货车整车质量辨识方法研究

针对货车整车质量辨识问题，分别对基于车辆纵向动力学和基于运动学的两种整车质量辨别方法进行仿真和实车试验研究。阐述了两种整车质量辨识方法的原理，在TruckSim软件平台上建立车辆模型并在多种工况下基于这两种方法进行仿真分析。根据仿真结果进行实车试验，提出了一种车速冗余计算和加速度计算的方法，利用带时变遗忘因子的递推最小二乘法（RLS）对实车质量进行估计。研究结果表明，所提出的车速冗余计算方法和加速度计算方法准确有效，同时减少换挡次数能够显著提高辨识算法的收敛速度。相比于运动学整车质量辨识算法，基于车辆纵向动力学整车质量辨识算法能够有效地估计货车整车质量，鲁棒性好，估计准确，收敛速度快，具有很好的工程应用价值。

Research on the method of mass estimation for heavy vehicles

To solve the vehicle mass estimation problems, two vehicle mass estimation methods based on both commonly used dynamics method and kinematic method are analyzed by simulation and experimental study. The principles of two vehicle mass estimation methods are described. Then a model is built on the TruckSim software platform and the simulation analysis is carried out taking into account these two methods in multi conditions. Some vehicle experiments are carried out based on the simulation results. A method of vehicle speed redundancy calculation and acceleration calculation are proposed, and the real time vehicle mass is estimated by using the recursive least squares (RLS) with time varying forgetting factor. The research results demonstrate that the speed redundancy calculation method and acceleration calculation method are effective and accurate. And the convergence speed of the algorithm can be significantly improved by reducing shifts. It is concluded that, compared with the kinematic estimation algorithm, the proposed longitudinal dynamic estimation algorithm can estimate vehicle mass effectively with better robustness, more accuracy and faster convergence, and its engineering application is valuable.