Research on Estimation of Key Parameters of Automotive ESP System
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摘要: 汽车ESP系统的关键参数对ESP系统控制效果起着至关重要的作用，而这些参数很难直接获取，对其进行估算是一种切实可行的方法。在CarSim阶跃工况下，利用递推最小二乘算法对不同转角、车速下等效前、后轴侧偏刚度进行辨识，并拟合辨识的MAP图。建立汽车3自由度简化模型，模型中等效前、后轴侧偏刚度随转角和车速自适应变化。将改进后的Sage-Husa噪声估计器结合无迹卡尔曼滤波对汽车行驶状态参数质心侧偏角、横摆角速度、纵向车速和横向车速进行估算，估算结果与CarSim软件仿真结果吻合度高；将估算的汽车行驶状态参数作为输入，利用自适应无迹卡尔曼滤波对高附着路面、低附着路面以及对接路面的附着系数进行估算，估算结果与CarSim路面附着系数设置值吻合程度高。Abstract: The key parameters of the motors ESP system play a vital role in the control effect of ESP, and these parameters are difficult to be obtained directly, so it is a feasible method to estimate them. In the CarSim step condition, the recursive least squares algorithm is used to identify the equivalent lateral deflection stiffness of front and rear axles at different angles and vehicle speeds, and the identified MAP was fitted. A 3-degree of freedom simplified vehicle model is established, in which the equivalent lateral stiffness of the front and rear axles changes with the angle of rotation and the speed of the vehicle. The improved Sage-Husa noise estimator was combined with unscented Kalman filter to estimate sideslip angle, yaw rate, longitudinal speed and transverse speed of the vehicle running state parameters. The estimated results were in good agreement with the simulation results of CarSim software. Taking the estimated vehicle driving state parameters as input, the adaptive traceless Kalman filter is used to estimate the adhesion coefficients of high-adhesion road surface, low-adhesion road surface and split road surface. The estimated results are in good agreement with the CarSim road adhesion coefficient setting values.
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表 1 汽车参数
参数及单位 数值 汽车质量m/kg 1 331 前轴到质心距离a/m 1.14 后轴到质心距离b/m 1.46 绕Z轴转动惯量Iz/ 2 031.4 轮胎有效半径R/m 0.311 汽车质心高度h/m 0.54 静止时前轮侧偏刚度Cxf/(N·rad-1) 105 108 静止时前轮纵向刚度Cyf/(N·rad-1) 854 121 静止时后轮侧偏刚度Cxr/(N·rad-1) 83 330 静止时后轮纵向刚度Cyr/(N·rad-1) 65 248 前轮轮距df/m 1.695 后轮轮距dr/m 1.695 前后轴距l/m 2.6 
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