Vehicle Mass and Road Slope Estimation Based on Interactive Multi-model

被引：0
|
作者：
Zhao J. [1 ]
Li Z.-X. [1 ]
Zhu B. [1 ]
Li Y.-X. [1 ]
Sun Y.-Z. [1 ]
机构：
[1] State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, 130022, Jilin
来源：
Zhongguo Gonglu Xuebao/China Journal of Highway and Transport | 2019年 / 32卷 / 12期
关键词：
Automotive engineering; Confidence factor of mass estimation; Intelligent estimation; Interactive multi-model fusion; Mass and slope estimation; Real vehicle test;
D O I：
10.19721/j.cnki.1001-7372.2019.12.006
中图分类号：
学科分类号：
摘要：
Real-time and accurate acquisition of vehicle structural parameters and road environment information is important to improve the performance of intelligent vehicle motion control. Vehicle mass and road slope are necessary information for various vehicle control systems. Therefore, research on the online estimation of mass and slope has always been concerned. Aiming at the joint estimation of vehicle mass and road slope, an estimation method based on interactive multi-model is proposed in this paper. Firstly, the working conditions suitable for accurate mass estimation were established. Under these conditions, an algorithm for calculating the confidence factor of mass estimation based on fuzzy rules was proposed. Then, a recursive least squares vehicle mass estimation algorithm based on a confidence factor was designed to realize online mass estimation. Based on the vehicle longitudinal dynamics model, two kinds of slope estimation models, kinematics, and dynamics were established. The linear Kalman filter slope observer based on the kinematics model was designed to realize slope estimation based on the longitudinal acceleration information of ESP (Electronic Stability Program). The unscented Kalman filter slope observer based on the dynamic model was designed to realize slope estimation according to the force information of ESP and EMS (Engine Manage System). The kinematics model did not consider the vehicle attitude information, and the slope estimation results deviated from the actual values. The dynamic model required high precision and had poor stability. In order to make full use of the advantages of the two methods and achieve accurate slope estimation, the weighted fusion of the two methods was realized using the interactive multiple model algorithm. Finally, the algorithm was verified by vehicle test. The results show that the mass and slope estimation algorithm has good real-time performance and accuracy, and meets the application requirements for intelligent vehicle motion control. © 2019, Editorial Department of China Journal of Highway and Transport. All right reserved.
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页码：58 / 65
页数：7
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