A Data-Driven Framework for Optimizing Multi-Period EV Charging Infrastructure Deployment

The rise of electric vehicles represents a transformative shift in the automotive industry, signaling the dawn of a new era of clean, sustainable transportation, but their operation requires a distributed rapid-charging infrastructure. Building such rapid charging networks is currently capital-intensive and therefore, requires careful planning and the development of the charging infrastructure must be maintained. However, infrastructure construction is not a one-off investment but a multi-period plan. A multi-period location and capacity expansion model of the charging stations will be needed. This study proposes a novel data-driven framework for deploying suitable rapid-charging infrastructure for EVs in large urban areas. This study combines an iterative clustering technique with a geographical information system analysis tool to determine the suitable regions for developing an optimized EV charging service. The analysis intends to plan a case study for Gurugram City of India and suggest the locations that should be the potential points for consideration of charging station development.