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The accurate modeling of the charging characteristics of electric vehicles (EVs) is the basis for the load forecasting, infrastructure planning, and orderly charging management. While, research based on the measured charging data of EVs is seldom carried out, and the concrete modeling of the correlations of various parameters is a gap in the knowledge. Aiming at this, we carried out an investigation based on operational data, from August 2016 to August 2017, of an EV charging service company in Nanjing, China. The time-energy characteristics of EV charging behavior can be described using the probability distributions and correlations of three charging parameters, i.e., charging start time, charging duration, and charged capacity. In this paper, we fitted the probability densities of these charging parameters using the kernel estimation method and verified the correlations of time parameters of the charging behavior. Multiple copula functions were used to model the correlation between the time and energy parameters of different types of charging behaviors. On this basis, we also carried out stochastic simulation for the load curve of disordered charging and analyzed the potential of the EV charging load participating in the orderly management and its coordination with the output of power generation using renewable energy.

An Analysis of the Charging Characteristics of Electric Vehicles Based on Measured Data and Its Application