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Using ANFIS technique for PEM fuel cell electric bicycle prediction model

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Abstract

Proton exchange membrane fuel cell (PEMFC) with low emission is considered as a promising vehicular industry. To achieve higher performance, modelling of the whole PEMFC system is an essential step in designing the most efficient system. In this study, the adaptive neuro-fuzzy inference system (ANFIS) is employed to model the 250-W PEMFC which is located in an electric bicycle. The temperature, humidity, current, hydrogen and oxygen flowrate were used as the inputs and voltage and efficiency employed as the outputs. The analyses of results determine that ANFIS is an accurate and reliable technique for predicting the PEMFC performance.

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Acknowledgements

We thank the TDTU for their support.

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Authors and Affiliations

  1. Department of Electrical Engineering, Faculty of Engineering, University Malaya, 50603, Kuala Lumpur, Malaysia

    A. Kheirandish & M. Dahari

  2. Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Viet Nam

    E. Akbari

  3. Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Viet Nam

    E. Akbari

  4. Halal Research Center of IRI, FDA, Tehran, Iran

    M. Nilashi

Authors
  1. A. Kheirandish
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  2. E. Akbari
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  3. M. Nilashi
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  4. M. Dahari
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Correspondence to E. Akbari.

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The author(s) declare that there is no conflict of interest regarding the publication of this article.

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Editorial responsibility: M. Abbaspour.

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Kheirandish, A., Akbari, E., Nilashi, M. et al. Using ANFIS technique for PEM fuel cell electric bicycle prediction model. Int. J. Environ. Sci. Technol. 16, 7319–7326 (2019). https://doi.org/10.1007/s13762-019-02392-6

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  • DOI: https://doi.org/10.1007/s13762-019-02392-6

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