计算机科学 ›› 2020, Vol. 47 ›› Issue (11): 280-285.doi: 10.11896/jsjkx.190700129

• 人工智能 • 上一篇    下一篇

一种基于蚁群的电动汽车充电调度优化方法

周欣悦, 钱丽萍, 黄玉蘋, 吴远   

  1. 浙江工业大学信息工程学院 杭州 310023
  • 收稿日期:2019-07-18 修回日期:2019-12-11 出版日期:2020-11-15 发布日期:2020-11-05
  • 通讯作者: 钱丽萍(lpqian@zjut.edu.cn)
  • 作者简介:xyzhou_zjut@163.com

Optimization Method of Electric Vehicles Charging Scheduling Based on Ant Colony

ZHOU Xin-yue, QIAN Li-ping, HUANG Yu-pin, WU Yuan   

  1. College of Information Engineering,Zhejiang University of Technology,Hangzhou 310023,China
  • Received:2019-07-18 Revised:2019-12-11 Online:2020-11-15 Published:2020-11-05
  • About author:ZHOU Xin-yue,born in 1995,postgra-duate.Her main research interests include network and intelligent system.
    QIAN Li-ping,born in 1981,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.Her main research interests include wireless communication and networking,IoT and vehicle network.

摘要: 电动汽车的快速发展为人们的生活出行及物流运输带来了诸多便利,但是其存在因为电量不足而导致续驶里程短的问题。文中提出了一种基于蚁群的电动汽车充电调度优化方法,来增加电动汽车的续航里程。首先,运用库仑计数法计算电动汽车的电池余量,同时根据道路交通状况计算电动汽车的行驶能耗。其次,建立相应的0-1整数规划模型,运用基于蚁群的路径规划算法来实现车辆调度并优化电动汽车充电路径。根据具体的选择策略规划电动车的行驶路径,更新路径上的信息素,通过不断迭代获得全局最优解和最优路径。仿真结果表明,与其他优化算法相比,所提优化方法能够有效降低行车过程中电量耗尽的概率,为电动汽车提供准确的行驶路径,可有效增加电动汽车的续驶里程。

关键词: 电动汽车, 调度优化, 路径规划, 蚁群, 整数规划

Abstract: The rapid development of electric vehicles has brought many convenience to people's living travel and logistics transportation,but electric vehicles have the problem of short driving range due to insufficient power.This paper proposes a charging path scheduling optimization algorithm based on the ant colony for electric vehicles to increase the driving range of electric vehicles.In particular,we first adopt the coulomb counting method to calculate the battery remaining amount of the electric vehicle,and calculate the driving energy consumption of the electric vehicle according to the road traffic condition.Then,we establish the corresponding 0-1 integer programming model,and use the path planning algorithm based on ant colony to obtain the optimal charging path for electric vehicles.After the driving path of the electric vehicle is planned,the pheromone on the path is updated,and the optimal solution and the optimal path are obtained through continuous iteration.The simulation results show that compared with other optimization algorithms,the proposed optimization method can effectively reduce the probability of energy consumption in the process of driving,provide an accurate driving path for electric vehicles,and effectively increase the driving range of electric vehicles.

Key words: Ant colony, Electric vehicle, Integer programming, Path planning, Scheduling optimization

中图分类号: 

  • TP391
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