一种基于数据融合的全轮驱动车辆质心侧偏角估计方法

为准确估计全轮电驱动车辆行驶状态参数，设计了一种基于数据融合的质心侧偏角估计方法。该方法充分利用低成本普通车载传感器信息、电机输入信息和驾驶信号，在建立非线性3自由度 车辆模型和轮胎模型基础上，采用无迹卡尔曼滤波算法对质心侧偏角进行估计；同时通过信号积分法估计质心侧偏角，结合车辆行驶工况和路面条件，将无迹卡尔曼滤波和信号积分两种算法结果进行了数据融合。基于硬件在环实时仿真平台进行了车辆操纵仿真验证，结果表明，提出的估计算法与单一估计算法相比，具有更高的观测精度，能够满足多种行驶工况下的质心侧偏角观测需求。

Method for Estimating Sideslip Angle of All-wheel Drive Vehicle Based on Data Fusion

A method for estimating sideslip angle based on data fusion is proposed to obtain the driving state parameters of all-wheel electric drive vehicle. On the basis of three-degree-of-freedom vehicle model and tire model, the method fully utilizes the information from a low-cost common vehicle-mounted sensor, in-wheel motor input information as well as driving signals, and the sideslip angle is estimated by using the unscented Kalman filter algorithm. Besides, the sideslip angle is estimated by signal integration method. Combined with vehicle driving conditions and road conditions, the estimated values of the unscented Kalman filtering algorithm and the signal integral algorithm are fused to obtain the final estimation of sideslip angle. A series of simulations were conducted on the hardware-in-the-loop real-time simulation platform. The results show that the proposed estimation algorithm has higher observation accuracy compared with the single estimation algorithm, which can meet the requirements of mass center sideslip angle observation under various driving conditions. Key