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 ^® . Simulation results of MATLAB/Simulink-Carsim ^® 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.

中文翻译：

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利用双重无味卡尔曼滤波方法在线估计轻型电动汽车的惯性参数

准确了解车辆惯性参数（例如，车辆质量和偏航惯性矩）对于管理车辆潜在轨迹和提高车辆主动安全性至关重要。对于轻型电动汽车（LEV），其动态系统的控制性能可能会由于汽车重量和车身尺寸的大幅降低而受到实质性的影响，因此这些知识变得尤为重要。这项研究提出了一种双重无味卡尔曼滤波器（DUKF）方法，其中两个UKF并行运行以同时估算车辆状态和参数，例如车辆速度，车辆侧滑角和惯性参数。所提出的方法仅利用来自标准汽车中轮内电动机和传感器的扭矩信息的实时测量。建立了考虑有效载荷变化的四轮非线性车辆动力学模型，通过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问题，还提出并比较了DUKF和双重扩展卡尔曼滤波器（DEKF）。使用MATLAB / Simulink-Carsim平台进行各种演习的仿真 ^® 。的MATLAB / Simulink的Carsim仿真结果 ^®表明，该DUKF方法能有效地估计在不同有效载荷LEV的惯性参数。此外，研究表明，与DEKF方法相比，所提出的DUKF方法具有更好的车辆惯性参数估计性能。