The invention discloses a vehicle state estimation method based on improved Kalman filtering, and the method comprises the steps: building a longitudinal, transverse and yaw nonlinear three-degree-of-freedom dynamic model and a tire model according to the vehicle dynamic characteristics of an electrically driven bus; according to the Sage-Husa self-adaptive filtering principle, a measurement noise covariance matrix R which is regarded as a fixed value in a traditional Kalman filtering algorithm is dynamically adjusted and corrected in real time, and a Sage-Husa coefficient is optimized according to the filtering divergence degree; on the basis of a nonlinear three-degree-of-freedom kinetic model, a self-adaptive extended Kalman filtering algorithm based on a Sage-Husa filtering principle is constructed, and vehicle driving state estimation in a noise environment is realized; through Simulink and TrickSim joint simulation, the estimation precision of the running state parameters of the electrically driven bus in the noise environment, such as the yaw velocity, the side slip angle and the longitudinal velocity, is finally improved. According to the method, the influence of sensor noise on a result is effectively reduced by improving extended Kalman filtering, and accurate estimation of the state of the electrically-driven bus is realized.
本发明公开了一种基于改进卡尔曼滤波的汽车状态估计方法,根据电驱动公交车的车辆动力学特性,构建纵向、横向、横摆的非线性三自由度动力学模型及轮胎模型;通过Sage‑Husa自适应滤波原理,对传统卡尔曼滤波算法中视作固定值的量测噪声协方差阵R进行实时动态调整修正,并依据滤波发散程度优化Sage‑Husa系数;基于非线性三自由度动力学模型,构建基于Sage‑Husa滤波原理自适应的扩展卡尔曼滤波算法,实现噪声环境下的车辆行驶状态估计;通过Simulink与TruckSim联合仿真,最终提升噪声环境下对电驱动公交车行驶状态参数如:横摆角速度、质心侧偏角与纵向速度的估计精度。本发明通过改进扩展卡尔曼滤波有效降低传感器噪声对结果影响,实现对电驱动公交汽车状态的精准估计。
Automobile state estimation method based on improved Kalman filtering
一种基于改进卡尔曼滤波的汽车状态估计方法
2024-07-16
Patent
Electronic Resource
Chinese
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