摘要
为提高分布式电动汽车的控制性能,提出一种更加综合、全面的车辆状态及参数估计方法。针对车辆在行驶过程中某些动力学状态及参数难以实时量测的问题,以分布式电驱动汽车为研究对象,探讨基于无迹卡尔曼滤波的车辆状态估计及参数识别方法。建立7自由度时变参数车辆模型;以车辆易于测量的纵向加速度、侧向加速度、横摆角速度和轮速为观测变量,通过状态扩维,将车辆相关参数引入到车辆状态矢量中,采用无迹卡尔曼滤波算法设计一种车辆状态和参数的联合观测器,以便同时估计和辨识车辆纵向速度、侧向速度、轮胎侧向力、车辆质量、转动惯量、质心位置及其高度;在Simulink/Carsim平台上进行鱼钩角输入、滑行和加速工况的仿真验证,结果表明,该联合观测器能够有效的估计和辨识出上述相关车辆状态和参数,收敛效果较好。
In order to improve the control performance of distributed drive electric vehicle(EV)a more comprehensive vehicle state and parameter estimation method is proposed.For the problem that it is difficult to measure the vehicle states and parameters in real time,the distributed EV is considered as the object,and a method of vehicle states and parameters estimation based on unscented Kalman filter(UKF)is discussed.Firstly,the 7-DOF time-varying parameter vehicle model is established;secondly,taking the longitudinal acceleration,lateral acceleration,yaw angular velocity and wheel speed which are easy to be measured as the measured variables,a combined observer of vehicle states and system parameters is designed using the UKF algorithm to estimate longitudinal velocity,lateral velocity,lateral tire forces,vehicle mass,center position and inertia moment by extending the dimension of state variables.Finally,the combined observer is simulated and analyzed on Simulink/Carsim platform.The results show that the observer can estimate vehicle state and system parameters effectively,and has a good convergence effect.
作者
宋义彤
舒红宇
陈仙宝
靖长青
郭成
SONG Yitong;SHU Hongyu;CHEN Xianbao;JING Changqing;GUO Cheng(State Key Laboratory of Mechanical Transmissions,Chongqing University,Chongqing 400044;School of Automotive Engineering,Chongqing University,Chongqing 400044)
出处
《机械工程学报》
EI
CAS
CSCD
北大核心
2020年第16期204-213,共10页
Journal of Mechanical Engineering
基金
国家自然科学基金(51975069)
重庆市自然科学基金(cstc2018jcyj AX0077)
重庆市研究生科研创新(CYB18059)资助项目。
