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Dynamic fuzzy cognitive network approach for modelling and control of PEM fuel cell for power electric bicycle system

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  • Kheirandish, Azadeh
  • Motlagh, Farid
  • Shafiabady, Niusha
  • Dahari, Mahidzal
  • Khairi Abdul Wahab, Ahmad

Abstract

Modelling Proton Exchange Membrane Fuel Cell (PEMFC) is the fundamental step in designing efficient systems for achieving higher performance. Among the development of new energy technologies, modelling and optimization of energy processes with pollution reduction, sufficient efficiency and low emission are considered one of the most promising areas of study. Despite affecting factors in PEMFC functionality, providing a reliable model for PEMFC is the key of performance optimization challenge. In this paper, fuzzy cognitive map has been used for modelling PEMFC system that is directed to provide a dynamic cognitive map from the affecting factors of the system. Controlling and modification of the system performance in various conditions is more practical by correlations among the performance factors of the PEMFC derived from fuzzy cognitive maps. On the other hand, the information of fuzzy cognitive map modelling is applicable for modification of neural networks structure for providing more accurate results based on the extracted knowledge from the cognitive map and visualization of the system’s performance. Finally, a rule based fuzzy cognitive map has been used that can be implemented for decision-making to control the system. This rule-based approach provides interpretability while enhancing the performance of the overall system.

Suggested Citation

  • Kheirandish, Azadeh & Motlagh, Farid & Shafiabady, Niusha & Dahari, Mahidzal & Khairi Abdul Wahab, Ahmad, 2017. "Dynamic fuzzy cognitive network approach for modelling and control of PEM fuel cell for power electric bicycle system," Applied Energy, Elsevier, vol. 202(C), pages 20-31.
    • Handle: RePEc:eee:appene:v:202:y:2017:i:c:p:20-31
    DOI: 10.1016/j.apenergy.2017.05.084
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Hou, Junbo & Yang, Min & Ke, Changchun & Zhang, Junliang, 2020. "Control logics and strategies for air supply in PEM fuel cell engines," Applied Energy, Elsevier, vol. 269(C).
    2. Zou, Wei & Froning, Dieter & Shi, Yan & Lehnert, Werner, 2020. "A least-squares support vector machine method for modeling transient voltage in polymer electrolyte fuel cells," Applied Energy, Elsevier, vol. 271(C).
    3. Asensio, F.J. & San Martín, J.I. & Zamora, I. & Saldaña, G. & Oñederra, O., 2019. "Analysis of electrochemical and thermal models and modeling techniques for polymer electrolyte membrane fuel cells," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    4. Ba Hung, Nguyen & Lim, Ocktaeck, 2019. "The effects of operating conditions and structural parameters on the dynamic, electric consumption and power generation characteristics of an electric assisted bicycle," Applied Energy, Elsevier, vol. 247(C), pages 285-296.
    5. Chen, Kui & Laghrouche, Salah & Djerdir, Abdesslem, 2019. "Degradation model of proton exchange membrane fuel cell based on a novel hybrid method," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    6. Yang, Duo & Pan, Rui & Wang, Yujie & Chen, Zonghai, 2019. "Modeling and control of PEMFC air supply system based on T-S fuzzy theory and predictive control," Energy, Elsevier, vol. 188(C).
    7. 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.
    8. Hieu, Le Trong & Lim, Ock Taeck, 2024. "Deep learning application in fuel cell electric bicycle to optimize bicycle performance and energy consumption under the effect of key input parameters," Applied Energy, Elsevier, vol. 369(C).
    9. Miao, Di & Chen, Wei & Zhao, Wei & Demsas, Tekle, 2020. "Parameter estimation of PEM fuel cells employing the hybrid grey wolf optimization method," Energy, Elsevier, vol. 193(C).

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