基于主成分分析与K-means聚类的汽车行驶工况构建

Construction of Vehicle Driving Cycle Based on Principal Component Analysis and K-means Clustering

汽车行驶工况图是车辆开发、评价最为基础的依据。以汽车行驶GPS数据为基础,综合运用多种数据处理和数据分析方法,根据运动学片段构建误差较小的汽车行驶工况图。首先,对数据进行清洗,提取运动学片段;然后利用15种特征参数对运动学片段提取特征,之后对特征参数矩阵进行主成分分析和K-means聚类,构建出一条行驶工况曲线;最后将构建的行驶工况曲线和采样总体进行误差分析,得到相对误差值为0.84,验证了该汽车行驶工况图的有效性和合理性。

The vehicle driving condition diagram is the most basic basis for vehicle development and evaluation.Based on the GPS data of vehicle driving,use a variety of data processing and data analysis methods comprehensively and construct the vehicle driving cycle diagram with small errors according to the kinematics segment.First,the data were cleaned and the kinematics fragments were extracted.Secondly,15 characteristic parameters were used to extract features from the kinematics fragments.Then,principal component analysis and K-means clustering were carried out on the characteristic parameter matrix to construct a driving cycle curve.Finally,error analysis was conducted on the constructed driving cycle curve and the sampling population,and the relative error was 0.84.The results verified the validity and rationality of the vehicle driving cycle diagram constructed.