Capturing diversity in electric vehicle charging behaviour for network capacity estimation

Constance Crozier, Thomas Morstyn, Malcolm Mcculloch

Research output: Contribution to journalArticlepeer-review


This paper proposes a stochastic data-driven model for uncontrolled charging that accurately captures diversity in individual consumer behaviour. This is important because understanding the diversity between consumers is necessary to accurately estimate the number of electric vehicles’ charging a distribution network could support without reinforcements. The model combines readily available travel survey data with high resolution data from an electric vehicle trial, using clustering and conditional probabilities. We demonstrate through a case study of UK residential charging that existing approaches may overestimate the increase in peak distribution network demand by 50%, which has implications for assessing the cost of network investments required. We also show that the peak charging demand varies regionally from 0.2–1.4 kW per household, demonstrating the importance of using locally representative vehicle usage data.
Original languageEnglish
Pages (from-to)102762
JournalTransportation Research Part D: Transport and Environment
Early online date12 Mar 2021
Publication statusPublished - 1 Apr 2021


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