State And Parameters Estimation Of Four Wheel Independent Drive And Steering Electric Vehicle

Vehicle driving state estimation has become the hotspot in the research of vehicle dynamics control.The parameters such as road adhesion coefficient,automobile quality and centroid position have important influence on estimation accuracy.The four-wheel drive or brake torque,wheel angle and wheel speed of the four wheel independent drive and steering electrical vehicle can be controlled independently and easy to measure,which is the important development direction of the next generation of electric vehicles.Therefore,according to the characteristics of the four wheel independent driving and steering electric vehicle dynamic control and the advantages of multiple information sources,considering the influence of vehicle parameter uncertainty,the vehicle state and parameters estimation method for four wheel independent drive and steering electrical vehicle is studied to achieve accurate estimation of parameters such as vehicle driving state,road adhesion coefficient and vehicle parameter,provide precondition for automobile control.Based on the project supported by the National Natural Science Foundation of China(51675257)“Study on dynamics control for four wheel independent drive and steering electrical vehicle considering driver characteristic” and the open fund project of the State Key Laboratory of automobile simulation and control of Jilin University(20161116)“state and parameter estimation are studied on four wheel independent drive and steering electric vehicle in this paper”.Firstly,the vehicle dynamics model and Dugoff tire model were established for the four wheel independent drive and steering electric vehicle.Low cost sensors,such as longitudinal/ lateral acceleration,yaw rate,steering wheel angle,etc.,are used.Making full use of the advantages of four wheel drive / brake torque,four wheel speed,and four wheel rotation angle,which can be obtained accurately,applied dynamics theory and information fusion technology.The vehicle driving state estimator is designed based on the Cubature Kalman Filter theory,and the longitudinal / lateral vehicle speed and the center of mass side angle are estimated.Secondly,it is considered that the change of the adhesion coefficient of the road surface will have a direct effect on the vehicle driving state estimation.Based on the Double Cubature Kalman Filter theory,the vehicle driving state and the road adhesion coefficient estimator are designed.So that the two estimators are relative to each other,thus the accurate estimation of the vehicle driving state and the road adhesion coefficient is achieved.What's more,considering the uncertainty of vehicle parameters such as mass,moment of inertia and centroid position during vehicle driving,the accuracy of estimation is seriously affected by the uncertainty of operation conditions.The algorithm is combined with state and parameter estimation,based on the theory of Triple Cubature Kalman Filter,the state and parameter estimation algorithm of four wheel independent drive and steering electric vehicle is designed.In the state estimation at the same time the uncertain vehicle parameters are continuously corrected,so as to obtain more accurate state and parameter estimation.Finally,the state and parameter estimation algorithm is verified based on the hardware in loop of the driving simulator experiment platform.The longitudinal speed and the side angle of the center of mass are estimated by the independent four wheel independent drive and steering electric vehicle.The research results show that the estimation algorithm studied in this paper can accurately estimate the state and parameters of the four wheel independent drive and steering electric vehicle.