基于模糊扩展卡尔曼滤波的轮毂电机驱动车辆纵向速度估计算法

为了获取轮毂电机驱动车辆的纵向速度,设计了基于轮速信号和车身加速度信号的扩展卡尔曼滤波的估计算法,建立了研究对象的离散状态方程和测量方程,并采取不同的扩展卡尔曼滤波器对测量信号进行滤波去噪处理和车辆纵向速度估算.通过模糊控制器,对车速估计滤波器的估算参数进行实时动态调节,实现了估计算法的自适应性.研究结果表明,在路面附着系数为1.00的良好路面上,估算得到的车速和动力学模型输出的实际车速误差小于2%;在路面附着系数为0.25的路面上,最大估算误差小于10%. 

Estimation of Longitudinal Speed of In-wheel Motor Driven Vehicle Using Fuzzy Extended Kalman Filter

In order to obtain the longitudinal speed of the in-wheel motor driven vehicle, a new estimation algorithm for the extended Kalman filter was designed based on signals of wheel speed and vehicle body acceleration. First, the discrete state equation and measurement equation of the research object were established. Then, two extended Kalman filters (EKFs), including a noise filter and an estimation filer, were designed to deal with measuring signals and estimate the vehicle's longitudinal speed, respectively. Finally, the parameters obtained by the estimation filer were adjusted through the fuzzy controller to ensure the adaptivity of the algorithm. The simulation results show that the error between the estimated speed and the actual speed was less than 2% when the road adhesion coefficient was 1.00, and the error was less than 10% when the road adhesion coefficient was 0.25.