摘要
为了能够实时准确的获得当前车轮的轮胎力及路面附着系数以提高汽车主动安全性能,提出一种轮边驱动电动汽车状态估计与路面附着系数估计相结合的估计方法。根据车载传感器及七自由度非线性车辆动力学模型,采用扩展卡尔曼滤波算法(EKF)进行车辆状态及轮胎力的估计。结合EKF估算结果和轮胎模型,采用递归最小二乘法(RLS)实时估计不同路面的附着系数。仿真结果表明:该方法可以在较为复杂工况下估计出不同的路面附着系数,估计精度较高,实时性较好。
A kind of estimation method combined the wheel drive electric vehicle state estimation and tire-road friction coefficient estimation is developed to obtain current tire force of the wheel and to improve vehicle active safety performance accurately. According to the on-board sensors and 7-DOF non-linear vehicle dynamic model, the estimator of the state of vehicle and tire force based on Extended Kalman Filter（EKF） is designed. Combined with EKF estimation results and tire model, Recursive Least Squares（RLS） for real-time estimation of different road adhesion coefficient is designed. The simulation results show that the estimation method can estimate the different road adhesion coefficient under complex conditions and has highly precision and real-time performance.
作者
刘万里
彭冲
韩家伟
Liu Wanli 1, Peng Chong1 , Han Jiawei2(1.Chongqing Vehicle Test ＆ Research Institute, National Coach Quality Inspection and Test Center, Chongqing 401122; 2.State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400030)
出处
《汽车实用技术》
2018年第10期35-38,41,共5页
Automobile Applied Technology
基金
重庆市质量技术监督局科研计划项目(CQZJKY2014012)
