stolten, D., 2019. "Modeling polymer electrolyte fuel cells: A high precision analysis," Applied Energy, Elsevier, vol. 233, pages 1094-1103.
    5. Ying Tian & Qiang Zou & Jin Han, 2021. "Data-Driven Fault Diagnosis for Automotive PEMFC Systems Based on the Steady-State Identification," Energies, MDPI, vol. 14(7), pages 1-17, March.
    6. Pang, Ran & Zhang, Caizhi & Dai, Haifeng & Bai, Yunfeng & Hao, Dong & Chen, Jinrui & Zhang, Bin, 2022. "Intelligent health states recognition of fuel cell by cell voltage consistency under typical operating parameters," Applied Energy, Elsevier, vol. 305(C).
    7. Qaisar Abbas & Mojtaba Mirzaeian & Michael R.C. Hunt & Peter Hall & Rizwan Raza, 2020. "Current State and Future Prospects for Electrochemical Energy Storage and Conversion Systems," Energies, MDPI, vol. 13(21), pages 1-41, November.
    8. Pedro Andrade & Khaled Laadjal & Adérito Neto Alcaso & Antonio J. Marques Cardoso, 2024. "A Comprehensive Review on Condition Monitoring and Fault Diagnosis in Fuel Cell Systems: Challenges and Issues," Energies, MDPI, vol. 17(3), pages 1-45, January.
    9. Zhong, Xiaobo & Xu, Yuanwu & Liu, Yanlin & Wu, Xiaolong & Zhao, Dongqi & Zheng, Yi & Jiang, Jianhua & Deng, Zhonghua & Fu, Xiaowei & Li, Xi, 2020. "Root cause analysis and diagnosis of solid oxide fuel cell system oscillations based on data and topology-based model," Applied Energy, Elsevier, vol. 267(C).
    10. Logan Battrell & Aubree Trunkle & Erica Eggleton & Lifeng Zhang & Ryan Anderson, 2017. "Quantifying Cathode Water Transport via Anode Relative Humidity Measurements in a Polymer Electrolyte Membrane Fuel Cell," Energies, MDPI, vol. 10(8), pages 1-16, August.
    11. Gabriele Loreti & Andrea Luigi Facci & Stefano Ubertini, 2021. "High-Efficiency Combined Heat and Power through a High-Temperature Polymer Electrolyte Membrane Fuel Cell and Gas Turbine Hybrid System," Sustainability, MDPI, vol. 13(22), pages 1-24, November.
    12. Peng, Fei & Zhao, Yuanzhe & Li, Xiaopeng & Liu, Zhixiang & Chen, Weirong & Liu, Yang & Zhou, Donghua, 2017. "Development of master-slave energy management strategy based on fuzzy logic hysteresis state machine and differential power processing compensation for a PEMFC-LIB-SC hybrid tramway," Applied Energy, Elsevier, vol. 206(C), pages 346-363.
    13. Liao, Shuxin & Qiu, Diankai & Yi, Peiyun & Peng, Linfa & Lai, Xinmin, 2022. "Modeling of a novel cathode flow field design with optimized sub-channels to improve drainage for proton exchange membrane fuel cells," Energy, Elsevier, vol. 261(PB).
    14. Li, Yuehua & Pei, Pucheng & Ma, Ze & Ren, Peng & Wu, Ziyao & Chen, Dongfang & Huang, Hao, 2019. "Characteristic analysis in lowering current density based on pressure drop for avoiding flooding in proton exchange membrane fuel cell," Applied Energy, Elsevier, vol. 248(C), pages 321-329.
    15. Chang, Huawei & Wan, Zhongmin & Zheng, Yao & Chen, Xi & Shu, Shuiming & Tu, Zhengkai & Chan, Siew Hwa & Chen, Rui & Wang, Xiaodong, 2017. "Energy- and exergy-based working fluid selection and performance analysis of a high-temperature PEMFC-based micro combined cooling heating and power system," Applied Energy, Elsevier, vol. 204(C), pages 446-458.
    16. Lin, Jui-Yen & Shih, Yu-Jen & Chen, Po-Yen & Huang, Yao-Hui, 2016. "Precipitation recovery of boron from aqueous solution by chemical oxo-precipitation at room temperature," Applied Energy, Elsevier, vol. 164(C), pages 1052-1058.
    17. Wang, Chu & Li, Zhongliang & Outbib, Rachid & Dou, Manfeng & Zhao, Dongdong, 2022. "Symbolic deep learning based prognostics for dynamic operating proton exchange membrane fuel cells," Applied Energy, Elsevier, vol. 305(C).
    18. Wang, Junye, 2015. "Theory and practice of flow field designs for fuel cell scaling-up: A critical review," Applied Energy, Elsevier, vol. 157(C), pages 640-663.
    19. Pei, Pucheng & Wu, Ziyao & Li, Yuehua & Jia, Xiaoning & Chen, Dongfang & Huang, Shangwei, 2018. "Improved methods to measure hydrogen crossover current in proton exchange membrane fuel cell," Applied Energy, Elsevier, vol. 215(C), pages 338-347.
    20. Xuexia Zhang & Zixuan Yu & Weirong Chen, 2019. "Life Prediction Based on D-S ELM for PEMFC," Energies, MDPI, vol. 12(19), pages 1-15, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:14:p:3643-:d:384736. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.