Analysis of state transition characteristics for typical vehicle driving cycles

被引：0

作者：

Zhang M. ^{[1
]}

Shi S.-M. ^{[1
]}

机构：

[1] College of Transportation, Jilin University, Changchun

来源：

Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2018年 / 48卷 / 04期

关键词：

K-means clustering; Principal component analysis; State transition probability matrix; Typical driving cycles; Vehicle engineering;

D O I：

10.13229/j.cnki.jdxbgxb20170301

中图分类号：

学科分类号：

摘要：

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. © 2018, Editorial Board of Jilin University. All right reserved.

引用

页码：1008 / 1015

页数：7

共 15 条

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