Open Access

ARTICLE

Optimization of Electric Vehicle Charging Station Layout Based on Point of Interest Data and Location Entropy Evaluation

by Annan Yang¹, Jiawei Zhang^2,*, Haojie Yang^3,*, Keyi Tao¹, Mengna Xu¹, Yuyu Zhao¹

1 School of Communication Engineering, Tongda College of Nanjing University of Posts and Telecommunications, Yangzhou, 225127, China
2 School of Electrical and Electronic Engineering, Northumbria University, Newcastle, NE1 8ST, UK
3 School of Information and Communication Engineering, Nanjing Institute of Technology, Nanjing, 211167, China

* Corresponding Authors: Jiawei Zhang. Email: ; Haojie Yang. Email:

Journal on Big Data 2024, 6, 21-41. https://doi.org/10.32604/jbd.2024.057612

Received 22 August 2024; Accepted 27 November 2024; Issue published 31 December 2024

Abstract

This study introduces an electric vehicle charging station layout optimization method utilizing Point of Interest (POI) data, addressing traditional design limitations. It details the acquisition and visualization of POI data for Yancheng’s key locations and charging stations. Employing a hybrid K-Means and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm, the study determines areas requiring optimization through location entropy and overlap analysis. The research shows that the integrated clustering approach can efficiently guide the fair distribution of charging stations, enhancing service quality and supporting the sustainable growth of the electric vehicle sector.

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Keywords

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