A Comprehensive Data Analysis of Electric Vehicle User Behaviors Toward Unlocking Vehicle-to-Grid Potential

Abstract

Electric vehicles (EVs) improve the power grid by increasing intermittent renewable energy consumption and providing financial support to EV users via vehicle-to-grid (V2G) integration. While estimating these advantages, a number of studies have neglected to consider the effect of driving and charging behavior patterns on their results. This article provides a framework that systematically evaluates EV driving and charging behaviors to improve charge management in the light of recent standards and advancements. In addition, the collected data on driving habits are analyzed in order to provide a consistent and usable dataset. By evaluating the individual and simultaneous charging demand characteristics, the V2G potential is further explored. Moreover, managerial recommendations for EV charging management are offered by improving the time step using the Bootstrap approach for more precise results than lower resolution. It is also addressed that the simultaneous use of a limited number of EVs required minimum time. According to the findings of this study, daily travel habits have a crucial influence in defining seasonal and individual charging demands. In order to continue with EV charging-related assessments with a confidence interval of more than 95%, the findings suggest that time steps of lower than ten minutes must be used. In addition, the purpose of this study is to assist researchers from academia and business with further information as they build initiatives linked to EV charging infrastructure and real-time charging management standards that account environmental aspects.

Keywords

• Bootstrap
• charging behavior
• distributed network
• driving data
• electric vehicle