利用实时交通信息感知的电动汽车路径选择和充电导航策略

发布时间：2018-07-02 07:11

  本文选题：电动汽车 + 路径选择　； 参考：《电力系统自动化》2017年11期

【摘要】：适当选择电动汽车(EV)路径并优化充电导航,有助于提高用户出行效率及缓解大量EV同时充电对配电系统安全运行的影响。在此背景下,提出借助群智感知技术来获得实时交通路况和充电站服务信息,并在此基础上利用矩阵分解得到交通信息矩阵。之后,在考虑路径选择、到达时间、电池容量及充放电状态互斥约束的前提下,构建了分时电价机制下以用户出行总成本最小为目标的EV路径选择和充电导航优化模型。以某市中心25km×25km区域内所包含的4座快速充电站连接到IEEE 33节点配电系统为例来说明所述方法的基本特征,并针对考虑和不考虑实时交通信息情形,分析了参与群智感知的EV数量对用户出行路径及EV充放电对配电系统的影响。
[Abstract]:The proper selection of electric vehicle (EV) path and the optimization of charging navigation will help to improve the travel efficiency of users and alleviate the impact of a large number of EV charging simultaneously on the safe operation of distribution system. Under this background, the real-time traffic information and charging station service information are obtained by using group intelligence perception technology, and then the traffic information matrix is obtained by matrix decomposition. After that, considering the constraints of path selection, arrival time, battery capacity and charge / discharge states, an EV path selection and charging navigation optimization model with the goal of minimizing the total travel cost of the user under the time-sharing pricing mechanism is constructed. Taking the four fast charging stations in a downtown 25km 脳 25km area as an example to illustrate the basic characteristics of the proposed method, considering and disregarding the real-time traffic information, an example is given to illustrate the basic characteristics of the proposed method, which is connected to the IEEE33-bus distribution system. The effects of the number of EV participating in group intelligence perception on the travel path of users and the charging and discharging of EV on the distribution system are analyzed.
【作者单位】： 湖南省电动交通与智能配网工程技术研究中心长沙理工大学电气与信息工程学院;国网江苏省电力公司兴化市供电公司;浙江大学电气工程学院;文莱科技大学电机与电子工程系;澳大利亚联邦科学与工业研究组织;香港中文大学(深圳)科学与工程学院;
【基金】：国家自然科学基金资助项目(71331001) 中国南方电网有限责任公司科技项目(WYKJ00000027) 湖南省重点研发科技项目(2016WK2015)~~
【分类号】：TM73
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本文编号：2089331