Modeling and Data Analysis of Electric Vehicle Fleet Charging

Abstract

In the transition to electric fleets around the world, electricity demand from electric vehicle (EV) fleets is expected to become significant in the future. Since fleet cars can display different charging characteristics than individual EVs, analyzing the charging behavior patterns of fleet cars is essential. To do so, this study first examines real EV fleet data from 724 charging events using data analytics methods. Based on this analysis, a charging behavior model is then developed to predict the realistic charging demand of an EV fleet with any number of EVs. In order to overcome the limitations of traditional probability density functions, this study utilizes Gaussian Mixture Models and Kernel distribution in developing charging behaviour models, i.e., charging start and end times, and total charging energy. The models’ behaviours are then compared in terms of goodness-of-fit (GoF) to determine the best match for the original data, in which normalised root mean squared error serving as the fitness criteria.