面向昆明的典型城市乘用车行驶工况构建

Typical Driving Cycle Construction for Passenger Car in Kunming

为提高构建行驶工况的准确性和代表性,基于采集的10辆出租车930余万条数据,构建了典型城市乘用车行驶循环工况。首先,运用切比雪夫滤波器和筛选规则对行驶工况实测数据进行预处理,然后进行短行程划分,采用主成分分析法对高维特征参数进行降维处理,运用两步聚类分析进行聚类和异常值提取,获取三类短行程样本库,并通过短行程切分法构建怠速段库和运动段库,确定了怠速段和运动段时间分布;最后,依据分布特征参数平均绝对误差最小原则,构建了持续时间为1 200 s的拥堵、一般拥堵、畅通行驶循环工况和总行驶循环工况。结果表明,构建的城市行驶工况与道路试验数据平均绝对误差不大于3.15%,说明该方法是准确可行的。

Aiming to improve the accuracy and representativeness of driving cycle,based on more than 9.3 million test data of 10 taxis,the driving cycle for passenger cars of a typical city is constructed.Firstly,the Chebyshev filter and the screening rule are used to preprocess the measured data of the driving cycle and then short-trips are divided.Principal components analysis method is adopted to reduce the dimensionality of the characteristic parameters.Two-step clustering analysis is applied to extract the clustering and abnormal value for the feature parameters matrix to obtain three types of short-trip databases.Through the short-trip segmentation method,the idle condition database and running condition database are constructed,then the time distributions of the idle section and the running section are determined.Finally,the traffic jam cycle,the slight jam cycle,the clear road cycle and total driving cycle of 1 200 s are built according to the minimum mean absolute deviation of distribution characteristics.The results show that the mean absolute deviation of driving cycles and road test data is no more than 3.15%,which indicates that the method is accurate and feasible.