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Clustering of Usage Profiles for Electric Vehicle Behaviour Analysis

Crozier, C., Apostolopoulou, D. ORCID: 0000-0002-9012-9910 and McCulloch, M. (2018). Clustering of Usage Profiles for Electric Vehicle Behaviour Analysis. Paper presented at the 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), 21-25 Oct 2018, Sarajevo, Bosnia and Herzegovina.

Abstract

Accurately predicting the behaviour of electric vehicles is going to be imperative for network operators. In order for vehicles to participate in either smart charging schemes or providing grid services, their availability and charge requirements must be forecasted. Their relative novelty means that data concerning electric vehicles is scarce and biased, however we have been collecting data on conventional vehicles for many years. This paper uses cluster analysis of travel survey data from the UK to identify typical conventional vehicle usage profiles. To this end, we determine the feature vector, introduce an appropriate distance metric, and choose a number of clusters. Five clusters are identified, and their suitability for electrification is discussed. A smaller data set of electric vehicles is then used to compare the current electric fleet behaviour with the conventional one.

Publication Type: Conference or Workshop Item (Paper)
Additional Information: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher Keywords: Clustering algorithms; Demand forecasting; Electric vehicles; Pattern analysis
Subjects: H Social Sciences > HE Transportation and Communications
T Technology > TK Electrical engineering. Electronics Nuclear engineering
T Technology > TL Motor vehicles. Aeronautics. Astronautics
Departments: School of Mathematics, Computer Science & Engineering > Engineering > Electrical & Electronic Engineering
URI: http://openaccess.city.ac.uk/id/eprint/22149
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