Mobility Analysis of Plug-in Electric Vehicles in San Francisco Applying Monte Carlo Markov Chain

Abstract

The paper realistically studies the uncertainty of state of charge (SOC) of plug-in electric vehicles (PEV) by investigation of real driving routes of 100 vehicles in San Francisco, CA, US. In this study, Monte Carlo Markov Chain (MCMC) is applied to determine the hourly probability distribution function of SOC of PEVs in the typical day. The primary dataset used in this study includes the real longitude and latitude of driving routes of 100 vehicles in San Francisco, recorded in every four-minute period of the day. The four-minute position dataset is converted to the four-minute distance travelled by each PEV, and then the four-minute SOC of PEVs is determined considering the technical specifications of each PEV. Next, the hourly SOC of PEVs are calculated and entered to MCMC to determine the hourly probability distribution function of SOC of PEVs. In this study, the effects of MCMC parameters on its outputs are also investigated and analyzed.