Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (11): 280-285.doi: 10.11896/jsjkx.190700129

• Artificial Intelligence • Previous Articles     Next Articles

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.

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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

CLC Number: 

  • TP391
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