Location Selection of Electric Vehicles Charging Stations Based on Analytical Hierarchy Process and Clustering Algorithm

Abstract

In recent years, the charging infrastructure for electric vehicles (EVs) in China has been developing quickly, yet the charging station industry still faces problems such as unreasonable layout, difficulty in profitability and limited coverage. Based on this, this paper systematically compared the key factors of charging facility location and used the AHP method to measure the degree of influence of various factors at different levels on the location of charging stations of enterprises, so as to establish a scientific and reasonable evaluation model for the location of charging stations. Secondly, we selected 12 typical enterprises such as TELD NEW ENERGY Co., Ltd.from the dimensions of company scale and strategic objectives, and then classified them into state-owned enterprises, vehicle enterprises and specialized operating enterprises through k-means clustering. Finally it turns out that various types of enterprises are in the stage of differentiation in the investment and construction speed of charging station enterprises. In addition, this study proposes personalized siting solutions for different types of charging piles, and makes referenceable suggestions for the siting of charging stations.
 
 
 atasets MNIST and CIFAR-10. After a few epochs, the model is convergent and reaches high accuracy of 99.34% in MNIST and 92.04% in CIFAR-10. This model outperforms the single CNN and some state-of-the-art models in classifying both datasets, especially in distinguishing similar images like ‘6’ and ‘9’, ‘bird’ and ‘plane’. These results indicate the model's good robustness and generality.