Charging demand analysis framework for electric vehicles considering the bounded rationality behavior of users

A novel analytic framework is proposed for the charging demand of electric vehicles (EVs), which considers charging demand is primarily determined by the travel behavior. And the bounded rationality of the EV users in travel choices is focused in this paper. The activity-based analysis is expanded to divide the travel behavior of users into the transfer relationship between activity chains and the time-space transfer rule for each activity chain. The transfer relationship between different activity chains is established by the Bayesian method. Based on the cumulative prospect theory, we prioritize the bounded rationality of users in the selection of the travel mode, the departure time and the travel path. And the time-space transfer rule and the charging demand of EVs on each activity chain are described by combining the dynamic traffic assignment model. On this basis, the daily charging demand rule for EVs is revealed. Finally, a test traffic network and a real urban traffic network are used to study the travel behavior and daily charging demand of EVs. The simulation results show that the proposed method can effectively describe dynamic changes in the charging demands of EVs. In addition, the charging demand of EVs will be affected by the ownership rate of EVs, the service capacity of charging stations and the degree of the bounded rationality of users.