典型汽车运行工况的状态转移特征分析

doi:10.13229/j.cnki.jdxbgxb20170301

摘要/Abstract

摘要： 针对当前运行工况研究仅选取某些特征参数作为特征、缺乏对工况状态转移特征分析的不足,本文在提取典型工况特征参数的基础上,运用主成分分析和K均值聚类获得行驶特征较明显的3类工况,并分析各类工况状态转移转移矩阵(TPM)的状态、转移概率和特征值。结果表明:各类TPM状态、转移概率的分析与各类典型工况特征参数的分析一致;特征值对高速、混合和城市道路运行工况的工况生成效率存在本质影响,揭示了运行工况设计中构建两参数TPM的合理性。

关键词: 车辆工程, 状态转移概率矩阵, 主成分分析, K均值聚类, 典型运行工况

Abstract: In current research of vehicle driving cycles only some parameters are selected as characteristics of the driving cycles, which lacks the analysis of the characteristics of driving cycle state transition. To solve this problem, based on the characteristic parameters extracted from typical driving cycles, this paper adopts the principal component analysis and K-means clustering to obtain three kinds of typical driving cycles, which represent the vehicle driving characteristics under different traffic conditions. The state, transition probability and eigenvalues of the state Transition Probability Matrixes (TPM) under the typical driving cycles are analyzed, and the analysis results are consistent with that of the characteristic parameter analysis under three kinds of typical driving cycles. It is shown that the eigenvalues essentially influences the efficiency of cycle generation of highway, suburb and city roads, revealing the reasonability of construction two-parameter TPM in designing driving cycles.

Key words: vehicle engineering, state transition probability matrix, principal component analysis, K-means clustering, typical driving cycles

中图分类号:

U461.1

张曼, 施树明. 典型汽车运行工况的状态转移特征分析[J]. 吉林大学学报(工学版), 2018, 48(4): 1008-1015.

ZHANG Man, SHI Shu-ming. Analysis of state transition characteristics for typical vehicle driving cycles[J]. 吉林大学学报(工学版), 2018, 48(4): 1008-1015.

参考文献

[1] Bata R, Yacoub Y, Wang W, et al.Heavy duty testing cycles: survey and comparison[C]∥SAE Paper, 942263.
[2] Andre M.Driving cycles development: characterization of the methods[C]∥SAE Papers, 961112.
[3] Lin J.A Markov process approach to driving cycle development[D]. Davis: University of California, 2002.
[4] Hung W T, Tam K M, Lee C P, et al.Comparison of driving characteristics in cities of Pearl River Delta, China[J]. Atmospheric Environment, 2005, 39(4): 615-625.
[5] 刘志华, 葛蕴珊, 谭建伟, 等. 柴油公交车行驶工况解析及排放特性研究[J]. 北京理工大学学报, 2010, 30(11):1285-1289.
Liu Zhi-hua, Ge Yun-shan, Tan Jian-wei, et al.Analysis of bus driving cycle and investigation of bus emission characteristic[J].Journal of Beijing Institute of Technology, 2010, 30(11): 1285-1289.
[6] Shi S, Lin N, Zhang Y, et al.Research on markov property analysis of driving cycles and its application[J]. Transportation Research Part D:Transport & Environment, 2016, 47:171-181.
[7] Liu L, Huang C, Lu B, et al.Study on the design method of time-variant driving cycles for EV based on Markov process[C]∥Vehicle Power and Propulsion Conference (VPPC), IEEE, 2012: 1277-1281.
[8] Yue B, Shi S, Lin N, et al.Study on the design method of driving cycle with road grade based on Markov chain model[C]∥Vehicle Power and Propulsion Conference, IEEE, 2015:1-5.
[9] 韦淑颖. 乘用车瞬态运行工况设计方法的改进研究[D]. 长春:吉林大学交通学院, 2008.
Wei Shu-ying.Improvements of the design method of transient driving cycles for passenger car[D]. Changchun: College of Transportation, Jilin University, 2008.
[10] 石琴, 仇多洋, 周洁瑜. 基于组合聚类法的行驶工况构建与精度分析[J]. 汽车工程, 2012, 34(2):164-169.
Shi Qin, Chou Duo-yang, Zhou Jie-yu.Driving cycle construction and accuracy analysis based on combined clustering technique[J]. Automotive Engineering, 2012,34(2):164-169.
[11] DieselNet. Emisson test cycle[EB/OL][2016-04-24].https:∥www.dieselnet.com/standards/cycles/index.php.
[12] Lee T C, Judge G G, Zellner A.Estimating the parameters of the markov probability model from aggregate time series data[J]. Journal of the American Statistical Association, 1971, 66(335).
[13] Haveliwala T, Kamvar S.The second eigenvalue of the Google matrix[R]. San Francisco Bay Area: Stanford University, 2003.
[14] 孙洪祥. 随机过程[M]. 北京:机械工业出版社,2007.
[15] 苟江涛. 基于谷歌矩阵第二特征值重数的两类外推加速算法[D]. 北京:清华大学数学科学系, 2009.
Gou Jiang-tao.The multiplicity of second eigenvalue of Google matrix and two new extrapolation methods[D]. Beijing: Department of Mathematical Sciences, Tsinghua Uuniversity, 2009.
[16] Haveliwala T, Kamvar S, Klein D, et al.Computing PageRank using power extrapolation[R]. San Francisco Bay Area: Stanford University, 2003.

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