针对四轮独立驱动电动汽车无从动轮及转矩独立可控等特点,建立了7自由度车辆模型和非线性魔术轮胎模型,采用含加性噪声的无迹卡尔曼滤波(UKF)算法对车辆状态参数进行离线仿真估计,并基于实车平台对所设计的估计算法进行巡航转向工况和蛇形工况道路试验,以GPS测量的纵向车速作为基准值,以陀螺仪等传感器测量的其他状态参数作为测量值,以此来验证算法的有效性.仿真和实车试验结果均表明,所设计的UKF算法估计精度较高,且具有较强的工况适应性.%According to the feature that the four wheels independent drive electric vehicle has no driven wheel and its torque can be controlled independently, the seven degrees of freedom vehicle model and nonlinear magic formula tyre model are established.The offline simulation estimates of vehicle state parameters are carried out with the help of the additive noise UKF (Unscented Kalman Filter) algorithm? Based on the estimation algorithms designed by experimental platform, the longitudinal velocity measured by GPS is used as the reference value and other state parameters measured by gyro and other sensors are used as the measurement values, and road tests on cruise steering and S-shaped route driving case are performed to validate the effectiveness of UKF algorithm.Simulation and road test results show that UKF algorithm has high estimation precision and strong adaptability.
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