基于聚类算法的新能源汽车电池分配算法与机制研究

Qian Wang, Ma Qiu, Wanzhen Wang
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引用次数: 0

摘要

在新能源汽车的推广过程中,车辆的续航能力是一个不可忽视的重要环节。由于新能源充电站数量有限,中途更换电池将成为一种新的服务模式。根据新能源汽车和电站需求动态变化的特点,建立了新能源汽车电池分配路径问题的动态车辆调度模型。采用自适应准则对遗传算法进行改进,构造了自适应遗传算法。同时,利用遗传算法对充换电站在时间和空间上进行聚类,并将聚类结果应用到路径调整中,使充换电站尽可能多地加入到时间和空间较短的充换电站所在的路径中,可以有效地缩小搜索范围,更快地得到更好的解决方案。通过MATLAB对算法进行了编程,并对配电系统进行了数值仿真,显示了新能源汽车在使用成本方面的优势,验证了模型的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on Algorithm and Mechanism of New Energy Vehicle Battery Distribution Based on Clustering Algorithm
In the promotion process of new energy vehicles, the endurance of vehicles is an important link that can not be ignored. Due to the limited number of new energy charging stations, replacing batteries in transit will become a new service mode. According to the characteristics of dynamic changes in the demand of new energy vehicles and power stations, a dynamic vehicle scheduling model for battery distribution routing problem of new energy vehicles is established. The adaptive genetic algorithm is constructed by improving the genetic algorithm with adaptive criteria. At the same time, the genetic algorithm is used to cluster the charging and replacing stations in time and space, and the clustering results are applied to the path adjustment, so that the charging and replacing stations can be added to the path where the charging and replacing stations with short time and space are located as much as possible, which can effectively reduce the search range and reach a better solution faster. The algorithm is programmed by MATLAB, and the numerical simulation of the distribution system shows the advantages of new energy vehicles in terms of use cost, which verifies the effectiveness of the model.
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