Li Wenfei, Li Huiyun, Xu Kun, Huang Zhejun, Li Ke, Du Haiping
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Shenzhen 518055, China.
Sensors (Basel). 2021 May 26;21(11):3711. doi: 10.3390/s21113711.
Vehicle dynamic parameters are of vital importance to establish feasible vehicle models which are used to provide active controls and automated driving control. However, most vehicle dynamics parameters are difficult to obtain directly. In this paper, a new method, which requires only conventional sensors, is proposed to estimate vehicle dynamic parameters. The influence of vehicle dynamic parameters on vehicle dynamics often involves coupling. To solve the problem of coupling, a two-stage estimation method, consisting of multiple-models and the Unscented Kalman Filter, is proposed in this paper. During the first stage, the longitudinal vehicle dynamics model is used. Through vehicle acceleration/deceleration, this model can be used to estimate the distance between the vehicle centroid and vehicle front, the height of vehicle centroid and tire longitudinal stiffness. The estimated parameter can be used in the second stage. During the second stage, a single-track with roll dynamics vehicle model is adopted. By making vehicle continuous steering, this vehicle model can be used to estimate tire cornering stiffness, the vehicle moment of inertia around the yaw axis and the moment of inertia around the longitudinal axis. The simulation results show that the proposed method is effective and vehicle dynamic parameters can be well estimated.
车辆动力学参数对于建立可行的车辆模型至关重要，这些模型用于提供主动控制和自动驾驶控制。然而，大多数车辆动力学参数难以直接获取。本文提出了一种仅需传统传感器的新方法来估计车辆动力学参数。车辆动力学参数对车辆动力学的影响通常涉及耦合。为解决耦合问题，本文提出了一种由多模型和无迹卡尔曼滤波器组成的两阶段估计方法。在第一阶段，使用纵向车辆动力学模型。通过车辆加速/减速，该模型可用于估计车辆质心与车辆前端之间的距离、车辆质心高度和轮胎纵向刚度。估计出的参数可用于第二阶段。在第二阶段，采用具有侧倾动力学的单轨车辆模型。通过使车辆持续转向，该车辆模型可用于估计轮胎侧偏刚度、车辆绕偏航轴的转动惯量和绕纵向轴的转动惯量。仿真结果表明，所提出的方法是有效的，并且车辆动力学参数能够得到很好的估计。
