基于二分法的车辆状态参数融合估计

doi:10.19562/j.chinasae.qcgc.2022.12.012

Abstract

Abstract:

In order to enhance the accuracy and reliability of vehicle state parameters estimation， a vehicle state fusion estimation method based on dichotomy is proposed. Firstly， an extended Kalman filtering （EKF） algorithm based on the vehicle 3-DOF dynamic model and the data-driven radial basis function （RBF） neural network estimation algorithm are designed. Then for further raising the reliability of the estimation algorithm and reducing the estimation error of single algorithm， a fusion estimation method is proposed with model-driven estimation algorithm and data-driven estimation algorithm compensating each other. The weights of the estimation results of EKF and RBF neural network are assigned based on dichotomy and the fusion of algorithms enhances the estimation accuracy. Finally， the co-simulation of MATLAB/Simulink and CarSim， and the real vehicle in-the-loop experiment are performed to verify the effectiveness of the fusion method. The results show that the change trend of the estimation result is consistent with the real one， and the estimation accuracy of fusion algorithm proposed is significantly enhanced， compared to RBFNN and single EKF algorithms.

Key words: state parameters, fusion estimation, dichotomy, EKF algorithm, RBF neural network algorithm

Yunfei Zha,Lü Xiaolong,Xinye Liu,Fangwu Ma. Fusion Estimation of Vehicle State Parameters Based on Dichotomy[J].Automotive Engineering, 2022, 44(12): 1910-1918.

Figures/Tables 16

References 25

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驾驶工况	车速/（km·h^-1）
驾驶工况	匀加速	匀减速	变加速
直线驾驶	20-70	80-30	10-60
转向驾驶	20-70	80-30	10-60
蛇行驾驶	20-70	80-30	10-60

整车各部分参数	数值
整车质量m/kg	600
质心至前轴的距离a/m	1 .080
质心至后轴的距离b/m	1.320
转动惯量I_z /（kg·m²）	1 343.1
质心高度h/m	0.52

车辆状态参数	估计方法	e_RMS
横摆角速度	EKF	0.176 5
	RBF	0.009 5
	OPT	0.005 1
质心侧偏角	EKF	0.007 8
	RBF	0.013 0
	OPT	0.002 6
纵向车速	EKF	1.324 0
	RBF	0.498 0
	OPT	0.438 1

车辆状态参数	估计方法	e_RMS
横摆角速度	EKF	0.545 9
	RBF	0.425 7
	OPT	0.052 1
纵向车速	EKF	3.602 3
	RBF	0.816 1
	OPT	0.270 9

Fusion Estimation of Vehicle State Parameters Based on Dichotomy

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References 25

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