控制理论(社会学)
卡西姆
卡尔曼滤波器
工程类
车辆动力学
扩展卡尔曼滤波器
惯性参考系
可观测性
惯性测量装置
计算机科学
汽车工程
数学
航空航天工程
物理
控制(管理)
量子力学
人工智能
应用数学
作者
Xianjian Jin,Junpeng Yang,Liwei Xu,Chongfeng Wei,Zhaoran Wang,Guodong Yin
出处
期刊:Chinese journal of mechanical engineering
[Elsevier]
日期:2023-08-17
卷期号:36 (1)
被引量:2
标识
DOI:10.1186/s10033-023-00914-5
摘要
Abstract Distributed drive electric vehicles (DDEVs) possess great advantages in the viewpoint of fuel consumption, environment protection and traffic mobility. Whereas the effects of inertial parameter variation in DDEV control system become much more pronounced due to the drastic reduction of vehicle weights and body size, and inertial parameter has seldom been tackled and systematically estimated. This paper presents a dual central difference Kalman filter (DCDKF) where two Kalman filters run in parallel to simultaneously estimate vehicle different dynamic states and inertial parameters, such as vehicle sideslip angle, vehicle mass, vehicle yaw moment of inertia, the distance from the front axle to centre of gravity. The proposed estimation method only integrates and utilizes real-time measurements of hub torque information and other in-vehicle sensors from standard DDEVs. The four-wheel nonlinear vehicle dynamics estimation model considering payload variations, Pacejka tire model, wheel and motor dynamics model is developed, the observability of the DCDKF observer is analysed and derived via Lie derivative and differential geometry theory. To address system nonlinearities in vehicle dynamics estimation, the DCDKF and dual extended Kalman filter (DEKF) are also investigated and compared. Simulation with various maneuvers are carried out to verify the effectiveness of the proposed method using Matlab/Simulink-Carsim ® . The results show that the proposed DCDKF method can effectively estimate vehicle dynamic states and inertial parameters despite the existence of payload variations and variable driving conditions . This research provides a boot-strapping procedure which can performs optimal estimation to estimate simultaneously vehicle system state and inertial parameter with high accuracy and real-time ability.
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