计算机科学

计算机科学 ›› 2023, Vol. 50 ›› Issue (12): 285-293.doi: 10.11896/jsjkx.230100099

• 人工智能 • 上一篇    下一篇

面向绿色节能的智能网联电动车调度方法

陈瑞1,2, 沈鑫3, 万得胜1,2, 周恩亦1,2   

  1. 1 重庆大学计算机学院 重庆 400044
    2 信息物理社会可信服务计算教育部重点实验室(重庆大学) 重庆 400044
    3 解放军陆军勤务学院勤务指挥系 重庆 401331
  • 收稿日期:2023-01-17 修回日期:2023-04-08 出版日期:2023-12-15 发布日期:2023-12-07
  • 通讯作者: 沈鑫(5912829@qq.com)
  • 作者简介:(chenrui.cqu@foxmail.com)
  • 基金资助:
    国家自然科学基金(62172063)

Intelligent Networked Electric Vehicles Scheduling Method for Green Energy Saving

CHEN Rui1,2, SHEN Xin3, WAN Desheng1,2, ZHOU Enyi1,2   

  1. 1 College of Computer Science,Chongqing University,Chongqing 400044,China
    2 Key Laboratory of Dependable Service Computing in Cyber Physical Society(Chongqing University),Chongqing 400044,China
    3 Army Logistics University,Chongqing 401331,China
  • Received:2023-01-17 Revised:2023-04-08 Online:2023-12-15 Published:2023-12-07
  • About author:CHEN Rui,born in 1998,master.Her main research interests include mobile crowdsensing and urban computing.
    SHEN Xin,born in 1983,Ph.D.His main research interests include big data intelligence,service computing and AI.
  • Supported by:
    National Natural Science Foundation of China(62172063).

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摘要： 随着新能源电动车的飞速发展,以智能化、网联化、节能化为特点的智能网联电动车具备群体智能的优点,适合执行大规模城市任务,被广泛用于智慧城市的社会服务建设中。为此,以智能网联电动车为研究对象,重点研究电动车群体的城市任务调度问题,主要面临以下挑战:由于城市任务的分配策略与车辆个体执行任务的能力密切相关,在面向车辆群体制定派遣策略时,需要综合考虑车辆个体在其行驶轨迹上所产生的区域效益,以保证车辆在有限电量的约束条件下完成任务并顺利返回。因此,车辆群体派遣策略与车辆个体路径规划方案之间相互影响,是一个带权二分图匹配问题和旅行商问题紧耦合的NP-hard问题。为了解决上述挑战,提出了基于最大权值匹配的车辆派遣算法,首先采用贪心策略为单个车辆在子区域内选择任务路段;然后利用车辆行驶轨迹产生的区域效益,制定车辆与子区域的最优派遣策略,从而最大化区域效益总量。最后,基于四川省成都市238辆智能环卫车30天的作业数据集,对所提算法进行评估。实验结果表明,所提算法的城市道路清扫率相比源数据方法、随机算法和不更新地图算法平均提升了11.2%。

关键词: 智能网联电动车, 智慧城市任务, 电池电量, 派遣策略, 路径规划

Abstract: With the rapid development of new energy electric vehicles,intelligent networked electric vehicles featuring intelligence,networking,and energy saving not only have the advantages of group intelligence and are suitable for performing large-scale urban tasks,but also are widely used in the construction of social services in smart cities.For this reason,this paper focuses on the urban task dispatching problem for groups of electric vehicles with intelligent networked electric vehicles as the research object,which mainly faces the following challenges:since the urban task dispatching strategy is closely related to the ability of individual vehicles to perform the task,the regional benefits generated by each vehicle on its driving trajectory needs to be consi-dered when developing a dispatching strategy for a group of vehicles to ensure that the vehicles complete their tasks under the constraint of limited power and return.Therefore,the vehicle group dispatching strategy and the individual vehicle path planning scheme interact as a tightly coupled NP-hard problem with a weighted bipartite graph matching problem and a travel quotient problem.To solve the above challenges,a vehicle dispatching algorithm based on maximum weight matching is proposed,which first selects task sections for individual vehicles within sub-regions by employing a greedy strategy.Then,the optimal dispatching strategy for vehicles and sub-regions is developed using the regional benefits generated by vehicle travel trajectories,to maximize the total regional benefits.Finally,the proposed algorithm is evaluated based on a 30-day operation dataset of 238 intelligent sanitation vehicles in Chengdu,Sichuan province.Experimental results show that the proposed algorithm has an average 11.2% improvement in urban road sweeping rate compared to the source data method,the randomized algorithm and the non-updated map algorithm.

Key words: Intelligent networked electric vehicles, Smart city tasks, Battery power, Dispatch strategy, Path planning

中图分类号: 

  • TP393
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