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.
Highlights
Hydrogen energy is known as the cleanest secondary energy, and hydrogen fuel cell vehicles (HFCVs) have attracted much attention due to their near-zero emission and pollution-free advantages [1,2]
This paper will establish an evaluation system for hydrogen refueling stations (HRSs) planning through analytic hierarchy process (AHP), and use fuzzy comprehensive evaluation and artificial neural network model (FCE-ANN) to evaluate and predict the site selection of HRSs under planning
The FCE results of the 9 evaluation indices of 50 HRSs in major cities in China were taken as the input of the ANN, and the ranking of FCE results was obtained by fuzzy logic calculation are used as expected output layer for constructing and training the neural network [27]
Summary
Hydrogen energy is known as the cleanest secondary energy, and hydrogen fuel cell vehicles (HFCVs) have attracted much attention due to their near-zero emission and pollution-free advantages [1,2]. The motivation of this research is to seek to establish an intelligent decision-making model for the site selection of HRSs, which guides the location and construction of HRSs, so as to promote the rapid development of the hydrogen fuel cell vehicle industry. The sustainable development of HRSs plays an important role, so it must be meaningful to use the comprehensive evaluation results of the already operating HRSs as a reference in the planning process of the site selection of HRSs. Artificial neural network (ANN) has the ability to learn to predict behaviors and patterns from a limited set of correct data to make statistics and predict future develop-. This paper will establish an evaluation system for HRSs planning through AHP, and use fuzzy comprehensive evaluation and artificial neural network model (FCE-ANN) to evaluate and predict the site selection of HRSs under planning. A discussion and in-depth analysis of the results; Section 5 summarizes and prospects this study
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