| Summary: | 碩士 === 國立臺灣大學 === 土木工程學研究所 === 107 === In recent years, along with raising environmental consciousness, an innovative transportation mode—electric vehicles have gotten many attentions. Because the power source of electric vehicles is electric power, a renewable and environmentally friendly energy, worldwide countries have been devoted to the popularization of electric vehicles and Taiwan is no exception. However, there are two principal challenges for the penetration of electric vehicles: the limited endurance of the mode and its expensive batteries. The limited endurance lets the user has to refuel the energy during a certain using period and the problem of range anxiety comes out. Hence, an auxiliary equipment—the refueling facility is necessary for electric vehicles and it is essential to locate the charger appropriately. Actually, there already are many types of refueling methods and one of the innovative and promising ways is the swapping system. The swapping system reduces the entire refueling time from original several hours to a few minutes or even seconds. This user-friendly advantage lets the user conducts the refueling on the road become much more possible but on the other side, this benefit also increases the need for spare batteries. Therefore, not only the location but also the capacity of swapping stations has to be cautiously determined by the operator. Nevertheless, it is thorny to decide where and how big the swapping station should be directly, so the purpose of this research is to develop a model for deploying the refueling facilities of the swapping system. In addition, because the most popularized transportation mode in Taiwan is scooters, the research object of this study is electric scooters. In order to enhance the applicability of the model and depict the usage behavior conveniently, the methodology adopted in this study combines the genetic algorithm and the discrete-event simulation forming a two-stage planning. In addition, considering the usage habits of the swapping system, the notion of flow interception is adopted in the model.
After the sensitivity analysis of experiments, it is found that different parameters have diverse impacts on different shapes of demand spatial distribution. Regular spatial distribution is much more sensitive to the budget and the irregular one is sensitive to the power consumption rate of batteries. Noteworthily, a factor significantly influencing both the shape of demand distribution is the driving endurance. With the case study in the reality, some setting instructions for such a location problem have been obtained. The operator who is going to plan a layout for swapping stations should adopt the entire traffic “flow” rather than the traffic “zone”. Besides, the operator should give a locating priority to the station where is near heavy traffic flows regardless of the shape of demand distributions. Further, considering the user’s maximum tolerable driving distance for swapping, the operator should radially deploy the station taking origins and destinations as the center.
Many important factors are considered in the model such as the demand uncertainty, driving endurance, the tolerance of the user, usage behavior and the varied charging power levels by charging time. This is the first time that a model takes these significant factors into account at the same time. What’s more, after the experiments and the case study, the utility and validity of the model are exhibited and these results also show the importance of considering usage behavior. Thus, this model can be a useful tool to help the operator to decide a proper deployment of swapping stations for electric scooters.
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