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Generalized Regression Neural Network Based Meta-Heuristic Algorithms for Parameter Identification of Proton Exchange Membrane Fuel Cell

Author

Listed:
  • Peng He

    (Electric Power Science Institute, Yunnan Power Grid Co., Ltd., Kunming 650000, China)

  • Xin Zhou

    (Electric Power Science Institute, Yunnan Power Grid Co., Ltd., Kunming 650000, China)

  • Mingqun Liu

    (Electric Power Science Institute, Yunnan Power Grid Co., Ltd., Kunming 650000, China)

  • Kewei Xu

    (Electric Power Science Institute, Yunnan Power Grid Co., Ltd., Kunming 650000, China)

  • Xian Meng

    (Electric Power Science Institute, Yunnan Power Grid Co., Ltd., Kunming 650000, China)

  • Bo Yang

    (Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, China)

Abstract

An accurate parameter extraction of the proton exchange membrane fuel cell (PEMFC) is crucial for establishing a reliable cell model, which is also of great significance for subsequent research on the PEMFC. However, because the parameter identification of the PEMFC is a nonlinear optimization problem with multiple variables, peaks, and a strong coupling, it is difficult to solve this problem using traditional numerical methods. Furthermore, because of insufficient current and voltage data measured by the PEMFC, the precision rate of cell parameter extraction is also very low. The study proposes a parameter extraction method using a generalized regression neural network (GRNN) and meta-heuristic algorithms (MhAs). First of all, a GRNN is used to de-noise and predict the data to solve the problems in the field of PEMFC, which include insufficient data and excessive noise data of the measured data. After that, six typical algorithms are used to extract the parameters of the PEMFC under three operating conditions, namely high temperature and low pressure (HTLP), medium temperature and medium pressure (MTMP), and low temperature and high pressure (LTHP). The last results demonstrate that the application of GRNN can prominently decrease the influence of data noise on parameter identification, and after data prediction, it can greatly enhance the precision rate and reliability of MhAs parameter identification, specifically, under HTLP conditions, the V - I fitting accuracy achieved 99.39%, the fitting accuracy was 99.07% on MTMP, and the fitting accuracy was 98.70%.

Suggested Citation

  • Peng He & Xin Zhou & Mingqun Liu & Kewei Xu & Xian Meng & Bo Yang, 2023. "Generalized Regression Neural Network Based Meta-Heuristic Algorithms for Parameter Identification of Proton Exchange Membrane Fuel Cell," Energies, MDPI, vol. 16(14), pages 1-30, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:14:p:5290-:d:1190965
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    References listed on IDEAS

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    1. Sun, Zhe & Cao, Dan & Ling, Yawen & Xiang, Feng & Sun, Zhixin & Wu, Fan, 2021. "Proton exchange membrane fuel cell model parameter identification based on dynamic differential evolution with collective guidance factor algorithm," Energy, Elsevier, vol. 216(C).
    2. Yang, Bo & Li, Danyang & Zeng, Chunyuan & Chen, Yijun & Guo, Zhengxun & Wang, Jingbo & Shu, Hongchun & Yu, Tao & Zhu, Jiawei, 2021. "Parameter extraction of PEMFC via Bayesian regularization neural network based meta-heuristic algorithms," Energy, Elsevier, vol. 228(C).
    3. Sun, Zhe & Wang, Ning & Bi, Yunrui & Srinivasan, Dipti, 2015. "Parameter identification of PEMFC model based on hybrid adaptive differential evolution algorithm," Energy, Elsevier, vol. 90(P2), pages 1334-1341.
    4. Jones, D. F. & Mirrazavi, S. K. & Tamiz, M., 2002. "Multi-objective meta-heuristics: An overview of the current state-of-the-art," European Journal of Operational Research, Elsevier, vol. 137(1), pages 1-9, February.
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