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A data-driven framework for optimizing multi-period ev charging infrastructure deployment

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dc.contributor.author Digalwar, Abhijeet Kumar
dc.contributor.author Routroy, Srikanta
dc.date.accessioned 2025-02-25T07:06:57Z
dc.date.available 2025-02-25T07:06:57Z
dc.date.issued 2024-12
dc.identifier.uri https://ieeexplore.ieee.org/abstract/document/10795643
dc.identifier.uri http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/18001
dc.description.abstract 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. en_US
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.subject Mechanical Engineering en_US
dc.subject Electric vehicles (EVs) en_US
dc.subject Charging infrastructure en_US
dc.subject Charger allocation en_US
dc.subject e-mobility en_US
dc.subject Site selection en_US
dc.title A data-driven framework for optimizing multi-period ev charging infrastructure deployment en_US
dc.type Article en_US


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