Electric Vehicle Charging Guidance Strategy Considering User Decision Preferences

ObjectivesDuring the electric vehicle (EV) charging guidance process, users have different preferences in selecting charging stations, resulting in different selection criteria, which affects their participation in optimal scheduling. Therefore, considering user habits in selecting charging stations and the conflicts of interest between users and charging aggregators, an EV charging guidance strategy that incorporates target charging station selection habits is proposed.MethodsSelf-organizing feature mapping (SOFM) is used for user profiling, and Shapley additive explanations (SHAP) are utilized to determine the optimal profiling results. Secondly, the entropy weight method is applied to determine the weights of time cost and economic cost for each type of user during the charging station selection process. Based on the weight results, user economic cost and time cost are normalized into user satisfaction. Then, users are guided to charge based on their user satisfaction.ResultsThe example shows that compared with the shortest path The numerical example shows that compared with the shortest path algorithm, this strategy significantly improves user satisfaction in selecting charging stations and the profit per unit time of charging piles. Additionally, this strategy is less affected under different traffic conditions.ConclusionsThis strategy effectively addresses the impact of user charging decision preferences on charging guidance, which is of great significance for improving user charging benefits and reducing charging congestion.