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Title	Demand-Based Deployment Of Electric Vehicle Charging Stations Employing Empirical Mobility Dataset
ID_Doc	18247
Authors	Tan C.M.S.; Magsino E.R.
Year	2023
Published	Lecture Notes in Networks and Systems, 475
DOI	http://dx.doi.org/10.1007/978-981-19-2840-6_22
Abstract	In this study, we uniformly partition an empirical vehicular network map and determine the locations where electric vehicle (EV) charging stations will be deployed by implementing the particle swarm optimization (PSO) algorithm. The optimal placements utilize the spatiotemporal taxi movements representing energy demands. From each partition, the taxi GPS coordinates are extracted and used to obtain the global best location of the charging station. Parameters such as the total number of taxis accessible to each deployed EV charging station and the average distance of each taxi from all the charging stations have also been computed to evaluate the deployment method. Results have shown that the total number of taxis within the 1-km radius distance of each EV charging station ranges from 797 to 1218 taxis, while the average distance of each taxi from all of the charging stations ranges from 3.14 to 4.46 km. We also note that there is a small inter-distance separation among all charging stations. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Author Keywords	Electric vehicle charging stations; Global best; Intelligent transportation systems; Optimal deployment; Particle swarm optimization algorithm; Smart city

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