基于CKF的四轮驱动电动汽车质心侧偏角估计
Estimation of Sideslip Angle of Four Wheel Drive Electric Vehicle Based on Dual CKF
苏忆 1徐律 1李捷辉2
作者信息
- 1. 无锡商业职业技术学院,江苏 无锡 214153
- 2. 江苏大学,江苏 镇江 212013
- 折叠
摘要
针对传统质心侧偏角观测精度不高、实时性能差的问题,提出了一种基于CKF算法的质心侧偏角观测器.首先基于CKF算法结合Dugoff轮胎模型,实时观测路面附着系数,计算实时的轮胎力.再结合七自由度整车模型,基于CKF算法,实时、精确观测车辆的质心侧偏角.最后,利用Simulink/Carsim联合仿真验证平台和硬件在环平台进行仿真验证.试验结果表明,CKF算法相比与传统无迹卡尔曼滤波估计精度更高、实时性能更好,较好的改善了传统质心侧偏角观测器在非线性条件下的观测精度.
Abstract
Aiming at the problems of low accuracy and poor real-time performance of traditional sideslip angle observation,a sideslip angle observer based on a CKF algorithm is proposed.Firstly,based on the CKF algorithm and Dugoff tire model,the road adhesion coefficient is observed in real-time to calculate the real-time tire force.Combined with the seven degrees of freedom vehicle model and based on the CKF algorithm,the sideslip angle of the vehicle can be observed in real-time and accurately.Fi-nally,the Simulink/CarSim joint simulation verification platform and hardware in the loop platform are used for simulation veri-fication.The experimental results show that compared with the traditional unscented Kalman filter,the estimation algorithm based on dual volume Kalman Filter has higher estimation accuracy and better real-time performance,and better improves the observation accuracy of the traditional sideslip angle observer under nonlinear conditions.
关键词
容积卡尔曼滤波/路面附着系数/质心侧偏角/四轮驱动电动汽车Key words
Cubature Kalman Filter/Adhesion Coefficient of Pavement/Sideslip Angle/Four Wheel Drive Elec-tric Vehicle引用本文复制引用
基金项目
江苏省第六期"333高层次人才培养工程"第三层次培养对象(20223-16-816)
江苏省高等学校基础科学(自然科学)研究面上项目(22KJD460008)
无锡商业职业技术学院校级课题(KJZX21603)
出版年
2024
NETL
NSTL
万方数据