基于马尔科夫链的代表性行驶工况模型构建

Construction of Typical Driving Cycle Model Based on Markov Chain

针对现阶段以欧洲工况标准NEDC优化标定的汽车实际油耗与法规认证结果偏差较大的问题,以及为检验汽车燃油消耗量和污染物排放控制的研究提供理论参考,本文研究了乘用车代表性行驶工况模型的构建方法。首先,以福州市乘用车行驶数据为基础,采用聚类方法获取降维行驶特征,针对BIRCH聚类算法对数据集的插入顺序较为敏感,提出了基于K均值优化的BIRCH聚类算法(KM-BIRCH算法);然后,基于马尔可夫链理论,构建乘用车代表性行驶工况模型;最后,将构建工况与实际工况进行特征值对比分析。结果表明:构建的典型行驶工况与实际工况样本数据库总体特征的平均偏差仅为3.21%,满足偏差低于5%的开发精度要求,验证了代表性工况模型的准确性和有效性。

Aiming at the problem that there is a large gap between the actual fuel consumption of vehicle optimized and calibrated according to the European Driving Cycle Standard(NEDC)and the regulatory certification results,and to provide a theoretical reference for the study of vehicle fuel consumption and pollutant emission control,the construction method of typical driving cycle of passenger car was researched in this paper.First,according to the driving data of passenger cars in Fuzhou City,the reduced-dimension driving characteristics are obtained by cluster analysis.As the BIRCH clustering algorithm is sensitive to the insertion order of the data set,based on K-means clustering algorithm optimization BIRCH clustering algorithm(KM-BIRCH algorithm)is proposed;Then,Markov chain principle were applied to construct the typical driving cycle;Finally,a comparative analysis was made between the constructed typical driving cycle and the existing driving conditions.The results show that the average deviation of the overall characteristics of the constructed typical driving conditions and the actual operating conditions sample database is only 3.21%,which meets the development accuracy requirements with a deviation of less than 5%,which verifies the accuracy and effectiveness of the typical driving cycle.