中文摘要：

四驱混合动力轿车存在两轮/四轮多种驱动模式，针对某一驱动模式所设计的车速估计算法难以满足其他模式下车速估计的精度。考虑车辆不同驱动模式，利用车载传感器信号、后轮毂电机转矩信息以及既定档位下前轮转矩信息，提出两级分布式卡尔曼车速估计方法。考虑模型的强非线性，采用无味卡尔曼滤波（Unscented Kalman filter，UKF）算法设计主/子滤波器。同时为了提高UKF算法对模型误差、信号干扰的鲁棒性，实现了量测噪声均值和方差的自适应调节。基于Untire轮胎模型和滑移率递推方程，设计滑移率子滤波器；基于整车运动学模型，设计车速主滤波器；考虑不同驱动模式，利用子滤波器估计值在线融合得到车速。搭建 Carsim-Simulink 联合仿真平台，并分别在纯电动驱动模式和四轮混合驱动模式下，对车速估计算法进行仿真验证。结果表明，所提出的两级分布式卡尔曼车速估计方法能有效提高车速估计精度，并增强了对模型误差、量测信号干扰和不同驱动模式的鲁棒性。

英文摘要：

The driving mode of hybrid electric car can be two-wheel or four-wheel drive. The speed estimation designed for a specific driving mode can hardly achieve high estimated accuracy in all modes. Focus on the changeable driving mode, a kind of distributed Kalman filter with two levels is proposed, by means of utilize vehicle sensors signals, the driving torque of rear in-wheel motors and the torque of front driving wheels at the given gear state. Considering the strongly nonlinear of the model, UKF is adopted to design the filter, and to improve the filter’s robustness on modeling error and signal noise, measurement noise mean and covariance is self-adaptive. Firstly, based on UniTire Model and recursive equation of slip rate, the sub-filter is developed; secondly, based on the vehicle Kinematics model, the master-filter can be developed; at last, in consideration of different driving modes, a more accurate velocity estimation is obtained by fusing information from each sub-filters. With the help of Carsim and Simulink, the simulation platform about four wheel drive hybrid electric car is developed, and the proposed speed estimation algorithm is tested on this platform under the pure electric drive mode and the hybrid drive mode. The results show that the proposed algorithm has not only high precision, but also strong robustness on modeling error, measurement noise and the changeable driving mode.