基于车轮力传感器信息的全轮驱动车辆状态估计

State Estimation for 4WD Vehicle Based on Information of Tyre Force Sensor

提出一种基于车轮侧向力和纵向力传感器信息的车辆状态观测器。建立3自由度车辆动力学模型,并构建扩展卡尔曼滤波器,结合纵向加速度传感器和横摆角速度传感器的校正信息,实时估计车辆的纵向车速和质心侧偏角。在复杂附着条件下,该车辆状态观测器对车轮滑移和路面附着条件有很好的鲁棒性。通过veDYNA车辆动力学仿真软件,对该观测器进行了仿真验证。在分离附着系数路面条件下的仿真结果显示,传统的基于2自由度和非线性轮胎模型估计方法的纵向车速最大估计误差为25 km/h,质心侧偏角最大估计误差为3°,相同工况下,提出的基于车轮力传感器信息的全轮驱动车辆状态观测器对车辆的纵向车速和质心侧偏角估计结果具有更好的精确度,最大估计误差分别不超过0.6 km/h和0.2°,对车轮滑移和复杂路面附着条件具有更强的自适应能力。

A state estimation method for 4WD vehicle was demonstrated by measuring lateral tire forces and longitudinal tire forces. Based on 3-DOF vehicle model, a state observer was realized using extended Kalman filter technique. The observer provided several advantages that the vehicle velocity and sideslip angle estimates could be robust with variance of wheel slip ratio and road friction under extreme adherence condition, such as split friction road. The paper demonstrated the appropriateness of this observer by slalom simulation test using veDYNA software. Simulation results were compared with traditional nonlinear state observer. The comparisons indicated that the longitudinal velocity and sideslip angle estimates of proposed observer both approached the true value very well. The maximum errors were no more 0.6 km/h and 0.2°, comparing to 25 km/h and 3° of the maximum errors of traditional nonlinear state observer. The proposed estimation method was an effective self-adaptation approach to wheel slip ratio and road friction condition.