基于Kalman滤波的AMT车辆起步时离合器传递扭矩估计

Clutch transmitting torque estimation during AMT vehicle starting based on Kalman filter

为了实现自动离合器传递扭矩的直接控制，以自动手动变速器（AMT）车辆为研究对象，建立了车辆动力传动系统动力学模型。以发动机转速和离合器传递扭矩为元素构建状态向量，推导离散状态空间模型，设计了基于离散Kalman滤波的离合器传递扭矩估计算法，对车辆起步过程中的离合器扭矩进行了估计，通过与仿真设定值对比，对扭矩估计误差进行了分析。研究了采样周期变化（5～25ms内）对离合器扭矩估计的影响。结果表明，扭矩估计误差随采样周期的增加而增大，在采样周期为10ms时，扭矩估计精度下限为7．5％，所以该算法具有足够的精确性。

A power train dynamics model was built for an automatic manual transmission （AMT） vehicle to realize the transmitting torque direct control of the automatic clutch. The discrete state space model for estimating the clutch transmitting torque was derived. The state vector was constituted by the engine rotation speed and the clutch transmitting torque. The Kalman filtering algorithm was used to estimate the clutch transmitting torque, to estimate clutch transmitting torque for vehicle starting process, and to analyze estimation errors by comparing the estimated and simulated values of the clutch torque. Impact of the sample time （5-25 ms） on clutch torque estimation errors was explored. The results show that the torque estimation error increases with the increase of sampling time; the lower limit of torque estimation accuracy is 7.5% at sampling time 10 ms. Therefore, the algorithm show sufficient accuracy.