%0 Journal Article
%T 采用RKF轮胎力估计的4WID电动汽车纵向速度估计研究<br>Estimating Longitudinal Velocity of Four-wheel-independent-driving Electric Vehicle based on RKF Tire Force Estimator
%A 叶浩
%A 刘国海
%A 张多
%A 王洋
%J 机械科学与技术
%D 2017
%X 车辆纵向速度、轮胎力的准确获取是车辆主动安全控制的前提和基础，由于直接测量纵向速度和轮胎力的车载传感器价格极其昂贵，提出了采用随机卡尔曼（RKF）轮胎力估计的纵向车速估计。利用普通车载传感器直接测量的车辆状态参数结合车辆七自由度模型，采用RKF算法估计轮胎侧向力；其次，根据估计的轮胎力，运用卡尔曼滤波（KF）技术实现纵向车速估计。CarSim与MATLAB/Simulink联合仿真结果验证了该方法的有效性。<br>The accurate acquisition of longitudinal velocity and tire-road force is the premise and foundation of vehicle active safety control. Because the sensors of longitudinal velocity and tire force are expensive, we conduct the longitudinal velocity estimation based on the random Kalman filter (RKF) tire force estimator. First, the lateral tire force estimator based on the RKF is designed by using the vehicle states which are measured from on-board sensor combined with the vehicle's seven-DOF model. Then, the Kalman filtering algorithm is adopted to realize the estimation of the longitudinal velocity by relying on the estimated tire force. The co-simulations carried out by CarSim and MATLAB/Simulink demonstrate the effectiveness of the proposed estimator
%K 随机卡尔曼滤波
%K 侧向轮胎力估计
%K 纵向车速
%K 状态估计
%K 电动汽车&lt
%K br&gt
%K Kalman filter
%K algorithms
%K tire force
%K longitudinal velocity
%K state estimation
%K sensors
%K degree of freedom
%K model
%K electric vehicle
%K MATLAB
%U http://journals.nwpu.edu.cn/jxkxyjs/CN/abstract/abstract6694.shtml