Abstract
Electric Vehicles (EVs) are touted as the sustainable alternative to reduce our over-reliance on fossil fuels and stem our excessive carbon emissions. As the use of EVs becomes more widespread, planners in large metropolitan areas have begun thinking about the design and installation of charging stations city-wide. Unlike gas-based vehicles, EV charging requires a significant amount of time and must be done more periodically, after relatively shorter distances. We describe a KDD framework to plan the design and deployment of EV charging stations over a city. In particular, we study this problem from the economic viewpoint of the EV charging station owners. Our framework integrates user route trajectories, owner characteristics, electricity load patterns, and economic imperatives in a coordinated clustering framework to optimize the locations of stations and assignment of user trajectories to (nearby) stations. Using a dataset involving over a million individual movement patterns, we illustrate how our framework can answer many important questions about EV charging station deployment and profitability.
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Momtazpour, M., Bozchalui, M.C., Ramakrishnan, N., Sharma, R. (2016). Installing Electric Vehicle Charging Stations City-Scale: How Many and Where?. In: Lässig, J., Kersting, K., Morik, K. (eds) Computational Sustainability. Studies in Computational Intelligence, vol 645. Springer, Cham. https://doi.org/10.1007/978-3-319-31858-5_8
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DOI: https://doi.org/10.1007/978-3-319-31858-5_8
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