Stochastic Modeling of Battery Electric Vehicle Driver Behavior

Abstract

A stochastic modeling approach is proposed to characterize battery electric vehicle (BEV) drivers’ behavior. The approach uses longitudinal travel data and thus allows more realistic analysis of the impact of the charging infrastructure on BEV feasibility. BEV feasibility is defined as the probability that the ratio of the distance traveled between charges to the BEV range is kept within a comfort level (i.e., drivers are comfortable with driving the BEV when the battery's state of charge is above a certain level). When the ratio exceeds the comfort level, travel adaptation is needed–-use of a substitute vehicle, choice of an alternative transportation mode, or cancellation of a trip. The proposed stochastic models are applied to quantify BEV feasibility at different charging infrastructure deployment levels with the use of GPS-based longitudinal travel data collected in the Seattle, Washington, metropolitan area. In the Seattle case study, the range of comfort level was found to be critical. If BEV drivers were comfortable with using all the nominal range, about 10% of the drivers needed no or little travel adaptation (i.e., they made changes on less than 0.5% of travel days), and almost 50% of the drivers needed travel adaptation on up to 5% of the sampled days. These percentages dropped by half when the drivers were only comfortable with using up to 80% of the range. In addition, offering opportunities for one within-day recharge can significantly increase BEV feasibility, provided that the drivers were willing to make some travel adaptation (e.g., up to 5% of drivers in the analysis).