计算机集成制造系统 ›› 2023, Vol. 29 ›› Issue (11): 3909-3921.DOI: 10.13196/j.cims.2021.0412

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动态负载下电动汽车充电策略及路径优化问题

黄建华,刘方翔   

  1. 福州大学经济与管理学院
  • 出版日期:2023-11-30 发布日期:2023-12-04
  • 基金资助:
    国家社科基金一般资助项目(20BGL003)。

Charging strategy and routing optimization of electric vehicles under dynamic load

HUANG Jianhua,LIU Fangxiang   

  1. School of Economics and Management,Fuzhou University
  • Online:2023-11-30 Published:2023-12-04
  • Supported by:
    Porject supported by the National Social Science Foundation,China(No.20BGL003)。

摘要: 针对电动汽车配送过程中耗电速率受荷载大小影响的特点,探讨了动态负载下电动汽车耗电速率和不完全充电策略问题,并以电动车固定费用、行驶费用、电量补充费用和时间窗惩罚费用等综合成本最优为目标,构建了带软时间窗的车辆路径优化模型,设计了改进的混合遗传退火求解算法。最后,以A生鲜企业电动汽车配送业务为例,对模型及算法的有效性进行了验证。结果表明:车辆动态负载情形下,采用不完全充电策略比完全充电策略在充电时间、行驶距离、配送费用等方面具有显著优势;与经典遗传算法相比,所提出的改进混合遗传退火算法能够显著提高收敛速度。

关键词: 电动汽车, 动态负载, 充电策略, 车辆路径优化, 混合遗传退火算法

Abstract: In view of affection of load on power consumption rate in electric vehicle distribution process,a vehicle routing optimization model with soft time window was formulated to minimize the comprehensive cost,such as fixed cost,driving cost,power charging cost and time window penalty cost.An improved hybrid genetic annealing algorithm was designed to solve the problem,and then the power consumption rate and incomplete charging strategy of electric vehicles under dynamic load were discussed.The validity of the model and algorithm was verified by taking electric vehicle distribution service of fresh enterprise as an example.The results showed that an incomplete charging strategy had significant advantages over the complete charging strategy in case of charging time,driving distance and distribution cost under dynamic load of vehicles.Compared with the classical genetic algorithm,the improved hybrid genetic annealing algorithm could converge to optimal solutions quickly and effectively.

Key words: electric vehicles, dynamic load, charging strategy, vehicle routing optimization, hybrid genetic annealing algorithm

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