On the Evaluation of Plug-in Electric Vehicle Data of a Campus Charging Network

Abstract

The mass adoption of plug-in electric vehicles (PEVs) requires the deployment of public charging stations. Such facilities are expected to employ distributed generation and storage units to reduce the stress on the grid and boost sustainable transportation. While prior work has made considerable progress in deriving insights for understanding the adverse impacts of PEV chargings and how to alleviate them, a critical issue that affects the accuracy is the lack of real world PEV data. As the dynamics and pertinent design of such charging stations heavily depend on actual customer demand profile, in this paper we present and evaluate the data obtained from a $17$ node charging network equipped with Level $2$ chargers at a major North American University campus. The data is recorded for $166$ weeks starting from late $2011$. The result indicates that the majority of the customers use charging lots to extend their driving ranges. Also, the demand profile shows that there is a tremendous opportunity to employ solar generation to fuel the vehicles as there is a correlation between the peak customer demand and solar irradiation. Also, we provided a more detailed data analysis and show how to use this information in designing future sustainable charging facilities.

Comment: Accepted by IEEE Energycon 2016

Original document

The different versions of the original document can be found in:

• http://arxiv.org/abs/1602.02943

• http://arxiv.org/pdf/1602.02943

• https://strathprints.strath.ac.uk/70709/1/Bayram_etal_IEEE2016_On_evaluation_plug_in_electric_vehicle_data_campus_charging_network.pdf

• http://xplorestaging.ieee.org/ielx7/7507294/7513868/07514026.pdf?arnumber=7514026,

http://dx.doi.org/10.1109/energycon.2016.7514026

• https://dblp.uni-trier.de/db/journals/corr/corr1602.html#BayramZHK16,

https://arxiv.org/pdf/1602.02943.pdf,

https://escholarship.org/uc/item/0hc2604z,

http://ui.adsabs.harvard.edu/abs/2016arXiv160202943S/abstract,

https://www.scipedia.com/public/Bayram_et_al_2016a,

https://strathprints.strath.ac.uk/70709,

https://arxiv.org/abs/1602.02943,

http://export.arxiv.org/pdf/1602.02943,

https://pureportal.strath.ac.uk/en/publications/on-the-evaluation-of-plug-in-electric-vehicle-data-of-a-campus-ch,

https://export.arxiv.org/abs/1602.02943,

https://academic.microsoft.com/#/detail/2255008403