Vehicle state estimation of steer by wire system based on multi sensor fusion

Aiming at automobile steer-by-wire system, a vehicle state estimation algorithm based on federal Kalman filter is designed and proposed in this paper to improve its handing stability. Firstly, nonlinear seven degrees of freedom vehicle model and the Dugoff tire model is established, and a multi-sensor network (angle sensor, holzer sensor, accelerometer) is built in order to acquire the vehicle running state. Meanwhile, the system equation of the algorithm is obtained. Secondly, according to the time-varying and nonlinear characteristics of the system, the acceleration integral, wheel torque and wheel speed correction filter is respectively established, then the steering wheel angle is introduced into the main filter to reduce the deviation in the turning condition, to get the optimal state estimation of vehicle based on multi-sensor is initially completed. Finally, a simulated experiment platform is built to run an experiment about steering angle sine input and double lane change. The results show that the proposed algorithm can estimate the state and parameter estimation of vehicle driving.