基于扩展卡尔曼滤波的分布式电驱动汽车状态参数估计

State Parameter Estimation of Electric Drive VehicleBased on Kalman Filter

为了实时准确的获取汽车主动安全控制的状态量,基于汽车三自由度动力学模型和扩展卡尔曼滤波理论对质心侧偏角和横摆角速度进行估计。利用分布式驱动电动汽车提供的标准车载信息:轮速、侧向加速度、横摆角速度、前轮转角等测量,结合扩展卡尔曼滤波估计算法来实时估计质心侧偏角和消除横摆角速度误差,在MATLAB环境中开发分布式轮边四驱电动汽车,并建立Simulink-CarSim联合仿真平台,选取高速和低速工况,仿真验证了扩展卡尔曼滤波在车辆动力学系统状态估计中的可行性与适应性。

To obtain real-time and accurate state quantities for active safety control of the vehicle,the center-of-mass lateral eccentricity and transverse swing angular velocity are estimated based on a three-degree-of-freedom dynamics model of the vehicle and Extended Kalman Filter theory.Using the standard on-board information provided by the distributed drive electric vehicle:wheel speed,lateral acceleration,transverse swing angular velocity,front-wheel rotation angle,and other measured quantities,combined with the Extended Kalman Filter estimation algorithm to estimate the center-of-mass lateral deflection angle and eliminate the transverse swing angular velocity error in real-time,the distributed wheel side 4WD electric vehicle is developed in MATLAB environment,and a joint Simulink-CarSim simulation platform is established to select The simulation verifies the feasibility and adaptability of the extended Kalman filter in the state estimation of vehicle dynamics system.