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Comprehensive analysis of galvanostatic charge method for fuel cell degradation diagnosis

Author

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  • Hu, Zunyan
  • Xu, Liangfei
  • Huang, Yiyuan
  • Li, Jianqiu
  • Ouyang, Minggao
  • Du, Xiaoli
  • Jiang, Hongliang

Abstract

Cyclic voltammetry and linear sweep voltammetry are the most commonly used diagnosis methods to estimate the internal statement of fuel cell stack. However, both methods can only be applied in a single fuel cell. There is a lack of suitable in situ diagnosis methods for a multi-burl fuel cell stack. The galvanostatic charge method (GSC) is a very convenient in situ diagnosis method, which can be applied to a multi-burl fuel cell stack to calculate the electrochemical active surface area (ECSA), double-layer capacitance, and hydrogen crossover current. However, there are not enough experiments to analyze the adaption of GSC or apply this method to analyze fuel cell degradation process. In this study, we conducted experiments to validate the accuracy of GSC under different test conditions, and proposed a new correction algorithm to improve the accuracy. Next, this method was applied to analyze the performance degradation process of a four-cell stack. The experimental results showed that the estimated GSC parameters exactly coincide with the standard values. Additionally, for the degradation analysis of a four-cell stack, the GSC results showed that the ECSA reduction of cell four is the direct reason for performance degradation. Moreover, the assumptions of ECSA reduction and carbon corrosion were validated by material experiments. About 5° decrease in the contact angle of the gas diffusion layer (GDL) in the cathode catalyst was observed in the worst cell.

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  • Hu, Zunyan & Xu, Liangfei & Huang, Yiyuan & Li, Jianqiu & Ouyang, Minggao & Du, Xiaoli & Jiang, Hongliang, 2018. "Comprehensive analysis of galvanostatic charge method for fuel cell degradation diagnosis," Applied Energy, Elsevier, vol. 212(C), pages 1321-1332.
  • Handle: RePEc:eee:appene:v:212:y:2018:i:c:p:1321-1332
    DOI: 10.1016/j.apenergy.2018.01.005
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    4. Xu, Liangfei & Hu, Zunyan & Fang, Chuan & Li, Jianqiu & Hong, Po & Jiang, Hongliang & Guo, Di & Ouyang, Minggao, 2021. "Anode state observation of polymer electrolyte membrane fuel cell based on unscented Kalman filter and relative humidity sensor before flooding," Renewable Energy, Elsevier, vol. 168(C), pages 1294-1307.
    5. Xu, Liangfei & Fang, Chuan & Li, Jianqiu & Ouyang, Minggao & Lehnert, Werner, 2018. "Nonlinear dynamic mechanism modeling of a polymer electrolyte membrane fuel cell with dead-ended anode considering mass transport and actuator properties," Applied Energy, Elsevier, vol. 230(C), pages 106-121.
    6. Song, Ziyou & Hofmann, Heath & Lin, Xinfan & Han, Xuebing & Hou, Jun, 2018. "Parameter identification of lithium-ion battery pack for different applications based on Cramer-Rao bound analysis and experimental study," Applied Energy, Elsevier, vol. 231(C), pages 1307-1318.
    7. Zhao, Lei & Hong, Jichao & Xie, Jiaping & Jiang, Shangfeng & Wei, Xuezhe & Ming, Pingwen & Dai, Haifeng, 2023. "Investigation of local sensitivity for vehicle-oriented fuel cell stacks based on electrochemical impedance spectroscopy," Energy, Elsevier, vol. 262(PA).
    8. Pan, Mingzhang & Pan, Chengjie & Li, Chao & Zhao, Jian, 2021. "A review of membranes in proton exchange membrane fuel cells: Transport phenomena, performance and durability," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    9. Li, Sida & Wei, Xuezhe & Jiang, Shangfeng & Yuan, Hao & Ming, Pingwen & Wang, Xueyuan & Dai, Haifeng, 2022. "Hydrogen crossover diagnosis for fuel cell stack: An electrochemical impedance spectroscopy based method," Applied Energy, Elsevier, vol. 325(C).
    10. Song, Ziyou & Feng, Shuo & Zhang, Lei & Hu, Zunyan & Hu, Xiaosong & Yao, Rui, 2019. "Economy analysis of second-life battery in wind power systems considering battery degradation in dynamic processes: Real case scenarios," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    11. 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).
    12. Wu, Ziyao & Pei, Pucheng & Xu, Huachi & Jia, Xiaoning & Ren, Peng & Wang, Bozheng, 2019. "Study on the effect of membrane electrode assembly parameters on polymer electrolyte membrane fuel cell performance by galvanostatic charging method," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    13. Ma, Rui & Yang, Tao & Breaz, Elena & Li, Zhongliang & Briois, Pascal & Gao, Fei, 2018. "Data-driven proton exchange membrane fuel cell degradation predication through deep learning method," Applied Energy, Elsevier, vol. 231(C), pages 102-115.
    14. Zhang, Tong & Wang, Peiqi & Chen, Huicui & Pei, Pucheng, 2018. "A review of automotive proton exchange membrane fuel cell degradation under start-stop operating condition," Applied Energy, Elsevier, vol. 223(C), pages 249-262.

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