摘要
针对电动汽车智能选址及定容问题,首先建立充电站建设花费费用、运行维护费用、服务用户充电的费用、排队等候时间成本和用户行驶过程耗电成本最小的多目标多约束的充电站选址及定容模型。然后运用BP(back propagation)神经网络对研究区域进行选址,其次利用蚁群算法对所选地址进行定容,最后使用太原充电站的选址定容算例验证了BP神经网络对研究区域进行选址的有效性和采用蚁群算法对所选地址进行定容的可行性,并对长治市区充电站的选址定容进行了预测。
Aiming at the problem of intelligent location and capacity determination of electric vehicles.First establish a multi-objective and multi-constraint optimal charging station location and sizing model that minimizes the sum of investment cost,operation and maintenance cost,charging cost for service users,power consumption cost for users'driving and waiting time cost.Then using the BP neural network researching regional location.Secondly by using Ant colony algorithm for the selected address constant volume.At last,the example of Taiyuan charging station site selection and constant volume the effectiveness of BP neural network site selection for the study area and the feasibility of sizing the selected address by ant colony algorithm.And The location and capacity of the charging station in Changzhi city are predicted.
作者
孟涛
郭红戈
张春美
MENG Tao;GUO Hong-ge;ZHANG Chun-mei(School of Electronics and Information Engineering,Taiyuan University of Science and Technology,Taiyuan 030024,China)
出处
《太原科技大学学报》
2024年第4期342-347,共6页
Journal of Taiyuan University of Science and Technology
基金
国家自然科学基金(61603266)
山西省自然科学基金(201801D1211128)。
关键词
电动汽车充电站
选址
定容
BP神经网络
蚁群算法
electric vehicle charging station
site selection
constant volume
the BP neural network
ant colony algorithm
