Stochastic modeling of load demand of plug-in hybrid electric vehicles using fuzzy logic

This paper presents a methodology for modeling the load demand of Plug-in hybrid electric vehicles (PHEVs). The accurate prediction of PHEVs-induced loads needs a comprehensive study of PHEV characteristics. The authors divide the PHEV characteristics into two categories: driving pattern and vehicle parameters. Due to the stochastic nature of vehicle arrival time, departure time and daily mileage, probabilistic methods are used to model the driving pattern by many researchers. But the three elements of driving pattern are correlated which each other, making the probability density functions (PDFs) based probabilistic methods inaccurate. Based on the National Household Travel Survey (NHTS) database, the authors proposed a fuzzy logic based stochastic model to study the relationship between the three elements of driving pattern. Moreover, the authors proposed a load profile modeling framework (LPMF) for PHEVs to synthesize both the characteristics of driving pattern and vehicle parameters into a load profile prediction system. Finally, the proposed LPMF of PHEVs is tested in a residential distribution grid, and the results are compared with deterministic and probabilistic models of PHEVs.