Lateral Tire Force And State Estimation Of An In-wheeled Motor Driven Electric Vehicle

In-wheeled motor driven vehicle has the advantages of compact structure,short transmission chain,fast response and flexible wheel torque distribution.This type of electric vehicle has become an important development direction of electric vehicles.Torque distribution control is the key technology of the dynamic control of in-wheeled motor driven vehicle,and accurate acquisition of lateral tire force and vehicle state is a necessary prerequisite for torque distribution control.Because tire force and parts of vehicle states are usually not directly measured,or the cost of direct measurement is too expensive,which cannot be widely applied to automotive industry.Because the wheel torques and rotation speeds can be accurately measured without increasing sensor for in-wheeled motor driven vehicle,the information perception range is larger than the traditional vehicle,which provides a larger application space for the tire force and state estimation,and has also become a hot spot in the field of in-wheeled motor driven vehicle.Aiming at the estimation of lateral tire force and state of an in-wheeled motor driven vehicle,the following works are carried out in this paper.First of all,a three degree of freedom vehicle dynamics model of in-wheeled motor driven vehicle is established.Based on CarSim,the accuracy of vehicle dynamics model is verified.By comparing and analyzing the accuracy of lateral tire force calculation model,the necessity of lateral tire force estimation is demonstrated.According to the characteristics that the driving torques are easily measured for in-wheeled motor driven vehicle,the longitudinal tire forces are directly calculated by wheel rotation dynamic model.On this basis,an estimation method of lateral tire forces based on Unscented Kalman filter is proposed.Comparing and analyzing the accuracy of the direct calculation method,the Unscented Kalman filter and Extended Kalman Filter estimation method,it is proved that the proposed method not only has high estimation accuracy,but also has a certain robustness to parameter uncertainty.Finally,the longitudinal vehicle speed and the sideslip angle estimation are studied in this paper.Using the characteristics of the torques and speeds of four wheels can measurement directly in a distributed drive electric vehicle,an adaptive extended Kalman filtering method for vehicle state estimation is proposed.With normalized innovation square,the validity of vehicle state estimation is detected,and an adaptive adjustment rule of sliding window length is designed.An adaptive adjustment strategy of the gain of Kalman filter and the covariance matrix of state estimation error are proposed based on the statistical characteristics of innovation.The determination principle of adaptive parameters based on the steady-state error of vehicle state estimation and the dynamic response speed is determined.The numerical simulation and experiment can prove that the proposed algorithm of vehicle state estimation not only can improve estimation accuracy,but also has advantages of high real-time and easy to implement.