车辆行驶状态无迹卡尔曼软测量  

Vehicle Driving State Unscented Kalman Filter Soft Computing

作　　者：郝亮[1,2] HAO Liang(Automobile&Traffic Engineering College,Liaoning University of Technology,Jinzhou 121001,China;School of Mechanical Engineering&Automation,Northeastern University,Shenyang 110819,China)

机构地区：[1]辽宁工业大学汽车与交通工程学院 [2]东北大学机械工程及自动化学院

出　　处：《科学技术与工程》2019年第34期382-386,共5页Science Technology and Engineering

基　　金：国家自然科学基金(50875041);辽宁省教育厅重大科技平台科技项目(JP2016011)资助

摘　　要：为了有效解决电动汽车在行驶过程中的车速、质心侧偏角和横摆角速度等参数低成本测量的问题,采用无迹卡尔曼搭建了软测量算法,该算法充分考虑了电动汽车非线性的车辆运动状态估计,对于轮胎侧向力的估计采用非线性的魔术轮胎模型。最终,通过联合仿真实验方法验证了所建立的软测量算法能够准确、实时地估计出电动汽车的运动参数。Unscented Kalman filter (UKF) soft computing algorithm was adopt to solve low-cost measurement of electric vehicle dring states parameters including velocity, centroid slip angle and yaw rate and it taked a full account of electric vehicle nonlinear driving state estimation.Nonlinear Dugoff dynamic tire model was used to accurately estimate tire lateral forces. Finally co-simulation was carried out. The results show that soft computing algorithm can accurately and real-time estimate the dynamic vehicle parameters.

关 键 词：电动汽车 低成本测量 无迹卡尔曼 软测量算法 魔术轮胎模型 联合仿真 

分 类 号：U463.42[机械工程—车辆工程] U469.72[交通运输工程—载运工具运用工程]