Abstract
Currently, more than 30% of the fine dust generated in the Seoul metropolitan area is a pollutant emitted from automobiles such as diesel vehicles, and air pollution caused by this is becoming increasingly serious. In addition, the importance of electric vehicle distribution is increasing due to the strengthening of international environmental regulations on automobile exhaust gas and increasing the possibility of depletion of petroleum resources. This manuscript proposes a method for selecting an optimal electric vehicle charging station location in expanding charging facilities to activate electric vehicle distribution. For the sake of illustration, directions will be provided on how to select the best location for electric vehicle charging stations using data from Seoul, which has the best access. As the features, the number of living population and work force people and the number of guest facilities, which are determined to affect demand for quick charging, are considered. The missing values of the observed data are imputed based on the kriging technique from spatial correlation, and by segmenting the data through clustering, a representative technique of unsupervised learning, the characteristics of each cluster are examined and the characteristics of the clusters are identified. In addition, machine learning techniques such as the elastic net, random forest, support vector machine, and extreme gradient boosting are applied to examine the influence of the features used in predicting classes of data. In clustering analysis, the optimal number of clusters was determined to be 3 based on the heuristic and information-theoretic methods, and all the machine learning techniques considered showed that the number of work force population is the most important feature in predicting classes of data. All things considered from our results, it is reasonable to install quick electric vehicle charging stations in the places with the highest concentration of work force population and guest facility.
Highlights
The slow charger takes an average of four to five hours to buffer one electric vehicles (EVs), which greatly reduces user convenience due to their lower turnover rate compared to gas stations. This makes users prefer the quick charger, especially when driving in the suburbs, where quick charging infrastructure is scarce, the demand for the quick charger is bound to increase. In keeping with these issues, this paper focuses on optimal allocation methods for quick charging stations for building quick charging infrastructures unlike existing studies, and studies efficient customized strategies to find areas that are expected to be in high demand through clustering analysis and machine learning techniques
This paper proposes a methodology for optimal quick charging station allocation to build an efficient EV quick charging station infrastructure to expand the distribution of
EVs, an alternative to diesel vehicles, which are the main cause of environmental pollution
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. If internal combustion locomotives are replaced with electric vehicles (EVs) that generate driving power by supplying electric energy from high-voltage batteries to electric motors, the air pollution problem caused by cars can be solved drastically because they do not emit air pollutants and greenhouse gases For this reason, on 18 December 2018, the Ministry of Trade, Industry and Energy announced that it has significantly raised its domestic supply target for EVs to 430,000 units by 2022 [1]. This makes users prefer the quick charger, especially when driving in the suburbs, where quick charging infrastructure is scarce, the demand for the quick charger is bound to increase In keeping with these issues, this paper focuses on optimal allocation methods for quick charging stations for building quick charging infrastructures unlike existing studies, and studies efficient customized strategies to find areas that are expected to be in high demand through clustering analysis and machine learning techniques.
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