基于动态哈夫模型及双边匹配的电动汽车充电引导策略OA北大核心CSTPCD

Due to the limited number of charging piles in charging stations and the long charging time for electric vehicles, there is competition for charging station resources among various electric vehicle users who are successively generating charging demand. This not only increases the queuing probability of users, reduces the revenue and utilization rate of the charging station, but also makes the users' personalized needs in terms of charging station size, price, evaluation not fully satisfied. For this reason, a guidance strategy for electric vehicle charging is proposed that combines the dynamic Huff model with the bilateral matching method. First, the big data mining is performed on real data sets such as charging station passenger flow, charging order, and charging pile profile to analyze the charging station selection preferences and charging behavior characteristics of public charging station users. Then, based on the dynamic Huff model, the probability of users going to different charging stations in different regions is quantified by combining the users' selection preferences for charging stations, and the charging station recommendation lists are generated. Finally, the prospect theory is combined with the bilateral matching strategy for charging guidance. Case analysis shows that the proposed strategy significantly reduces the queuing probability of users, meeting their personalized charging needs while ensuring the interests of charging stations.