基于扩展卡尔曼滤波算法的车辆参数估计  

Vehicle Parameter Estimation Based on Extended Kalman Filter Algorithm

作　　者：熊俊程 李强飞 张岩 XIONG Juncheng;LI Qiangfei;ZHANG Yan(School of Automobile,Chang'an University,Xi'an 710064,China)

机构地区：[1]长安大学汽车学院,陕西西安710064

出　　处：《汽车实用技术》2023年第21期54-58,共5页Automobile Applied Technology

摘　　要：为解决智能汽车传感器测量质心侧偏角成本高昂的问题,文章提出了通过扩展卡尔曼滤波(EKF)算法降低测量成本的方法。基于Dugoff轮胎模型在MATLAB/Simulink软件中构建车辆非线性二自由度仿真模型,利用EKF算法设计车辆参数和轮胎侧偏力的状态观测器,设计了一种适应多工况条件的自适应协方差矩阵。对设计的算法进行多种典型工况实验仿真验证,与汽车动力学软件CarSim同工况下进行曲线对比,仿真值与估计值基本吻合。结果表明,所设计的车辆参数估计算法具有良好的多工况适应能力,具有一定的实用性。In order to solve the problem of high cost of measurement of centroid side deflection angle by intelligent vehicle sensor,a low-cost measurement method using extended kalman filter(EKF)algorithm is proposed in this paper.Based on Dugoff tire model,a vehicle nonlinear two-degree-of-freedom simulation model is constructed in MATLAB/Simulink software.The state observer of vehicle parameters and tire side force is designed by using EKF,and an adaptive covariance matrix is designed to adapt to multi-operating conditions.The designed algorithm is verified by experimental simulation in a variety of typical working conditions,and the curve is compared with that of CarSim,the vehicle dynamics software,under the same working conditions,and the simulated value is basically consistent with the estimated value.The results show that the designed vehicle parameter estimation algorithm has good adaptability to multiple operating conditions and has certain practicability.

关 键 词：EKF算法 质心侧偏角 横摆角速度 Dugoff轮胎模型 

分 类 号：U461.6[机械工程—车辆工程]