Catalog Home Page

Sensitivity of electric vehicles demand profile to the batteries departure state-of-charge

Pashajavid, E. and Shahnia, F. (2014) Sensitivity of electric vehicles demand profile to the batteries departure state-of-charge. In: 24th Australasian Universities Power Engineering Conference (AUPEC) 2014, 28 Sept. - 1 Oct. 2014, Perth, W.A

PDF - Authors' Version
Download (1MB) | Preview
Link to Published Version:
*Subscription may be required


This paper focuses on the impacts of considering batteries state-of-charge (SOC) at the departure time on thedemand modeling of plug-in electric vehicles (PEVs). Almost all of the previous researches assumed that PEVs batteries at the departure time are fully charged; however, this assumption is highly questionable because it is probable for a PEV to not be charged every day. The probability density function of a vehicle owners’ willingness to fulfill the daily charging is extracted according to the initial SOC of a PEV and the estimated distance of its next trip. Afterwards, with the aim of considering the uncertainties with the associated random variables as well as properly adjusting vehicles SOC at the departure time, a Monte Carlo based multi loop (MCML) algorithm is developed which is composed of two loops, namely the inner loop and the outer loop. In order to implement the proposed stochastic method, a case study has been conducted employing the gathered datasets related to the ICE vehicles in Tehran. Appropriate Student’s t copula functions have been fitted to the datasets in order to take into account the correlation structure among them as well as to generate the required random samples

Publication Type: Conference Paper
Item Control Page Item Control Page


Downloads per month over past year