基于最小二乘的车速解耦路面辨识方法

doi:10.13229/j.cnki.jdxbgxb.20221193

摘要/Abstract

摘要：

针对路面辨识方法需要大量训练集或高的算力支撑不利于驾乘感提升实现的问题，提出了一种改进的最小二乘估计方法，无需训练集，直接采集悬架响应来辨识路面激励及路面等级变化。在建立的路面等级系数和车速为变参数模型的基础上，探讨路面激励数据的取样处理规则，通过解耦行驶速度的影响，得到了实时的路面不平度系数。仿真结果表明，A-E级路面综合估值准确度在97%以上，对路面等级突变的响应时间少于0.15 s，对路面输入的跟随性能良好。采集不同路段不同车速下的实车动力学参数进行辨识，试验结果表明，该工况下估计值准确度为98.2%，与三米尺检测法所得实际路面等级相符，验证了这种车速解耦路面辨识方法的可行性及准确性。

关键词: 车辆工程, 路面估计, 最小二乘法, 路面不平度, 车速解耦

Abstract:

Aiming at the problem that the road recognition method requires a large number of training sets or high computational power support is not conducive to the realization of ride sensation improvement. In this paper， an improved least squares estimation method is proposed， which does not require a training set and directly collects vehicle responses to identify road excitation and road grade changes. On the basis of the variable parameter model of road grade coefficient and vehicle speed， the sampling processing rules of road excitation data are discussed， and the real-time road roughness coefficient is obtained by decoupling the influence of driving speed. The simulation results show that the comprehensive estimation accuracy of the A-E road grade is above 97%， the response time to the sudden change of road surface grade is less than 0.15 s， and the following performance to the road surface input is good. The dynamic parameters of the real vehicle at different speeds of different road sections are collected for identification. The test results show that the accuracy of the estimated value under this working condition is 98.2%， which is consistent with the actual road surface grade obtained by the three-meter-foot detection method. The feasibility and accuracy of this vehicle speed decoupling road identification method are verified.

Key words: vehicle engineering, road surface estimation, least squares method, road surface roughness, vehicle speed decoupling

中图分类号:

U461.4

刘建泽,柳江,李敏,章新杰. 基于最小二乘的车速解耦路面辨识方法[J]. 吉林大学学报(工学版), 2024, 54(7): 1821-1830.

Jian-ze LIU,Jiang LIU,Min LI,Xin-jie ZHANG. Vehicle speed decoupling road identification method based on least squares[J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(7): 1821-1830.

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路面等级	G_q （n₀）/10^-6m³（n₀=0.1 m^-1）
路面等级	下限	几何平均值	上限
A	8	16	32
B	32	64	128
C	128	256	512
D	512	1 024	2 048
E	2 048	4 096	8 192

参数	数值	参数	数值
m_b/kg	320	n₀/m^-1	0.1
m_w/kg	40	G_q（n₀）/m³	6.4×10^-5
K_s/（N·m^-1）	2×10⁴	f₀/Hz	0.1
K_t/（N·m^-1）	2×10⁵	c_s/（N·s·m^-1）	1 000

车速/ （km·h^-1）	仿真RMS（BA）/ （m·s^-2）	试验RMS（BA）/ （m·s^-2）	试验 G_q（n₀）/10^-6m³
10	0.398 4	0.413 5	128.64
20	0.486 7	0.497 4	126.72
30	0.615 4	0.626 2	147.84
40	0.920 0	0.931 9	179.20

车速/ （km·h^-1）	G_q_i 试验 RMS/10^-4m³	G_{q_fit}拟合 RMS/10^-4m³	RMSE/ 10^-5m³	δ_RMS/%
10	1.41	1.46	3.76	-3.28
20	1.42	1.46	4.00	-3.24
30	1.45	1.46	5.07	-0.09
40	1.40	1.46	3.72	-4.14