基于扩展卡尔曼滤波的车辆质量与道路坡度估计

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。

ehicle Mass and Road Grade Estimation Based on Extended Kalman Filter

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.