A novel clustering based method for charging infrastructure planning in the context of Indian cities

Rapid urbanization and increasing environmental concerns have underscored the need for sustainable transportation solutions, with electric vehicles (EVs) being a promising alternative. However, the widespread adoption of EVs is contingent upon the availability of adequate charging infrastructure. This paper presents a novel clustering-based methodology for planning EV charging infrastructure, using K-means clustering and silhouette analysis to categorize cities based on socio-economic and environmental factors, such as EV adoption rates and existing charging points. Each cluster is analyzed to develop a generalized framework for the planning of EV infrastructure, including policy recommendations and infrastructure development strategies. Additionally, specific E V models are assigned to each cluster to optimize infrastructure distribution and promote the growth of the EV ecosystem. This data-driven approach offers customized strategies to improve EV adoption and charging infrastructure, providing valuable information for policymakers, urban planners, and stakeholders working toward sustainable urban mobility.