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Optimal Allocation of Electric Vehicle Charging Station using Weighted Average Method

Publication Type : Conference Paper

Publisher : IEEE

Source : 2025 7th International Conference on Intelligent Sustainable Systems (ICISS)

Url : https://doi.org/10.1109/iciss63372.2025.11076193

Campus : Bengaluru

School : School of Engineering

Department : Electrical and Electronics

Year : 2025

Abstract : In order to acknowledge the problem of global warming, rising pollution, and decreasing natural resources, Electric Vehicles (EVs) are seen as a replacement for the current generation automobile. For wide acceptance of Electrical vehicles on road, the proper charging infrastructure need to be developed. As the electric vehicle has been for a long time, it has drawn attention in the past decade amid rising carbon footprints and environmental effects on fuel vehicles. EVs increase our energy independence and contribute to healthier air and lower carbon emissions. The objective of this work is to identify an optimal location for building a charging station for EVs. A scheme needs to be developed which will identify where the location is suitable for building a charging station or not. As enough data is not available on EV charging stations, real time data of fuel stations is analyzed to develop a model. The major contribution of this work is the development of a scheme to identify optimal locations for electric vehicle (EV) charging stations. The model uses specific parameters such as a total number of consumers visiting the station per day, total fuel consumption per day, average distance to other fuel stations etc. are used to determine suitable locations for a new EV charging station. Essentially, it leverages existing infrastructure data to strategically plan the EV charging infrastructure.

Cite this Research Publication : Maheswara Sai Pavan Kavuru, Manitha P V, Optimal Allocation of Electric Vehicle Charging Station using Weighted Average Method, 2025 7th International Conference on Intelligent Sustainable Systems (ICISS), IEEE, 2025, https://doi.org/10.1109/iciss63372.2025.11076193

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