Increasing sales of conventional fuel-based vehicles are leading to an increase in carbon emissions, which are dangerous to the environment. To reduce these, conventional fuel-based vehicles must be replaced with alternative fuel vehicles such as hydrogen-fueled. Hydrogen can fuel vehicles with near-zero greenhouse gas emissions. However, to increase the penetration of such alternative fuel vehicles, there needs to be adequate infrastructure, specifically, refueling infrastructure, in place. This paper presents a comprehensive review of the different optimization strategies and methods used in the location of hydrogen refueling stations. The findings of the review in this paper show that there are various methods which can be used to optimally locate refueling stations, the most popular being the p-median and flow-capture location models. It is also evident from the review that there are limited studies that consider location strategies of hydrogen refueling stations within a rural setting; most studies are focused on urban locations due to the high probability of penetration into these areas. Furthermore, it is apparent that there is still a need to incorporate factors such as the safety elements of hydrogen refueling station construction, and for risk assessments to provide more robust, realistic solutions for the optimal location of hydrogen refueling stations. Hence, the methods reviewed in this paper can be used and expanded upon to create useful and accurate models for a hydrogen refueling network. Furthermore, this paper will assist future studies to achieve an understanding of the extant studies on hydrogen refueling station and their optimal location strategies.
This paper presents a model to predict the number of refuelling trips by vehicles on any given day considering weather conditions and time of the year. The predicted refuelling trips were founded on count-based data, i.e., data that contain events that occur at a certain rate. The paper presents an algorithm developed using Python programming language and the statsmodels module to achieve this. The results indicate that the GP-1 model developed in this paper is statistically significant at the 95% confidence level as it was able to converge—however, precipitation and high ambient temperature conditions are considered statistically insignificant in this model. The viability of the model was further tested on the remaining 20% of the data. Sensitivity tests indicate that there is a good correlation between the actual trips and predicted trips when 70% of the data are used to train the model. Overall, the model presented can be used to predict the number of trips taken by vehicles to refuel as well as model future trends, accurately. This model, can in the future, be applied to predict the refuelling behaviour of alternative fuel vehicles such as hydrogen fuel vehicles, when such data become available.
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