Online estimation of inertial parameter for lightweight electric vehicle using dual unscented Kalman filter approach
Online estimation of inertial parameter for lightweight electric vehicle using dual unscented Kalman filter approach
复制标题
使用双无迹卡尔曼滤波器方法在线估计轻型电动汽车惯性参数
DOI：
10.1049/iet-its.2019.0458
复制
发表时间：
2020-05-01
影响因子：
2.7
通讯作者：
Yin, Guodong
中科院分区：
文献类型：
--
作者：
Jin, Xianjian;Yang, Junpeng;Yin, Guodong
准确了解车辆惯性参数（例如车辆质量和横摆惯性矩）对于管理车辆潜在轨迹和提高车辆主动安全性至关重要。对于轻量化电动汽车（LEVs），由于整车重量和车身尺寸的急剧减小，其动力学系统的控制性能会受到很大影响，因此了解这些知识就显得尤为重要。本研究提出一种双无迹卡尔曼滤波器（DUKF）的方法，其中两个UKFs并行运行，同时估计车辆的状态和参数，如车辆速度，车辆侧滑角和惯性参数。所提出的方法仅利用标准汽车中轮内电机和传感器扭矩信息的实时测量结果。建立了考虑载荷变化的四轮非线性车辆动力学模型，利用微分几何理论分析和推导了DUKF观测器的局部可观测性。为了解决车辆动力学中的非线性，DUKF和双扩展卡尔曼滤波器（DEKF）也提出和比较。使用MATLAB/Simulink-Carsim（R）平台进行各种机动的仿真。MATLAB/Simulink-Carsim（R）仿真结果表明，所提出的DUKF方法能有效地估计不同载荷下的轻型车辆惯性参数。此外，研究表明，所提出的DUKF方法具有更好的性能，估计车辆的惯性参数相比，DEKF方法。
Accurate knowledge of vehicle inertial parameters (e.g. vehicle mass and yaw moment of inertia) is essential to manage vehicle potential trajectories and improve vehicle active safety. For lightweight electric vehicles (LEVs), whose control performance of dynamics system can be substantially affected due to the drastic reduction of vehicle weights and body size, such knowledge is even more critical. This study proposes a dual unscented Kalman filter (DUKF) approach, where two UKFs run in parallel to simultaneously estimate vehicle states and parameters such as vehicle velocity, vehicle sideslip angle, and inertial parameters. The proposed method only utilises real-time measurements from torque information of in-wheel motor and sensors in a standard car. The four-wheel non-linear vehicle dynamics model considering payload variations is developed, local observability of the DUKF observer is analysed and derived via differential geometry theory. To address the non-linearities in vehicle dynamics, the DUKF and dual extended Kalman filter (DEKF) are also presented and compared. Simulations with various manoeuvres are carried out using the platform of MATLAB/Simulink-Carsim(R). Simulation results of MATLAB/Simulink-Carsim(R) show that the proposed DUKF method can effectively estimate inertial parameters of LEV under different payloads. Moreover, the investigation reveals that the proposed DUKF approach has better performance of estimating vehicle inertial parameters compared with the DEKF method.