自适应软测量算法的汽车行驶状态估计

为了实现车辆行驶状态低成本测量，设计了估计汽车行驶状态参数的传统无迹卡尔曼滤波器和能够有效解决噪声时变特性的次优Sage-Husa噪声估计器相结合算法，通过建立电动汽车3自由度的动力学模型和HSRI轮胎模型，且融合低成本测量的纵、横向加速度和方向盘转向角传感器测量信息，从而可精确估计电动汽车行驶状态.在选定的典型工况下，通过与无迹卡尔曼软测量算法进行对比，硬件在环实验结果有效地验证了自适应无迹卡尔曼软测量算法具有很好的鲁棒性，且比无迹卡尔曼软测量算法更加能够有效地估计电动汽车的行驶状态.

Vehicle Driving State Estimation of the Adaptive Soft-Sensing Algorithm

The low-cost measurement of vehicle driving states is realized by establishing an algorithm based on the traditional unscented Kalman filter(UKF) which can estimate vehicle driving state parameters and the sub-optimal Sage-Husa noise estimator which can effectively solve the problem of noises varying with time. Meanwhile three-degree-of-freedom(3-DOF) dynamic model of electrical vehicles and highway safety research institute(HSRI) tire model are established. Accordingly， electrical vehicle driving states can be accurately estimated by fusing the low-cost measurement information of longitudinal and lateral acceleration and handwheel steering angles. Under the selected typical working condition， the adaptive unscented Kalman filter(AUKF) soft-sensing algorithm is compared with the UKF soft-sensing algorithm， and the hardware-in-the-loop(HIL) testing platform result indicates the AUKF soft-sensing algorithm has a good performance in robustness and is able to realize the effective estimation of electrical vehicles’ driving state more precisely than the UKF soft-sensing algorithm.