A Distributed locomotive Velocity Estimation Method Based on Cubature Kalman Filtering

The accurate and rapid acquisition of locomotive longitudinal velocity is of great significance for adhesion control. However, the longitudinal velocity of the locomotive can't be directly measured. Therefore, a new kind of distributed locomotive velocity estimation method based on the cubature Kalman filter(CKF) is studied in this paper. In order to avoid large errors in the estimation of wheel slip, a comprehensive locomotive velocity estimation module is designed. Based on a six-axle locomotive dynamics model and a wheel-rail model, the cubature Kalman filter algorithm is used to obtain the velocity of each bogie. Then combined with the wheel slip signal of the idle-recognition module, the locomotive running velocity is estimated by the locomotive velocity comprehensive estimation module. Compared with the simulation results of EKF algorithm, the results show that the method is more robust and the estimation accuracy is higher.