Data-Model Hybrid-Driven Simulation Method for Electric Vehicle Fleet Behavior

To address the issues of lack of actual electric vehicle (EV) charging load data, significant regional differences, and complex simulation methods, a behavior simulation method for EVs is proposed, which consists of a trip chain, an energy consumption chain, and a charging chain. First, to tackle the scarcity of charging data, a data-driven approach is adopted to derive the construction method and process of vehicle trip chain based on Gaussian mixture model (GMM). The travel patterns of EVs are obtained based on the similarities between EV and conventional vehicle trips. Then, energy consumption models are summarized, creating energy consumption chains based on trip chains. Finally, considering factors like charging anxiety, queueing, and charging time, the EV charging chain is developed. Simulations are conducted on common charging strategies to verify the impacts on user charging costs and the power grid.