Deviations in Markov chain modeled electric vehicle charging patterns from real world data

Within this paper a Markov chain is used to simulate the charging and driving behavior of a cohort of electric vehicles. The probability transition matrix is constructed by analyzing the real world data from an ongoing Electric Vehicle (EV) trial, Switch EV. The vehicles are split into a combination of drive, park and charge states as well as low, mid and high state of charge. The Markov chain is formed from the probability of transitioning between each state for a specified time period. The Markov chain produced results from both work and home based charging regimes that correlate well with the real world results with modeled results showing the features present in real world data. From comparisons of fresh sample runs to the ideal modeled charging distribution it can be seen that the Markov chain has little systematic difference between the ideal distribution and the newly generated distribution within a short period of time. The Markov chain was used to investigate the effect on the charging pattern from a large perturbation of the vehicle state population. It is found that despite initial fluctuations to the charging pattern, which qualitatively correspond with expectations, the population distribution settles down into a typical pattern within 1-2 days.