Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (12): 285-293.doi: 10.11896/jsjkx.230100099

• Artificial Intelligence • Previous Articles     Next Articles

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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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

CLC Number: 

  • TP393
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