基于轮胎分段仿射辨识模型的车辆行驶状态估计策略研究

Research on Estimation Strategy of Vehicle Driving State Based on Tire Piecewise Affine Identification Model

以车辆横向运动过程中的行驶状态精确估计为目标,提出了一种考虑轮胎非线性侧偏力学特性的行驶状态估计算法。为准确反映特殊行驶工况下车辆横向动力学行为演化规律,采用分段仿射辨识方法建立了轮胎非线性侧偏力学特性模型,进而实现整车横向动力学分段仿射模型的构建。在此基础上,基于强跟踪平方根容积卡尔曼滤波算法设计了针对系统分段仿射模型的“多模切换”行驶状态估计策略,以期当系统状态发生突变时依然能够保持良好的状态估计精度。基于CarSim和Matlab/Simulink建立了车辆行驶状态估计性能联合仿真验证平台,通过设置两种典型工况,对车辆横摆角速度和质心侧偏角的状态估计效果进行了验证。结果表明,所提出的估计算法能够实现特殊行驶工况下车辆行驶状态的高精度估计。

For accurate state estimation in the process of vehicle lateral motion,an estimation algorithm considering the tire nonlinear cornering mechanical characteristics is proposed.In order to accurately reflect the evolution law of vehicle lateral dynamics under special driving conditions,a vehicle dynamics model considering the tire nonlinear cornering mechanical characteristics is established by using the piecewise affine identification method,and then the piecewise affine model of vehicle lateral dynamics is constructed.On this basis,a"multi-mode switching"state estimation strategy for the piecewise affine model of the system is designed based on the strong tracking square root cubature Kalman filter algorithm to maintain good state estimation accuracy when the system state changes suddenly.Based on CarSim and MATLAB/Simulink,a Co-Simulation platform for vehicle driving state estimation performance is established.By setting up two typical working conditions,the state estimation effect of vehicle yaw rate and sideslip angle is verified.The results show that the proposed estimation algorithm can achieve high-precision estimation of vehicle driving state under special driving conditions.