一种基于SOC与位置双预测的电动汽车时空调度方法
By adopting a time-based scheduling method for electric vehicles based on dual prediction of SOC and location, and combining user preferences and regional collaborative decision-making, the problems of foresight and personalization in electric vehicle charging scheduling are solved, thereby achieving high efficiency, stability and improved user satisfaction in the charging network.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- HEFEI UNIV OF TECH
- Filing Date
- 2026-04-30
- Publication Date
- 2026-07-17
AI Technical Summary
Current electric vehicle charging scheduling lacks foresight, failing to predict future vehicle movement trends and power consumption, potentially leading to charging station saturation. Furthermore, the lack of personalized recommendation strategies results in low charging network operating efficiency and grid impact risks.
By employing a dual prediction model of SOC and location, combined with user preference mining and regional collaborative decision-making, and through scene identification, dual prediction, clustering and genetic algorithms, personalized charging scheduling for electric vehicles is achieved. A two-layer decision-making framework is constructed and updated in real time to optimize the charging solution.
It enhances the foresight and personalization of charging scheduling, avoids charging congestion, balances the utilization of charging resources, and ensures grid stability and user experience.
Smart Images
Figure CN122134059B_ABST