基于K-均值聚类算法的行驶工况构建方法

Driving cycle construction using K-means clustering method

提出一种基于K-均值聚类算法的城市循环工况构建方法,该方法通过实车采集某城市道路行驶工况的数据,将工况数据预处理后划分为工况块,运用平均速度、行驶距离和巡航时间比3个参数对工况块进行K-均值聚类分析,采用距离聚类中心越近越能代表簇特征的原则选取工况块,最终拟合出某城市循环工况,并对其从特征参数、转毂实验和废气分析采集的油耗和排放数据3个方面与其他典型城市循环工况进行了对比。对比分析结果表明:采用本方法构建的城市循环工况能够很好地反映某地实际交通道路状况,具有实用价值。

A construction procedure of driving cycle is proposed using K-means clustering method.Experimental data of a city driving cycle is obtained by driving test vehicle,and is divided into＂microtrip＂after preprocessing.The micro-trips are then clustered into groups in ＂average speed＂,＂distance＂and ＂cruise time percentage＂using the K-means clustering method.The micro-trips are pick out on the principle of the closer distance between the micro-trip＇s driving feature and the center of cluster,which is more representative of the cluster.Then the final city driving cycle is developed.The data of fuel consumption and pollutant emission are collected using revolving drum test and exhaust-gas analysis.The characteristic parameter,fuel consumption and pollutant emission among different typical driving cycles are compared.Results show that the driving cycle constructed by this new approach can better reflect the city actual traffic conditions and it has high practical value.