Fast Guidance Strategy for Electric Vehicle Charging Based on Dynamic Traffic Inference
Hu Junjie1, Pan Yi1, Xu Chengming1, Zhang Kuan1, Wang Fangyu2
1. State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing 102206 China; 2. State Grid Beijing Electric Power Research Institute Beijing 100075 China
Abstract：Electric vehicles (EV) have the characteristics of both traffic and mobile load, and their charging behavior will have an interactive impact on the power grid. With the rapid increase in the number of electric vehicle and the continuous improvement of their penetration rate, charging guidance for large-scale EVs has become an important measure to alleviate the contradiction between local limited charging resources and strong charging demand. Therefore, considering the influence of future traffic information changes on navigation strategy, this paper proposes a fast guidance strategy for electric vehicle charging based on dynamic traffic inference. First of all, a dynamic traffic information prediction model based on spatio-temporal self-supervised learning (ST-SSL) is established. A self-supervised learning (SSL) module for spatial and temporal heterogeneity of traffic data is designed to achieve accurate prediction of multi-period traffic flow information. Secondly, a multi-time dynamic impedance modeling method for urban road network considering future traffic information changes is designed, a charging navigation strategy considering multi-demand scenarios and multi-navigation objectives of users is established, and a solution method based on dynamic Dijkstra algorithm is proposed to realize the selection of the optimal charging station and the planning of the optimal navigation path. Based on the global charging navigation results, the service range of urban charging stations is dynamically evaluated, to achieve rapid charging guidance for electric vehicles. Finally, taking the actual road network of a certain area in Los Angeles as an example, the accuracy of the prediction model and the effectiveness of the guidance strategy are proved, which can effectively perceive the dynamic traffic information and quickly realize the service range division of urban charging stations and the charging guidance for electric vehicles. In this paper, a fast guidance strategy for electric vehicle charging based on dynamic traffic inference is proposed, based on the case simulation results, the main conclusions can be obtained as follows. (1) The model based on ST-SSL can make full use of the spatial and temporal heterogeneity of traffic data, improve the prediction effect of traffic flow information, and provide an effective data basis for the construction of dynamic traffic impedance. (2) The proposed multi-scenario and multi-objective charging navigation strategy based on dynamic impedance can effectively perceive traffic information and take into account the diversified needs of users, effectively reduce the cost of charging navigation for different users, and reasonably guide the load distribution of electric vehicles. (3) The proposed dynamic Dijkstra algorithm can recommend the optimal path according to the future traffic information, which can be used as a navigation algorithm to plan the driving path, and can also recommend the customized optimal charging station according to the needs of users. (4) The division of charging station service range based on the global charging navigation results can effectively evaluate the service range of charging station, and provide an important reference for the construction planning of charging station. Based on the evaluation results, the charging navigation strategy is quickly assigned to each node, which effectively reduces the computing resource consumption of charging navigation.
胡俊杰, 潘羿, 徐成明, 张宽, 王方雨. 基于动态交通推演的电动汽车充电快速引导策略[J]. 电工技术学报, 2025, 40(9): 2880-2896.
Hu Junjie, Pan Yi, Xu Chengming, Zhang Kuan, Wang Fangyu. Fast Guidance Strategy for Electric Vehicle Charging Based on Dynamic Traffic Inference. Transactions of China Electrotechnical Society, 2025, 40(9): 2880-2896.
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