The invention discloses a vehicle running state estimation method. The method comprises the following steps: acquiring vehicle running data through a sensor; establishing a vehicle three-degree-of-freedom dynamic model comprising a system equation and an observation equation by using the vehicle operation data and the vehicle static parameters; based on the vehicle three-degree-of-freedom dynamics model and vehicle operation data obtained by a sensor, the driving state of the vehicle is estimated through a method combining extended Kalman filtering and a neural network, and the part, for calculating Kalman gain, in the extended Kalman filtering is obtained through calculation of the neural network. According to the method, the extended Kalman filtering and neural network combined method is adopted to estimate the driving state of the vehicle, so that better nonlinear processing capability and more robust effects are obtained, and the nonlinear processing capability, robustness and adaptability of the extended Kalman filtering when the extended Kalman filtering is used for estimating the driving state of the vehicle are enhanced; therefore, the method can better adapt to system dynamics and measurement noise which change in real time.
本发明公开了一种车辆行驶状态估计方法,包括:通过传感器获取车辆运行数据;使用所述车辆运行数据和车辆静态参数,建立包括系统方程和观测方程的车辆三自由度动力学模型;基于所述车辆三自由度动力学模型和传感器获取的车辆运行数据,通过扩展卡尔曼滤波与神经网络相结合的方法进行车辆的行驶状态估计,其中,所述扩展卡尔曼滤波中计算卡尔曼增益的部分通过所述神经网络计算得到。本发明采用了扩展卡尔曼滤波与神经网络结合的方法进行车辆的行驶状态估计,获得了更好的非线性处理能力和更鲁棒的效果,增强了扩展卡尔曼滤波用于汽车行驶状态估计时的非线性处理能力和鲁棒性以及适应性,使其更好地适应实时变化的系统动态和测量噪声。
Vehicle driving state estimation method
一种车辆行驶状态估计方法
19.03.2024
Patent
Elektronische Ressource
Chinesisch
IPC: | B60W CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION , Gemeinsame Steuerung oder Regelung von Fahrzeug-Unteraggregaten verschiedenen Typs oder verschiedener Funktion |
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