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An Intelligent Site Selection Model for Hydrogen Refueling Stations Based on Fuzzy Comprehensive Evaluation and Artificial Neural Network—A Case Study of Shanghai

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  • Yan Zhou

    (Hubei Collaborative Innovation Center for Automotive Components Technology, Hubei Key Laboratory of Advanced Technology for Automotive Components, School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China
    School of Mechanical and Electrical Engineering, Wuhan Business University, Wuhan 430056, China)

  • Xunpeng Qin

    (Hubei Collaborative Innovation Center for Automotive Components Technology, Hubei Key Laboratory of Advanced Technology for Automotive Components, School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China)

  • Chenglong Li

    (Hubei Collaborative Innovation Center for Automotive Components Technology, Hubei Key Laboratory of Advanced Technology for Automotive Components, School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China
    Automobile Technology and Service College, Wuhan City Polytechnic, Wuhan, 430064, China)

  • Jun Zhou

    (China Automotive Technology and Research Center (Wuhan), Wuhan 430056, China)

Abstract

With the gradual popularization of hydrogen fuel cell vehicles (HFCVs), the construction and planning of hydrogen refueling stations (HRSs) are increasingly important. Taking operational HRSs in China’s coastal and major cities as examples, we consider the main factors affecting the site selection of HRSs in China from the three aspects of economy, technology and society to establish a site selection evaluation system for hydrogen refueling stations and determine the weight of each index through the analytic hierarchy process (AHP). Then, combined with fuzzy comprehensive evaluation (FCE) method and artificial neural network model (ANN), FCE method is used to evaluate HRS in operation in China’s coastal areas and major cities, and we used the resulting data obtained from the comprehensive evaluation as the training data to train the neural network. So, an intelligent site selection model for HRSs based on fuzzy comprehensive evaluation and artificial neural network model (FCE-ANN) is proposed. The planned HRSs in Shanghai are evaluated, and an optimal site selection of the HRS is obtained. The results show that the optimal HRSs site selected by the FCE-ANN model is consistent with the site selection obtained by the FCE method, and the accuracy of the FCE-ANN model is verified. The findings of this study may provide some guidelines for policy makers in planning the hydrogen refueling stations.

Suggested Citation

  • Yan Zhou & Xunpeng Qin & Chenglong Li & Jun Zhou, 2022. "An Intelligent Site Selection Model for Hydrogen Refueling Stations Based on Fuzzy Comprehensive Evaluation and Artificial Neural Network—A Case Study of Shanghai," Energies, MDPI, vol. 15(3), pages 1-23, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:3:p:1098-:d:740603
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    References listed on IDEAS

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    Cited by:

    1. Long Li & Shuqi Wang & Shengxi Zhang & Ding Liu & Shengbin Ma, 2023. "The Hydrogen Energy Infrastructure Location Selection Model: A Hybrid Fuzzy Decision-Making Approach," Sustainability, MDPI, vol. 15(13), pages 1-20, June.
    2. Lisha Jiang & Liang Wang, 2025. "A Bi-Level Optimization Model for Hydrogen Station Location Considering Hydrogen Cost and Range Anxiety," Sustainability, MDPI, vol. 17(7), pages 1-22, April.
    3. Kun Xu & Shuang Li & Jiao Liu & Cheng Lu & Guangzhe Xue & Zhengquan Xu & Chao He, 2022. "Evaluation Cloud Model of Spontaneous Combustion Fire Risk in Coal Mines by Fusing Interval Gray Number and DEMATEL," Sustainability, MDPI, vol. 14(23), pages 1-13, November.

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