分布式驱动电动汽车双无迹卡尔曼滤波状态参数联合观测

为实时观测分布式驱动电动汽车行驶过程中的车身质心侧偏角等状态及车辆惯性参数如车辆质量、横摆转动惯量等信息，针对车辆惯性参数估计易敏感于载荷参数变化的挑战，发展面向载荷参数不确定（乘客或货物加载）的纵向、侧向、横摆运动的四轮车辆非线性动力学系统估计模型，在融合轮毂转矩等车载多传感器信息的基础上，将能适应强非线性系统的无迹卡尔曼滤波引入到车辆惯性参数估计中，设计车辆并联双无迹卡尔曼滤波状态参数联合观测系统，其中一个无迹卡尔曼滤波器观测车辆速度、车身质心侧偏角等状态，而另一个无迹卡尔曼滤波器观测车辆惯性参数。在CarSim/Matlab高保真环境中使用双移线、正弦工况对观测器在不同的载荷加载条件的可行性和有效性进行仿真验证，结果表明：该观测系统能实时观测车辆运行的状态及车辆惯性参数，即使在重载荷加载条件下仍具有较高的观测精度。

Combined State and Parameter Observation of Distributed Drive Electric Vehicle via Dual Unscented Kalman Filter

In order to estimate vehicle different states such as vehicle sideslip angle and vehicle inertia parameters such as vehicle mass and yaw moment of inertia in distributed drive electric vehicle, the uncertain load parameters (passenger or cargo loaded)-based four-wheeled vehicle nonlinear dynamics estimation model including longitudinal, lateral, and yaw motions is developed to deal with sensitive challenges of vehicle inertia parameters estimation due to load parameter changes. Based on vehicle multi-sensor data fusion from the hub torque and other measurements, the unscented Kalman filter that can adapt to the strong nonlinear system is introduced, and then the nonlinear observer with the dual unscented Kalman filter is designed, where the first unscented Kalman filter observes vehicle different states and the other parallel unscented Kalman filter is used to observe the vehicle inertia parameters. Simulations for double lane change and sine manoeuvres are carried out to evaluate the feasibility and effectiveness of observer under different load parameters in CarSim/Matlab environment. The results show that the proposed observation system can observe vehicle states and inertia parameters in real time, and it has high observation accuracy even under large load condition.