基于空间语义和个体活动的电动汽车充电站选址方法  

Electric vehicle charging station siting method based on spatial semantics and individual activities

作　　者：郭茂祖 张雅喆 赵玲玲[3] GUO Maozu;ZHANG Yazhe;ZHAO Lingling(School of Electrical and Information Engineering,Beijing University of Civil Engineering and Architecture,Beijing 100044,China;Beijing Key Laboratory of Intelligent Processing for Building Big Data(Beijing University of Civil Engineering and Architecture),Beijing 100044,China;Faculty of Computing,Harbin Institute of Technology,Harbin Heilongjiang 150001,China)

机构地区：[1]北京建筑大学电气与信息工程学院,北京100044 [2]建筑大数据智能处理方法研究北京市重点实验室(北京建筑大学),北京100044 [3]哈尔滨工业大学计算学部,哈尔滨150001

出　　处：《计算机应用》2023年第9期2819-2827,共9页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61871020);北京市属高校高水平科研创新团队建设支持计划项目(IDHT20190506)。

摘　　要：针对电动汽车充电站(EVCS)的选址问题,提出一种基于空间语义和个体活动模式的城市充电站选址方法。首先,根据城市规划,采用无监督学习对非服务半径内兴趣点(POI)进行聚类,以确定新建充电站个数;然后,采用受约束的双存档进化算法(CTAEA)求解目标函数,在站间距最大化以及新充电站覆盖POI最多的约束条件下优化电动汽车选址方案。以成都市二环路内出租车的轨迹数据和POI为实验样本,并规划了15个充电站的选址方案。实验结果表明,相较于NSGA2(Non-dominated Sorting Genetic Algorithm 2)和SPEA2(Strength Pareto Evolutionary Algorithm 2),CTAEA的POI覆盖率指标提高了22.9和20.6个百分点,司机平均选择距离缩短了18.9%和25.5%,验证了所提方法在电动汽车选址方面的便利性与合理性。To address the issue of siting for Electric EVCS(Vehicle Charging Station),an urban charging station siting method based on spatial semantics and individual activities was proposed.First,according to the urban planning,unsupervised learning was used to cluster the Point Of Interests(POIs)out of the service radius to determine the number of new charging stations.Then,Constrained Two-Archive Evolutionary Algorithm(CTAEA)was used to solve the objective function to optimize the electric vehicle siting scheme under the constraints of maximizing the distance between stations and covering the most POIs with new charging stations.The trajectory data and POIs of taxis in the second-ring road of Chengdu were used as the experimental samples,and siting scheme with 15 charging stations was planned.Experimental results show that compared with NSGA2(Non-dominated Sorting Genetic Algorithm 2)and SPEA2(Strength Pareto Evolutionary Algorithm 2),CTAEA improves 22.9 and 20.6 percentage points on POI coverage,and reduces 18.9%and 25.5%on driver’s average selected distance,which illustrates the convenience and rationality of the method in electric vehicle charging station siting.

关 键 词：充电站选址 电动汽车 需求预测模型 聚类分析 双存档进化算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]