独立驱动电动汽车质心侧偏角融合估计方法

针对传统质心侧偏角估计精度低、实时性差等问题，把四轮独立驱动电动汽车作为研究对象，提出一种基于强跟踪卡尔曼滤波及无迹卡尔曼滤波融合估计的质心侧偏角估计方法。由于汽车在侧向加速度较小时车辆动力学特性基本呈线性变化，此时通过强跟踪卡尔曼滤波快速估计，当汽车侧向加速度较大时车辆动力学特性趋于非线性变化，通过无迹卡尔曼滤波准确估计。最后将两种估计方法的数据融合，完成不同车速不同工况下对车辆质心侧偏角的估计。搭建Simulink-Carsim联合仿真平台对提出的方法进行验证，结果表明该方法在保证估计精度的同时具有较好的实时跟踪效果及鲁棒性。

Sideslip Angle Fusion Estimation Method of Independent Drive Electric Vehicle

Aiming at the problems of poor accuracy and poor real-time estimation of traditional sideslip angle, a new sideslip angle estimation method based on strong tracking Kalman filter and unscented Kalman filter fusion estimation is proposed for four-wheel independent drive electric vehicle. Since the vehicle dynamics characteristics are roughly linear when the lateral acceleration is small, the strong tracking Kalman filter is used to quickly estimate the sideslip angle at this time. When the lateral acceleration of the vehicle is larger, the vehicle dynamics characteristics tend to be nonlinear, the sideslip angle can be accurately estimated by unscented Kalman filter. Finally, the data of the two estimation methods are fused to realize the estimation of the sideslip angle under different vehicle speeds and different working conditions. The Simulink-Carsim joint simulation platform is built to verify the proposed method. The results show that the method has good real-time tracking effect and robustness while ensuring the estimation accuracy.