基于扩展卡尔曼滤波的车辆质量与道路坡度估计  

Vehicle Mass and Road Grade Estimation Based on Extended Kalman Filter

作　　者：雷雨龙[1] 付尧[1] 刘科[1] 曾华兵[1] 张元侠 

机构地区：[1]吉林大学汽车动态仿真与控制国家重点实验室,长春130022

出　　处：《农业机械学报》2014年第11期9-13,8,共6页Transactions of the Chinese Society for Agricultural Machinery

基　　金：国家高技术研究发展计划(863计划)资助项目(2012AA111712);高等学校博士学科点专项科研基金资助项目(20120061110027);吉林大学'985'工程资助项目;长江学者和创新团队发展计划资助项目(IRT1017)

摘　　要：针对车辆自动变速器控制系统难以实时测得车辆质量与道路坡度参数这一问题,运用最优估计理论,以车辆纵向动力学模型为基础,建立系统的状态空间模型,运用前向欧拉法将过程方程离散化,进一步对非线性过程方程进行近似线性化,获得过程方程向量函数的Jacobian矩阵,实现了基于扩展卡尔曼滤波(EKF)的车辆质量及道路坡度估计算法。在Matlab/Simulink仿真平台下,进行了实车道路试验数据的离线仿真。仿真结果表明,该算法可有效估计车辆质量及道路坡度,能够满足车辆自动变速器控制系统的要求。To solve the problem that vehicle mass and road grade in automatic transmission control system are difficult to measure, the state-space model of system was established based on the vehicle longitudinal dynamic model, using the optimal estimation theory. The forward Euler method was adopted for discretization of the process equation. The Jacobian of the process equation was calculated for linearization of the non-linear process function. Then, the vehicle mass and road grade estimation algorithm using extended kalman filter was developed. The simulation using road test data was carried out in Matlab/Simulink environment. The simulation results show that this algorithm could estimate vehicle mass and road grade effectively and meet the requirements of the automatic transmission control system.

关 键 词：自动变速器最优估计 扩展卡尔曼滤波 车辆质量 道路坡度 

分 类 号：U463.2[机械工程—车辆工程]