基于用户关联的车辆耐久性载荷谱编制

doi:10.13229/j.cnki.jdxbgxb.20220619

Abstract

Abstract:

A new method to divide vehicle's driving conditions was presented and used in compiling vehicle durability load spectrum based on customer usage correlation. A seven dimension random vector was adopted to distinguish， quantify and identify road roughness， topographic feature and driving habit for division of vehicle's driving conditions. Correlation relations between the seven random variables were studied. The statistic results showed that any two variables among the seven random variables were not highly related， which indicated that further simplifying the method to divide vehicle's driving condition was not supported. Besides， the seven random variables were not independent， which highlighted the necessity to use the associated probability density distribution to describe vehicle's driving conditions. The results shown that the chosen dimensions thoroughly described and defined vehicle's driving conditions， which became an organic unity. When correlating to different loads of vehicle， the organic unity showed significant but distinctive overall correlation relationship， which could be used to compile vehicle durability load spectrum correlated to customer usage.

Key words: vehicle engineering, driving condition, big data analysis, customer usage correlation, vehicle durability, load spectrum

CLC Number:

U467

Xu-dong LI,Xin-yu WANG,Cheng TIAN,Xin-feng ZHANG,Zhi-hui NIU,Zhi-qiang ZHAO. Compiling vehicle durability load spectrum based on customer usage correlation[J].Journal of Jilin University(Engineering and Technology Edition), 2024, 54(1): 66-75.

Figures/Tables 9

Fig.1

Table 1

Correlation coefficients between randomvariables"

	$X x 0$	$X x 1$	$X y$	$X z$	$X S p e e d$
$X x 0$	1	0.1283	0.4943	0.4004	-0.3982
$X x 1$	0.1283	1	0.2743	0.1926	-0.2290
$X y$	0.4943	0.2743	1	0.4669	-0.4028
$X z$	0.4004	0.1926	0.4669	1	0.0178
$X S p e e d$	-0.3982	-0.2290	-0.4028	0.0178	1

Table 1

Table 2

Partial correlation coefficients betweenrandom variables"

	$X x 0'$	$X y'$	$X z'$	$X S p e e d'$
$X x 0'$	1	0.2186	-0.3036	-0.3248
$X y'$	0.2186	1	0.4153	0.3581
$X z'$	-0.3036	0.4153	1	0.3278
$X S p e e d'$	-0.3248	0.3581	0.3278	1

Table 2

Fig.2

Table 3

Fig.3

Table 4

Summary of hypothesis test results"

所分析的载荷	速度区间/（km·h^-1）	比较状态	$T$	$t n + m - 2 (α 2)$ 或 $u α / 2$
质心垂向加速度	40~60	空载vs满载	6.1373	1.9617
		空载vs半载	4.6003
		半载vs满载	2.0517
	80~100	空载vs满载	6.7046	1.96
		空载vs半载	5.4818
		半载vs满载	2.1199
前稳定杆剪应变	40~60	空载vs满载	3.9669	1.9617
前稳定杆剪应变	80~100	空载vs满载	2.6136	1.96
左前轮六分力垂向力分量	40~60	空载vs满载	0.9709	1.9617
		空载vs半载	0.5257
		半载vs满载	0.4981
	80~100	空载vs满载	1.2384	1.96
		空载vs半载	2.2909
		半载vs满载	1.0527

Table 4

Fig.4

Fig.5

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维度	前稳定杆应变	质心处垂向加速度	左前轮六分力所测得的力			左前转向拉杆横向力
维度	前稳定杆应变	质心处垂向加速度	纵向分量	侧向分量	垂向分量	左前转向拉杆横向力
车速	-0.4028	-0.0254	-0.4473	-0.435	-0.0207	-0.4857
坡度	0.0294	0.0467	-0.1679	-0.0178	0.0527	-0.1048
纵向加速	0.479	0.4563	0.5879	0.4914	0.4588	0.702
纵向减速	0.2706	0.2363	0.535	0.2967	0.2245	0.3488
侧向	0.8258	0.5676	0.4347	0.9122	0.5734	0.761
轴头垂向	0.5867	0.835	0.1889	0.394	0.914	0.4359

Compiling vehicle durability load spectrum based on customer usage correlation

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