考虑电池损耗的电动物流汽车充电设施选址与充电策略协同优化研究
Collaborative Optimization of Charging Network and Charging Strategy with Practical Battery Wear Model
黄志红 1黄卫来 2郭放1
作者信息
- 1. 郑州大学管理学院,河南 郑州 450001
- 2. 华中科技大学管理学院,湖北 武汉 430074
- 折叠
摘要
由于政府的大力支持和社会公众环保意识的增强,电动汽车在物流领域的应用得到了广泛的关注.不同于传统物流车辆,电动汽车运营成本受到电池损耗等因素的影响.本文以电池损耗模型为基础,建立了电动汽车充电设施选址与充电策略协同优化问题的整数规划模型.针对上述问题,本文提出了基于自适应大邻域搜索的混合启发式算法(SIGALNS),改进算子提升算法求解效率.随后,通过算例分析验证了模型的准确性和算法的有效性.研究结果表明,考虑电池损耗成本的电动汽车网络优化策略有利于降低物流企业运营成本,与其实际需要更为契合.
Abstract
Electric vehicles(EVs)have attracted increasing attention in the field of logistics owing to the strong support received from the government and the continuous increase in social environmental awareness.Compared to traditional logistics vehicles,EVs have additional charging costs such as charging time cost and battery wear cost.In this study,the routing problem of EVs is formulated as an integer programming model based on a nonlinear charging model and a practical battery wear model.Subsequently,a three-phase algorithm called SIGALNS is proposed for solving it.Based on the proposed model,a series of instances are generated showing the benefits of combining charging time,battery wear and distribution.Finally,sensitivity analyses are systematically conducted on wearing cost and charging time under a realistic background.The results show that the optimal planning of an EV network considering time and wear costs is in line with the practical needs of EV logistics enterprises;this can help reduce operating costs.The results of this paper demonstrate the impact of charging strategy on the cost of electric logistics vehicle logistics service network from a practical perspective,and propose a collaborative optimization scheme of distribution scheduling and charging plan to reduce the comprehensive operating cost of enterprises.The models and algorithms proposed in this study can provide a decision-making basis for logistics enterprises that use electric logistics vehicles as delivery vehicles to make operational decisions.
关键词
设施选址/电动物流汽车/充电策略/电池损耗/混合启发式算法Key words
facility location/electric freight vehicle/charging strategy/battery wear/hybrid heuristic algorithm引用本文复制引用
基金项目
国家自然科学基金青年项目(72201251)
国家自然科学基金青年项目(72301252)
中国博士后科学基金面上项目(2023M733207)
河南省重点研发与推广专项(科技攻关)项目(222102210109)
出版年
2024
NETL
NSTL
万方数据