基于冗余信息融合的车辆质心侧偏角估计方法

doi:10.19562/j.chinasae.qcgc.2022.02.016

Abstract

Abstract:

Vehicle sideslip angle is one of the important parameters to characterize the lateral stability of vehicle， and the study on related estimation methods can provide important support for vehicle stability control. In order to improve the effectiveness of vehicle sideslip angle estimation， a method of vehicle sideslip angle estimation based on redundant information fusion is proposed in this paper. The vehicle dynamic model and kinematic model is established respectively， and the dynamic-model-based estimator and kinematic-model-based estimator for vehicle driving state estimation are designed by using the cubature Kalman filter algorithm. At the same time， the inherent characteristics and application scope of the dynamic-model-based estimator and the kinematic-model-based estimator are analyzed， and on this basis， a vehicle sideslip angle estimation method based on redundant information fusion is proposed to fully integrate the advantages of dynamic-model-based estimator and kinematic-model-based estimator by means of adaptive weight dynamic adjustment. The simulation test in CarSim-Simulink co-simulation model and the vehicle road test are carried out. The results show that the proposed vehicle sideslip angle estimation method can effectively improve the accuracy of vehicle state estimation and adaptability to multiple driving conditions.

Key words: state estimation, sideslip angle, Kalman filter, information fusion

Qiu Xia,Te Chen,Long Chen,Xing Xu,Yingfeng Cai. Vehicle Sideslip Angle Estimation Method Based on Redundant Information Fusion[J].Automotive Engineering, 2022, 44(2): 280-289.

Figures/Tables 13

References 14

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参数	数值
整车质量/kg	800
车轮有效半径/m	0.245
前、后轮等效侧偏刚度/（kN?rad^-1）	60、40
质心与前、后轴的距离/m	0.795、0.975
前后轴半轮距/m	0.775
汽车转动惯量/（ kg?m²）	1 000

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Vehicle Sideslip Angle Estimation Method Based on Redundant Information Fusion

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