State Parameters Estimation And Torque Distribution Control For Distributed Electric Vehicles

Distributed drive electric vehicles have many advantages,including four drive motor of fast response and independent precise control,energy conservation and emissions reduction,structure arrangement,vehicle dynamics control.It is an important development direction of electric vehicles in the future.Because the moving vehicle is a parameter time-varying nonlinear dynamics,and distributed drive also brings difficulties to vehicle status and road adhesion estimation.Accurately coordinating the torque of each wheel according to different driving conditions,In order to give full play to the advantages of the drive multi degree of freedom in the performance control of the whole vehicle.In view of the above problems,the following research work has been carried out:A vehicle model is established to realize distributed drive electric vehicle dynamic control.A two-degree-of-freedom reference model is obtained by linearizing the tire force.A nonlinear model is used to establish the dynamic model of distributed drive ev with three degrees of freedom,longitudinal,lateral and horizontal.First,the vehicle model besides the drive system is built in the Carsim software.Then,the driving motor model is built by using MATLA/Simulink,and then a complete distributed drive electric vehicle dynamic model is constructed jointly.By comparing with the traditional automobile,the results show that the distributed electric vehicle has better response speed.Information on vehicle state parameters plays an important role in vehicle performance control.Vehicle driving state and road adhesion coefficient are estimated.Based on the nonlinear vehicle dynamics model,the external input quantity and output variables are designed.The longitudinal speed,the angular velocity and the center Angle of the centroid are calculated with the vehicle longitudinal acceleration and the front wheel Angle sensor.The state equation and the observation equation of the nonlinear system are established.The Untrace Kalman Filter introduces the value of Fuzzy inference to dynamically adjust the observed noise covariance,that are improved the self-adaptability of observing noise,In order to overcome the influence of uncertainty measurement noise on the state estimation accuracy.The simulation results show that the proposed method has higher precision than the Extended Kalman Filter and the Untrace Kalman Filter.Based on the above estimation method,a pavement identification method based on peak adhesion coefficient is designed.The effectiveness of the method is verified by simulation on high and low adhesion road surface.Research on torque distribution control based on comprehensive optimization objective,A torque distribution strategy is proposed to improve tire attachment margin and improve drive performance.The control system of vehicle torque distribution is designed,including the driver's power demand,the decision-making layer and the torque optimization distribution layer.A comprehensive objective function with two performances of tire utilization and longitudinal drive is established.The objective function is solved by using global Sequential Quadratic Programming method based on the generalized horizontal pendulum moment decision basis and under the constraint condition of driving anti-skid,motor failure,maximum driving torque and road surface.The simulation test of the road surface and the failure condition of driving motor is carried out under the joint control model,that shows he distribution strategy improves the vehicle's dynamic performance under the premise of ensuring the stability of the vehicle,and improves the load of the wheels.