Multivariate copula procedure for electric vehicle charging event simulation

Johannes Einolander*, Risto Lahdelma

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

13 Citations (Scopus)
133 Downloads (Pure)


This paper introduces a novel application area for multivariate copulas in electric vehicle charging event simulation and dependency analysis. We propose a multivariate copula procedure that can be used to generate new synthetic charging events, which retain the complex dependency and correlation structures present in real-world charging events. The paper compares the most popular multivariate copula functions to discover the most reliable one to be used with electric vehicle charging event data. Accurate EV charging event simulation and analysis is crucial in multiple theoretical and practical applications such as charging load and demand response aggregation modelling. Based on multiple goodness-of-fit tests and charging load profiles of simulated charging events, the Student-t copula was found to be the most reliable multivariate copula to be used with EV charging data. Overall, the multivariate copula procedure is effective in analysis and simulation of EV charging events as it retains the inherent variability and complex dependencies of real charging events.

Original languageEnglish
Article number121718
Number of pages11
Early online date11 Aug 2021
Publication statusPublished - 1 Jan 2022
MoE publication typeA1 Journal article-refereed


  • Charging event
  • Electric vehicle
  • Load profile
  • Multivariate copula
  • Simulation


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