基于CKF的四轮驱动电动汽车质心侧偏角估计

Estimation of Sideslip Angle of Four Wheel Drive Electric Vehicle Based on Dual CKF

针对传统质心侧偏角观测精度不高、实时性能差的问题,提出了一种基于CKF算法的质心侧偏角观测器。首先基于CKF算法结合Dugoff轮胎模型,实时观测路面附着系数,计算实时的轮胎力。再结合七自由度整车模型,基于CKF算法,实时、精确观测车辆的质心侧偏角。最后,利用Simulink/Carsim联合仿真验证平台和硬件在环平台进行仿真验证。试验结果表明,CKF算法相比与传统无迹卡尔曼滤波估计精度更高、实时性能更好,较好的改善了传统质心侧偏角观测器在非线性条件下的观测精度。

Aiming at the problems of low accuracy and poor real-time performance of traditional sideslip angle observation,a sideslip angle observer based on a CKF algorithm is proposed.Firstly,based on the CKF algorithm and Dugoff tire model,the road adhesion coefficient is observed in real-time to calculate the real-time tire force.Combined with the seven degrees of freedom vehicle model and based on the CKF algorithm,the sideslip angle of the vehicle can be observed in real-time and accurately.Finally,the Simulink/CarSim joint simulation verification platform and hardware in the loop platform are used for simulation verification.The experimental results show that compared with the traditional unscented Kalman filter,the estimation algorithm based on dual volume Kalman Filter has higher estimation accuracy and better real-time performance,and better improves the observation accuracy of the traditional sideslip angle observer under nonlinear conditions.