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Parameter extraction of polymer electrolyte membrane fuel cell based on quasi-dynamic model and periphery signals

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  • Xu, Liangfei
  • Fang, Chuan
  • Hu, Junming
  • Cheng, Siliang
  • Li, Jianqiu
  • Ouyang, Minggao
  • Lehnert, Werner

Abstract

It is important to extract parameters of a polymer electrolyte membrane fuel cell (PEMFC) using periphery signals. The main contribution of this work is to introduce a simple yet effective method for parameter-extraction basing on a quasi-dynamic model for a single PEMFC and periphery signals. The model includes filling-and-emptying sub-models, which set up relations between periphery signals and internal states, and a static water transferring sub-model for the membrane. The parameter-extraction method with 5 steps for 9 key parameters is proposed, drawing on experiments and algorithms of nonlinear least square (NLS) and neural networks (NN). Comparison of the identified parameters to data in literature shows that, the results in our study are reasonable.

Suggested Citation

  • Xu, Liangfei & Fang, Chuan & Hu, Junming & Cheng, Siliang & Li, Jianqiu & Ouyang, Minggao & Lehnert, Werner, 2017. "Parameter extraction of polymer electrolyte membrane fuel cell based on quasi-dynamic model and periphery signals," Energy, Elsevier, vol. 122(C), pages 675-690.
    • Handle: RePEc:eee:energy:v:122:y:2017:i:c:p:675-690
    DOI: 10.1016/j.energy.2017.01.078
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    Cited by:

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    2. Yuan, Hao & Dai, Haifeng & Wei, Xuezhe & Ming, Pingwen, 2020. "A novel model-based internal state observer of a fuel cell system for electric vehicles using improved Kalman filter approach," Applied Energy, Elsevier, vol. 268(C).
    3. Xu, Shuhui & Wang, Yong & Wang, Zhi, 2019. "Parameter estimation of proton exchange membrane fuel cells using eagle strategy based on JAYA algorithm and Nelder-Mead simplex method," Energy, Elsevier, vol. 173(C), pages 457-467.
    4. Wang, Bowen & Wu, Kangcheng & Xi, Fuqiang & Xuan, Jin & Xie, Xu & Wang, Xiaoyang & Jiao, Kui, 2019. "Numerical analysis of operating conditions effects on PEMFC with anode recirculation," Energy, Elsevier, vol. 173(C), pages 844-856.
    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. Seleem, Sameh I. & Hasanien, Hany M. & El-Fergany, Attia A., 2021. "Equilibrium optimizer for parameter extraction of a fuel cell dynamic model," Renewable Energy, Elsevier, vol. 169(C), pages 117-128.
    7. Fathy, Ahmed & Rezk, Hegazy, 2018. "Multi-verse optimizer for identifying the optimal parameters of PEMFC model," Energy, Elsevier, vol. 143(C), pages 634-644.
    8. El-Hay, E.A. & El-Hameed, M.A. & El-Fergany, A.A., 2019. "Optimized Parameters of SOFC for steady state and transient simulations using interior search algorithm," Energy, Elsevier, vol. 166(C), pages 451-461.
    9. Zhang, Qinguo & Tong, Zheming & Tong, Shuiguang & Cheng, Zhewu, 2021. "Self-humidifying effect of air self-circulation system for proton exchange membrane fuel cell engines," Renewable Energy, Elsevier, vol. 164(C), pages 1143-1155.
    10. Fathy, Ahmed & Elaziz, Mohamed Abd & Alharbi, Abdullah G., 2020. "A novel approach based on hybrid vortex search algorithm and differential evolution for identifying the optimal parameters of PEM fuel cell," Renewable Energy, Elsevier, vol. 146(C), pages 1833-1845.
    11. Zhang Peng Du & Andraž Kravos & Christoph Steindl & Tomaž Katrašnik & Stefan Jakubek & Christoph Hametner, 2021. "Physically Motivated Water Modeling in Control-Oriented Polymer Electrolyte Membrane Fuel Cell Stack Models," Energies, MDPI, vol. 14(22), pages 1-20, November.

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