Journal of Jilin University(Engineering and Technology Edition) ›› 2024, Vol. 54 ›› Issue (7): 1821-1830.doi: 10.13229/j.cnki.jdxbgxb.20221193

   

Vehicle speed decoupling road identification method based on least squares

Jian-ze LIU1(),Jiang LIU1,2(),Min LI1,Xin-jie ZHANG2   

  1. 1.School of Mechanical and Automotive Engineering，Qingdao University of Technology，Qingdao 266520，China
    2.State Key Laboratory of Automotive Simulation and Control，Jilin University，Changchun 130022，China
  • Received:2022-09-15 Online:2024-07-01 Published:2024-08-05
  • Contact: Jiang LIU E-mail:liujianze@qut.edu.cn;liujiang@qut.edu.cn

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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

CLC Number: 

  • U461.4

Fig.1

Schematic diagram of a 2-DOF 1/4 vehicle model"

Table 1

5-grade classification of road roughness"

路面等级Gq （n0）/10-6m3（n0=0.1 m-1）
下限几何平均值上限
A81632
B3264128
C128256512
D5121 0242 048
E2 0484 0968 192

Table 2

Value of the vehicle model parameters"

参数数值参数数值
mb/kg320n0/m-10.1
mw/kg40Gq（n0）/m36.4×10-5
Ks/（N·m-1）2×104f0/Hz0.1
Kt/（N·m-1）2×105cs/（N·s·m-1）1 000

Fig.2

Comparison of dynamic parameters and variable road surface at different speeds"

Fig.3

RMS value of road excitation in 5 road grades"

Fig.4

Comparison diagram of theoretical and estimated values of Gq（n0） for 5 road roughness coefficient"

Fig.5

Test vehicle and test equipment diagram"

Fig.6

Test route plan for the test vehicle"

Fig.7

Comparison of BA data between test vehicle and simulation"

Table 3

Test results of 4 different speeds in road 1"

车速/

（km·h-1）

仿真RMS（BA）/

（m·s-2）

试验RMS（BA）/

（m·s-2）

试验

Gq（n0）/10-6m3

100.398 40.413 5128.64
200.486 70.497 4126.72
300.615 40.626 2147.84
400.920 00.931 9179.20

Table 4

Test results of different speeds in roads 4， 5 and 6"

车速/

（km·h-1）

仿真RMS（BA）/

（m·s-2）

试验RMS（BA）/

（m·s-2）

试验

Gq（n0）/10-6m3

401.276 31.301 1376.96
501.505 81.525 1383.36
601.781 61.857 7440.96

Fig.8

Comparison of theoretical and test values of PSD"

Fig.9

Comparison diagram of estimated and fitted test values"

Table 5

Comparison of test value and fitting value of road 1"

车速/

（km·h-1）

Gqi 试验

RMS/10-4m3

Gq_fit拟合

RMS/10-4m3

RMSE/

10-5m3

δRMS/%
101.411.463.76-3.28
201.421.464.00-3.24
301.451.465.07-0.09
401.401.463.72-4.14
1 郭孔辉, 余五辉, 章新杰, 等. 自适应半主动悬架系统控制策略[J]. 湖南大学学报:自然科学版, 2013, 40(2):39-44.
Guo Kong-hui, Yu Wu-hui, Zhang Xin-jie, et al. Semi-active suspension adaptive control strategy[J]. Journal of Hunan University(Natural Sciences), 2013, 40(2):39-44.
2 Yang Y A, Hl A, Xt A, et al. Fundamental mode shape estimation and element stiffness evaluation of girder bridges by using passing tractor-trailers[J]. Mechanical Systems and Signal Processing, 2022, 169:No.108746.
3 Abulizi N, Kawamura A, Tomiyama K, et al. Measuring and evaluating of road roughness conditions with a compact road profiler and ArcGIS[J]. Journal of Traffic and Transportation Engineering (English Edition), 2016, 3(5): 398-411.
4 Ori T R, Kone N M, Traore S. Development of a virtual environment for simulation of a 3D road profile using OpenCRG and MATLAB GUI[J]. Engineering, 2021,13(12):677-689.
5 李杰, 郭文翠, 赵旗, 等. 基于车辆响应的路面不平度识别方法[J]. 吉林大学学报:工学版, 2019,49(6):1810-1817.
Li Jie, Guo Wen-cui, Zhao Qi, et al. Road roughness identification based on vehicle responses[J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(6): 1810-1817.
6 管欣, 金号, 段春光, 等. 汽车行驶道路侧向坡度估计[J]. 吉林大学学报: 工学版, 2019, 49(6): 1802-1809.
Guan Xin, Jin Hao, Duan Chun-guang, et al. Estimation of lateral slope of vehicle driving road[J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(6): 1802-1809.
7 巩明德, 颜鑫. 采用路面识别方法的重型救援车辆主动悬架控制策略[J]. 西安交通大学学报, 2019, 53(2): 32-39.
Gong Ming-de, Yan Xin.A control strategy for active suspension of heavy rescue vehicles based on road level estimation[J]. Journal of Xi´an Jiaotong University, 2019, 53(2): 32-39.
8 Liu W, Wang R, Ding R, et al. On-line estimation of road profile in semi-active suspension based on unsprung mass acceleration[J]. Mechanical Systems and Signal Processing, 2020, 135:No.106370.
9 Ding R, Wang R, Meng X, et al. Intelligent switching control of hybrid electromagnetic active suspension based on road identification - ScienceDirect[J]. Mechanical Systems and Signal Processing, 2021,152: No.107355.
10 Xue K, Nagayama T, Zhao B. Road profile estimation and half-car model identification through the automated processing of smartphone data[J]. Mechanical Systems and Signal Processing, 2020, 142: No.106722.
11 Zhao B, Nagayama T, Xue K. Road profile estimation, and its numerical and experimental validation, by smartphone measurement of the dynamic responses of an ordinary vehicle[J]. Journal of Sound and Vibration, 2019, 457: 92-117.
12 Liu J, Liu J, Zhang X, et al. Transmission and energy-harvesting study for a novel active suspension with simplified 2-DOF multi-link mechanism[J]. Mechanism and Machine Theory, 2021, 160: No.104286.
13 寇发荣, 高亚威, 景强强, 等. 基于路面等级自适应的主动悬架LQG控制[J]. 振动与冲击, 2020, 39(23): 30-37.
Kou Fa-rong, Gao Ya-wei, Jing Qiang-qiang, et al. LQG control of active suspension based on adaptive road surface level[J]. Journal of Vibration and Shock, 2020, 39(23): 30-37.
14 Sun Y, Li L, Yan B J, et al. A hybrid algorithm combining EKF and RLS in synchronous estimation of road grade and vehicle' mass for a hybrid electric bus[J]. Mechanical Systems & Signal Processing, 2016, 68/69:416-430.
15 Li Z, Yu W, Cui X. Online classification of road roughness conditions with vehicle unsprung mass acceleration by sliding time window[J]. Shock and Vibration, 2018, 2018:1-13.
16 He Z, Yang Z, Cui X, et al. A method of state-of-charge estimation for EV power lithium-ion battery using a novel adaptive extended kalman filter[J]. IEEE Transactions on Vehicular Technology, 2020, 69(12):14618-14630.
17 隗海林, 包翠竹, 李洪雪, 等. 基于最小二乘支持向量机的怠速时间预测[J]. 吉林大学学报:工学版, 2018, 48(5): 1360-1365.
Kui Hai-lin, Bao Cui-zhu, Li Hong-xue, et al. Idling time prediction method based on least square support vector machine[J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1360-1365.
18 Güneş H. Design and manufacture of tube type nonhollow linear generators for suspension systems of electric and hybrid cars[J]. Journal of Process Mechanical Engineering, 2021, 235(5): 1420-1428.
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