基于自适应无迹卡尔曼滤波的分布式驱动电动汽车车辆状态参数估计

以精确估计车辆状态参数为目标，提出了一种基于自适应无迹卡尔曼滤波的车辆状态参数估计算法，采用非线性三自由度车辆模型，将模糊控制与无迹卡尔曼滤波算法相结合，实现对系统测量噪声的自适应调整，通过对方向盘转角，纵向加速度和横向加速度等低成本传感器信息融合实现对质心侧偏角和横摆角速度的状态估计.应用CarSim与Matlab/Simulink建立分布式驱动电动汽车整车模型并且联合仿真对估计算法的有效性进行验证.结果表明自适应无迹卡尔曼滤波比无迹卡尔曼滤波更能有效准确地进行车辆状态参数估计，在双移线工况中，质心侧偏角估计精度提高了6.7%，横摆角速度估计精度提高了4.8%. 

State Parameter Estimation of Distributed Drive Electric Vehicle Based on Adaptive Unscented Kalman Filter

A vehicle state parameter estimation algorithm based on adaptive unscented Kalman filter (AUKF) was proposed to estimate vehicle state parameters accurately. Taking a nonlinear three freedom vehicle model as object, the fuzzy control algorithm and the unscented Kalman filter algorithm were combined to realize the adaptive adjustment of the system measurement noise. The sensor information about steering wheel angle, longitudinal acceleration and lateral acceleration were synthesized to realize the estimation of side slip angle and yaw rate. CarSim and Matlab/Simulink were used to establish the distributed driving electric vehicle model and the effectiveness of the algorithm was verified by simulation. The results show that the adaptive unscented Kalman filter is more effective and accurate than the unscented Kalman filter to estimate the parameters of the vehicle. In the double lane conditions, side slip angle estimation accuracy is improved by 6.7%, and the yaw rate estimation accuracy is improved 4.8%.