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Fault diagnosis of thermal management system in a polymer electrolyte membrane fuel cell

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  • Park, Jin Young
  • Lim, In Seop
  • Choi, Eun Jung
  • Kim, Min Soo

Abstract

One of the barriers for commercialization of polymer electrolyte membrane fuel cell system is its reliability. One good solution for reliability improvement is to develop fault diagnosis model. This study suggests a diagnostic method for polymer electrolyte membrane fuel cell thermal management system. With the increase of fuel cell stack power these days, waste heat from the stack also increased and thereby thermal management system is also receiving attention. In this study, thermal management system fault experiments are repeatedly performed under various loads and stack degradation conditions. With the degradation of stack, waste heat from the stack increases and fault response as well as its impact on the system varies. The diagnosis model developed in this study detects faults on component-level and diagnoses severity of the faulty components. Furthermore, the diagnosis model is applicable to fuel cell system with degraded stack. In the process of model development, single-task learning technique is applied to neural network diagnosis model for higher diagnostic accuracy and compared with conventional multi-task learning technique.

Suggested Citation

  • Park, Jin Young & Lim, In Seop & Choi, Eun Jung & Kim, Min Soo, 2021. "Fault diagnosis of thermal management system in a polymer electrolyte membrane fuel cell," Energy, Elsevier, vol. 214(C).
    • Handle: RePEc:eee:energy:v:214:y:2021:i:c:s0360544220321691
    DOI: 10.1016/j.energy.2020.119062
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    References listed on IDEAS

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    1. 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.
    2. Shao, Meng & Zhu, Xin-Jian & Cao, Hong-Fei & Shen, Hai-Feng, 2014. "An artificial neural network ensemble method for fault diagnosis of proton exchange membrane fuel cell system," Energy, Elsevier, vol. 67(C), pages 268-275.
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    Citations

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    Cited by:

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    2. Hui, Jiuwu & Yuan, Jingqi, 2022. "Neural network-based adaptive fault-tolerant control for load following of a MHTGR with prescribed performance and CRDM faults," Energy, Elsevier, vol. 257(C).
    3. Cai, Yonghua & Wu, Di & Sun, Jingming & Chen, Ben, 2021. "The effect of cathode channel blockages on the enhanced mass transfer and performance of PEMFC," Energy, Elsevier, vol. 222(C).
    4. Taehyung Koo & Rockkil Ko & Dongwoo Ha & Jaeyoung Han, 2023. "Development of Model-Based PEM Water Electrolysis HILS (Hardware-in-the-Loop Simulation) System for State Evaluation and Fault Detection," Energies, MDPI, vol. 16(8), pages 1-18, April.
    5. 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).
    6. Wang, Pengfei & Zhang, Jiaxuan & Wan, Jiashuang & Wu, Shifa, 2022. "A fault diagnosis method for small pressurized water reactors based on long short-term memory networks," Energy, Elsevier, vol. 239(PC).
    7. Lin, Rui & Wang, Hong & Zhu, Yu, 2021. "Optimizing the structural design of cathode catalyst layer for PEM fuel cells for improving mass-specific power density," Energy, Elsevier, vol. 221(C).
    8. 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).

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