Electric vehicle charging profile prediction for efficient energy management in buildings

Abstract

Predicting the charging profiles of electric vehicles (EVs) connected to a building incorporated with a Building Energy Management System (BEMS) will improve the energy efficiency of the building. The predicted charging profiles along with the forecasted load data can be utilized for calculating vehicle to grid (V2G) capacity and for performing load/source scheduling. In this paper, an Artificial Neural Network (ANN) based model is proposed for predicting the charging profiles of EVs connected to a building. The ANN model considers the previous charging profiles, initial State of Charge (SOC) and final SOC for predicting the charging profile of the EV. A BEMS simulation tool is developed using National Instruments LabVIEW to analyze the functionality of the model. Using the predicted charging profiles and forecasted building load, EV scheduling is demonstrated for a typical day. The V2G capacity available for peak saving is also computed and load/source scheduling is performed accordingly.