Research On Estimation Of Driving State And Road Adhesion Coefficient Of Distributed Drive Electric Vehicle

With the popularization of new energy vehicles and the related policies being tilted,electric vehicles have become one of the inevitable trends in the future development of automobiles,distributed drive electric vehicle is a new type of electric vehicle.which is directly driven by four motors on the powertrain structure.It has good vehicle dynamics control characteristics.State parameter estimation is one of the key technologies of vehicle active safety control.The sensor equipment which can directly measure state parameters can not be applied to mass production vehicles because of its high cost,the traditional vehicle state parameter estimation method is limited by the vehicle sensor information.The distributed drive electric vehicle has the advantages of easy access to four-wheel Torque and rotational speed,it is very important to study the state estimation method for distributed drive electric vehicle to improve the vehicle control effect.Firstly,the dynamic model of the distributed drive electric vehicle is established,considering the need of vehicle response analysis and state observer design,and the 14-dof model of the whole vehicle including the tire and the drive motor model is established,the Simulink simulation model of the whole vehicle is established according to the dynamics differential equation.The whole vehicle model in Carsim software is transformed into a distributed electric vehicle model driven by four motors.The model is used to verify the accuracy of the 14-dof Simulink model.Secondly,the response characteristics of the vehicle system are analyzed when the parameters change.Based on the dynamic model of distributed drive electric vehicle,the changes of vertical and lateral motion state caused by the change of several parameters are analyzed,and the dynamic response of vehicle is evaluated by standard deviation.Based on the stability boundary of the phase plane,the variation of the vehicle stability region under the condition of different initial speed,front wheel angle and road adhesion coefficient is analyzed.Thirdly,Design vehicle driving state observer.According to the different driving state parameters,the corresponding vehicle state estimation models are established,and the driving state estimation State observer based on the Kalman filter algorithm is designed,the vehicle centroid sideslip angle,longitudinal,lateral and vertical speed,as well as the vehicle body roll,pitch and yaw rate are estimated,the simulation results show that the Kalman filter is more accurate and more stable than the traditional Kalman filter and extended Kalman filter.Finally,the road adhesion coefficient observer is designed and the tire force in the Dugoff tire model is normalized to establish the vehicle 7-dof road adhesion coefficient estimation model,a road adhesion coefficient observer based on unsented Kalman filter and adaptive unsented Kalman filter algorithm is designed by combining the state parameters of the vehicle state observer output,the simulation results show that the road adhesion coefficient observer based on the adaptive Kalman filter algorithm has higher accuracy and stability.