基于动态交通推演的电动汽车充电快速引导策略

doi:10.19595/j.cnki.1000-6753.tces.240722

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摘要

随着电动汽车（EV）数量的急剧增长和渗透率的不断提高,为大规模电动汽车进行充电引导成为缓解局部有限的充电资源与旺盛的充电需求间的矛盾的重要措施,为此本文提出一种基于动态交通推演的电动汽车充电快速引导策略。首先,建立基于时空自监督学习（ST-SSL）的动态交通信息预测模型,分别设计面向交通数据空间异质性与时间异质性的自监督学习（SSL）模块,实现多时段交通流量信息的精准预测。其次,设计城市路网多时刻动态阻抗建模方法,建立兼顾用户多需求场景、多导航目标的充电导航策略,提出基于动态Dijkstra算法的求解方法,并基于全域充电导航结果对城市充电站服务范围进行动态评估,从而实现电动汽车充电快速引导。最后,以洛杉矶某区域实际路网为例,证明了预测模型的准确性与充电引导策略的有效性,能够有效感知动态交通信息快速实现城市充电站服务范围划分与电动汽车充电引导,同时有效降低充电导航计算资源消耗。

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关键词 ：电动汽车, 交通系统, 交通流量预测, 充电导航, 服务范围

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.

Key words： Electric vehicle traffic system traffic flow prediction charging navigation service coverage

收稿日期: 2024-05-07

PACS:

null

基金资助:

国家自然科学基金（52177080）和国家重点研发计划项目（2021YFB1600203）资助

通讯作者: 张宽男,1994年生,讲师,硕士生导师,主要研究方向为新能源电力系统及综合能源微网。E-mail：kuanzhang@ncepu.edu.cn

作者简介: 胡俊杰男,1986年生,教授,博士生导师,主要研究方向为新能源电力系统与微网,电动汽车与电网互动。E-mail：junjiehu@ncepu.edu.cn

引用本文:

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, 0, (): 20240722-20240722. 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, 0, (): 20240722-20240722.

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