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Maximum power efficiency operation and generalized predictive control of PEM (proton exchange membrane) fuel cell

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  • Li, Dazi
  • Yu, Yadi
  • Jin, Qibing
  • Gao, Zhiqiang

Abstract

Operating a proton exchange membrane fuel cell (PEMFC) system to produce power at the maximum power efficiency is one of the key issues in PEMFC's wide-spread applications. However, power density exhibits complex behavior and nonlinear dynamics with respect to the output cell voltage, fuel cell temperature, anode and cathode pressure, inlet gas humidity, and so on. In this paper, the distribution of power density in the domain of the output cell voltage and fuel cell temperature is delineated. By this delineation, the quadratic polynomial fitting was used to approximate the power density curve in local interval and estimate the maximum power efficiency point. Generalized predictive control (GPC) is presented to overcome the problem of time-varying dynamics of PEMFC in real time via applying a forgetting factor recursive least square (FFRLS) method. Based on the approximation and generalized predictive control strategy, maximum power efficiency operation of PEMFC is applied. The results of this work can contribute to the operation of PEMFC at the maximum power point, which guarantees the plant generating maximum power at the lowest consumption of hydrogen.

Suggested Citation

  • Li, Dazi & Yu, Yadi & Jin, Qibing & Gao, Zhiqiang, 2014. "Maximum power efficiency operation and generalized predictive control of PEM (proton exchange membrane) fuel cell," Energy, Elsevier, vol. 68(C), pages 210-217.
    • Handle: RePEc:eee:energy:v:68:y:2014:i:c:p:210-217
    DOI: 10.1016/j.energy.2014.02.104
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    References listed on IDEAS

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    4. Li, Qi & Wang, Tianhong & Li, Shihan & Chen, Weirong & Liu, Hong & Breaz, Elena & Gao, Fei, 2021. "Online extremum seeking-based optimized energy management strategy for hybrid electric tram considering fuel cell degradation," Applied Energy, Elsevier, vol. 285(C).
    5. Barzegari, Mohammad M. & Alizadeh, Ebrahim & Pahnabi, Amir H., 2017. "Grey-box modeling and model predictive control for cascade-type PEMFC," Energy, Elsevier, vol. 127(C), pages 611-622.
    6. Kong, Xiaobing & Liu, Xiangjie & Lee, Kwang Y., 2015. "Nonlinear multivariable hierarchical model predictive control for boiler-turbine system," Energy, Elsevier, vol. 93(P1), pages 309-322.
    7. Sun, Li & Jin, Yuhui & You, Fengqi, 2020. "Active disturbance rejection temperature control of open-cathode proton exchange membrane fuel cell," Applied Energy, Elsevier, vol. 261(C).
    8. Jiang, Qingfeng & Liang, Wenlong & Zhu, Ze & Li, Yiliang & Wang, Pengfei, 2023. "Multimodel generalized predictive control of a heat-pipe reactor coupled with an open-air Brayton cycle," Energy, Elsevier, vol. 279(C).
    9. Víctor Sanz i López & Ramon Costa-Castelló & Carles Batlle, 2022. "Literature Review of Energy Management in Combined Heat and Power Systems Based on High-Temperature Proton Exchange Membrane Fuel Cells for Residential Comfort Applications," Energies, MDPI, vol. 15(17), pages 1-22, September.
    10. Iranzo, Alfredo & Boillat, Pierre & Biesdorf, Johannes & Salva, Antonio, 2015. "Investigation of the liquid water distributions in a 50 cm2 PEM fuel cell: Effects of reactants relative humidity, current density, and cathode stoichiometry," Energy, Elsevier, vol. 82(C), pages 914-921.
    11. 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.
    12. Yang, Zirong & Du, Qing & Jia, Zhiwei & Yang, Chunguang & Jiao, Kui, 2019. "Effects of operating conditions on water and heat management by a transient multi-dimensional PEMFC system model," Energy, Elsevier, vol. 183(C), pages 462-476.

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