基于增广卡尔曼滤波并考虑车辆加速度的路面不平度识别

doi:10.19562/j.chinasae.qcgc.2022.02.012

Abstract

Abstract:

In order to achieve the accurate identification of road unevenness under actual acceleration and deceleration driving conditions， a road surface identification method based on augmented Kalman filtering algorithm with consideration of vehicle acceleration （AKF-a） is proposed. With the longitudinal acceleration of the vehicle as the known input， and the vertical and pitching vibrations of vehicle body as the observation vectors， the augmented Kalman filter observer is designed to estimate the roughness information of road surface. The international roughness index within the fixed displacement window length is obtained to achieve the grade classification of road surface. The results of simulation show that under typical non-uniform speed conditions， urban operating conditions and braking conditions， the identification accuracy of road unevenness and the correctness of road grade classification with AKF-a algorithm proposed are apparently higher than those with general AKF algorithm and can effectively identify the unknown input road surface.

Key words: road surface roughness, augmented Kalman filter, vehicle acceleration, road grade classification

Lang Liu,Zhifei Zhang,Hongwei Lu,Zhongming Xu. Road Roughness Identification Based on Augmented Kalman Filtering with Consideration of Vehicle Acceleration[J].Automotive Engineering, 2022, 44(2): 247-255.

Figures/Tables 23

参数	数值	参数	数值
$m H / k g$	730	$c r ?$ / $(k N ? s ? m - 1)$	1.5
$I y / (k g ? m 2)$	1 200	$k t f ?$ / $(k N ? m - 1)$	247.03
$m f / k g$	55	$k t r$ / $(k N ? m - 1)$	247.03
$m r / k g$	52.5	$L f / m$	1.187
$k f ?$ / $(k N ? m - 1)$	28.6	$L r / m$	1.533
$k r$ / $(k N ? m - 1)$	39.6	$h / m$	0.6
$c f ?$ / $(k N ? s ? m - 1)$	1.85

道路等级	$G q (n 0) / 10 - 6$ m³			$I R I$ /（m·km^-1）
道路等级	下限	几何平均	上限	下限	几何平均	上限
A	8	16	32	1.658 5	2.218 2	3.137 0
B	32	64	128	3.137 0	4.436 4	6.274 0
C	128	256	512	6.274 0	8.872 8	12.548 1
D	512	1 024	2 048	12.548 1	17.745 7	25.096 2

观测量	误差典型幅值	R
$x ¨ H$ /（m·s^-2）	0.005	$2.5 × 10 - 5$
$θ ˙$ /（rad·s^-1）	0.003	$9.0 × 10 - 6$
$x H$ /m	0.004 5	$2.0 × 10 - 5$
$θ$ /rad	0.001 2	$1.4 × 10 - 6$

状态变量	Q	状态变量	Q
$x H$	$2.4 × 10 - 7$	$x ˙ H$	$2.9 × 10 - 5$
$θ$	$3.1 × 10 - 7$	$θ ˙$	$3.0 × 10 - 5$
$x f$	$1.8 × 10 - 7$	$x ˙ f$	$7.8 × 10 - 4$
$x r$	$6.8 × 10 - 7$	$x ˙ r$	$3.6 × 10 - 3$

识别方法	$ρ$	RSME/mm
AKF	0.351 9	4.568
AKF-a	0.948 5	0.973 6

References 26

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识别方法	工况一	工况二	工况三	工况四
AKF	2.495	4.263	6.174	8.154
AKF-a	1.638	1.852	2.117	2.443

识别方法	工况一	工况二	工况三	工况四
AKF	0.779 8	0.564 0	0.398 9	0.281 5
AKF-a	0.891 9	0.864 0	0.823 5	0.766 8

Road Roughness Identification Based on Augmented Kalman Filtering with Consideration of Vehicle Acceleration

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