Hogeschool van Amsterdam

Intelligent Data-driven Optimisation of Charging Infrastructure

Performance of EV Charging Infrastructure

a decision support tool based on charging data

Paper

Developers of charging infrastructure, be it public or private parties, are highly dependent on accurate utilization data in order to make informed decisions where and when to expand charging points. The Amsterdam The Amsterdam University of Applied Sciences in close cooperation with the municipalities of Amsterdam, Rotterdam, The Hague, Utrecht and the metropolitan region of Amsterdam developed both the back- and front-end of a decision support tool. This paper describes the design of the decision support tool and its DataWareHouse architecture. The back-end is based on a monthly update of charging data with Charge point Detail Records and Meter Values enriched with location specific data. The design of the front-end is based on Key Performance Indicators used in the decision process for charging infrastructure roll-out. Implementing this design and DataWareHouse architecture allows all kinds of EV related companies and cities to start monitoring their charging infrastructure. It provides an overview of how the most important KPIs are being monitored and represented in the decision support tool based on regular interviews and decision processes followed by four major cities and a metropolitan region in the Netherlands.

Reference Maase, S. J. F. M., Dilrosun, X. F., Kooi, M. J. W., & van den Hoed, R. (2017). Performance of EV Charging Infrastructure: a decision support tool based on charging data. Paper presented at 30th International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium, Stuttgart, Germany.
Published by  IDO-Laad 1 January 2017