Stochastic Modeling for Studies of Real-World PHEV Usage: Driving Schedule and Daily Temporal Distributions

Abstract

Daily driving missions provide the fundamental information required to predict the impact of the plug-in hybrid electric vehicle (PHEV) on the grid. In this paper, we propose a statistical modeling approach of daily driving mission sets. The approach consists of temporal distribution modeling and the synthesis of individual representative cycles. The proposed temporal distribution model can capture departure and arrival time distributions with a small number of samples by statistically relating the distributions. Then, representative naturalistic cycles are constructed through a stochastic process and a subsequent statistical analysis with respect to driving distance. They are randomly assigned to the temporal distribution model to build up complete daily driving missions. The proposed approach enables the assessment of the impact on the grid of a large-scale deployment of PHEVs using a small number of simulations capturing real-world driving patterns and the temporal distributions of departure and arrival times.