论文标题
研究基于平均移位集群的车辆驾驶周期的施工方法
Research on the construction method of vehicle driving cycle based on Mean Shift clustering
论文作者
论文摘要
在这项研究中，提出了一种基于平均偏移聚类的驾驶周期的新方法，以解决传统的微旅行方法中存在的问题。首先，通过在实际道路条件下处理和划分驾驶数据来获得1701个运动段。其次，计算每个段的12个运动参数，并通过主成分分析（PCA）降低参数的维度。选择三个主成分将所有周期分类为三种类型的均值算法。最后，根据最小偏差的原理，从每种类型的循环中选择代表性的微型旅行来完成最终驾驶周期的构建。此外，本文中的构造方法与K均值聚类的微旅行构建方法进行了比较。结果表明，通过平均移位聚类的构建方法可以更有效地反映实际驾驶数据。这项研究实现了微旅行的构建方法的创新，并为制定汽车工作状况标准的制定提供了初步的理论基础，新能量车辆的能源管理以及对无人驾驶汽车中车辆动态的最佳控制。
In this study, a novel method for the construction of a driving cycle based on Mean Shift clustering is proposed to solve the problems existing in the traditional micro-trips method. Firstly, 1701 kinematic segments are obtained by processing and dividing the driving data in real road conditions. Secondly, 12 kinematic parameters are calculated for each segment, and the dimensionality of parameters is reduced through principal component analysis (PCA). Three principal components are chosen to classify all cycles into three types by the Mean Shift algorithm. Finally, according to the principle of minimum deviation, representative micro-trips are selected from each type of cycle to complete the construction of the final driving cycle. Further, the construction method in this paper is compared with the micro-trips construction method by the K-Means clustering. The results show that the construction method by Mean Shift clustering can more effectively reflect the real driving data. This study realizes the innovation in the construction method of micro-trips and provides a preliminary theoretical basis for the formulation of automobile working condition standards, energy management of new-energy vehicles, and optimal control of vehicle dynamics in driverless vehicles.
