A Framework for Analysis and Expansion of Public Charging Infrastructure under Fast Penetration of Electric Vehicles

Abstract

The improvement commercial competitiveness of private electric vehicles supported by the European policy for the decarbonisation of transport and with the consumers awareness-raising about CO2 emissions and climate change, are driving the increase of electric vehicles on the roads. Therefore, public charging networks are facing the challenge of supply electricity to a fast increasing number of electric cars. The objective of this paper is to establish an assessment framework for analysis and monitor of existing charging networks. The developed methodology comprises modelling the charging infrastructure electricity profile, analysing the data by using machine learning models such as functional k-means clustering and defining a novel congestion metric. The described framework has been tested against Irish public charging network historical datasets. The analyses reveal a lack of reliability of the communication network infrastructure, frequent congestion events for commercial and shopping areas in specific clusters of charge points and the presence of power peaks caused by the high number of simultaneous charging events. Several recommendations for future network expansion have been highlighted.

Keywords

• electric vehicles (ev)
• charging point
• public charging network
• congestion
• machine learning
• plug-in hybrids (phev)
• functional data analysis (fda)
• functional clustering
• demand side management
• data analytics