Motor Torque Fault Diagnosis For Four Wheel Independent Motor-drive Vehicle Based On UKF

In many types of electric vehicles,the hub-driven electric vehicle revolutionizes the chassis structure and driving methods of traditional fuel vehicles.It has compact structure,high transmission efficiency,and flexible and controllable wheel drive/brake torque.It is an electric vehicle drive.An important development direction of the system.Wheel drive is a distributed drive,which can significantly improve the handling of the car,and brings higher requirements to the coordinated control of the drive system.Its fault diagnosis technology is one of the important research contents of the wheel active drive vehicle safety control field.This dissertation focuses on the research of fault diagnosis methods for wheel-driven automotive motor.It has important theoretical and practical significance.This article first analyzes the fault state of the hub-driven electric vehicle,and according to the fault characteristics of the hub-driven electric vehicle and the vehicle dynamics principle,establishes the fault model for fault diagnosis of the drive motor,that is,consider the longitudinal movement,lateral movement,and lateral movement of the vehicle body.The seven-degree-of-freedom nonlinear vehicle dynamic model of pendulum motion and each wheel rotation motion introduces fault parameter simulation to drive the motor fault.The fault parameter is defined as the ratio of the actual output torque of the hub motor to the command torque,and the tire model is selected from the Dugoff tire model..Based on the high-precision vehicle dynamics simulation software ve DYNA,the accuracy and effectiveness of the model are verified and verified.The results show that it can be used as a model basis for the fault diagnosis of wheel hub-driven electric motor drive motors.Considering that the in-wheel motor drive vehicle is a kind of strong nonlinear system,the Unscented Kalman filter algorithm has higher estimation accuracy than the Extended Kalman filter algorithm when dealing with nonlinear systems,and it does not need to calculate the Jacobian matrix.Based on the above advantages,this paper proposes a fault diagnosis method based on the Unscented Kalman filter for the wheel-driving automobile drive motor,which transforms the problem of wheel-motor fault diagnosis into real-time estimation of model parameters.In order to fully verify the effectiveness of the algorithm,several sets of typical simulation conditions test estimation effects were designed for single wheel failure,same side wheel failure and three wheel failures.The results show that the proposed estimator can estimate the drive motor failure parameter in real time.The above fault diagnosis only uses body acceleration sensors and wheel speed sensors,but when the sensor signal has a deviation or disturbance,the estimated result of the fault parameter is no longer reliable.For this reason,this paper proposes a vehicle based on a vehicle equipped with a low-precision GPS sensor.The fault diagnosis method of wheel-driven automobile drive motor integrated with GPS/INS system uses Kalman filtering method to integrate GPS/INS information to solve the problems of slow GPS update rate and accumulated error of INS sensor,and improve model estimation accuracy.