Abstract
In this paper, a probabilistic charging demand profile of plug-in battery electric vehicles charging from a fast charging station is presented. Different vehicles' models are utilized, such as the Chevrolet Bolt, the Tesla Model S, and the Nissan Leaf. Two different parameters are considered, the daily distance travelled, and the battery state-of-charge. The Markov Chain Monte Carlo, relying on Metropolis-Hastings sampler, is utilized to estimate the aforementioned parameters based on exponential and Weibull distributions. The convergence of the algorithm is assessed based on the Gelman-Rubin approach. Distributions for the parameter of daily distance travelled were selected by comparing the data of daily mileage driven, corresponding to three types of electric vehicles collected in a mixed and marine climate zone, with theoretical empirical cumulative distributions calculated from seven standard distribution functions. The battery state-of-charge depends on the parameter of daily distance travelled and estimated by taking the difference between the battery total range and the daily distance traveled. The results have shown that the best distribution is exponential for Chevrolet Bolt and Nissan Leaf whereas Weibull represents the best distribution for Tesla Model S. The best distribution has been determined by calculating the Sum of Squares Error.