Remote Sensing-based optimal electric vehicle charging station identification in Kolkata urban agglomeration

Abstract

People are drawn to electric vehicles (EVs) because they help nations whose economies depend on fossil fuels create more sustainable and liveable metropolitan areas. The number of EVs in the Kolkata Urban Agglomeration (KUA) is rising, there aren’t enough charging stations, and most private vehicles are charged at home. The multi-criteria decision-making (MCDM) methodologies work to determine suitable locations for electric vehicle charging stations (EVCS). Techniques utilized for determining the weights of criteria include the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Analytic Hierarchy Process (AHP), and Fuzzy-AHP (FAHP). The analysis indicates very high 164.42 km², high 320.79 km², moderate 568.85 km², Low 509.92 km², and Least 201.82 km² of suitability level. The findings indicate that the suggested method provides a noteworthy way to choose appropriate sites for charging stations. These insights might also help administrators and planners to evaluate strategy and planning effectively.