基于多信息融合的汽车质心侧偏角估算

车辆稳定性控制系统因为其良好的主动安全性已经在汽车上广泛采用。对于汽车稳定性控制系统而言,横摆角速度和质心侧偏角是判断汽车运行情况的两个主要参考量。其中,车辆的横摆角速度可以通过横摆角速度传感器经过卡尔曼滤波直接得到,而质心侧偏角则必须通过估算得到。基于二自由度汽车动力学模型建立了一种车辆质心侧偏角估算器,该估算器包括基于车辆模型的卡尔曼滤波算法和动力学积分算法。在质心侧偏角较小的情况下,可认为轮胎的侧偏特性处于线性区域,故采用基于车辆模型的卡尔曼滤波算法,当质心侧偏角较大的情况下,切换为动力学积分算法。最后在Vedyna软件下搭建了该估算器的仿真平台,通过多工况的仿真,仿真结果表明该估算器可以准确估算车辆的质心侧偏角。

Estimation of Vehicle Sideslip Angle Based on Multi-information Fusion

Vehicle stability control system has been widely used in the cars. According to the vehicle stability control system, the yaw rate and the sideslip angle are very important parameters for vehicle stability control. Among them, the vehicle yaw rate can obtained by the yaw-rate sensor after kalman filter, but the vehicle sideslip angle must be estimated. Vehicle sideslip angle estimator is built based on the 2 DOFs vehicle Dynamic model in this paper. This estimator includes two parts: Kalman filter algorithm and dynamic integral algorithm. When the sideslip angle is small, the kalman filter algorithm could be used in estimator. When the sideslip angle is large, the estimator switches to dynamic integral algorithm. The simulation model was built in software Vedyna. The simulation results show that the sideslip angle estimator can achieve the precise estimation results whether the vehicle is in linear area or in the nonlinear area.