Study On Differential And Slip Rate Control For The In-wheel Motor Electric Vehicle

Nowadays,the energy crisis has become increasingly serious.Energy conservation and environmental protection have become the mainstream topics in the world.Countries around the world have successively issued timetables for the ban on sales of traditional fuel vehicles.The related technologies of electric vehicles have quickly become the key research issues of many major automotive companies and research institutes in the world.Compared with traditional automobiles,wheel hub motor electric vehicles can independently control the driving force of each wheel,which has more controllability and also brings more technical difficulties.Among them,motor control and vehicle differential control have become two key technical issues for in-wheel motor electric vehicles.This paper has conducted the following research work on these two key technical issues.(1)wheel hub motor control research.The permanent magnet synchronous motor is selected as the research object,the structure of the motor is analyzed and a corresponding learning model is established.The space vector pulse width modulation strategy(SVPWM)is established and the correctness of the established modulation strategy is verified through simulation analysis.For the purpose of reducing the motor torque ripple,the traditional torque prediction control algorithm is analyzed and studied.The vectoriality of torque error and flux linkage error is considered based on the traditional algorithm.At the same time,the maximum torque current ratio(MTPA)technology is introduced.Therefore,an improved torque prediction control algorithm is proposed.By comparing the simulation with the traditional torque prediction control algorithm,the effectiveness of the improved torque prediction control algorithm is verified.(2)Establish a vehicle dynamic model.According to the needs of the control study object,an eight-degree-of-freedom vehicle model was established,including longitudinal,lateral,yaw and roll motions and four-wheel rotation of the vehicle body.The wheel movement model and the Dugoff tire model were established.The two-degree-offreedom model was established by considering the pursuit of the ideal state.The correctness of the established eight-degree-of-freedom vehicle model was verified through simulation and comparison with the Carsim model.(3)Research on vehicle differential control strategy.The differential speed control problem is analyzed from the perspective of vehicle stability.Based on the idea of hierarchical control,the vehicle differential control strategy is designed and the upper controller composed of a sliding mode controller and a tire force optimization distributor is used to track the ideal state of the vehicle body.An Ackermann-Jeantand ideal differential model was established.By analyzing the ideal differential model,the wheel speed controller was designed to track the ideal wheel speed.The stability and differential performance of the designed differential control strategy are analyzed through two different operating conditions.(4)Study on differential speed control strategy considering slip ratio.Based on the Burckhardt s-? theory,an optimal slip rate estimation model is established.With the best slip ratio as the control target,a slip ratio controller is designed based on the sliding mode theory.Speed controller for comparative analysis.In order to introduce slip factor into differential control,the relationship between slip ratio,wheel speed and wheel center speed is studied and analyzed.The ideal wheel speed and ideal wheel center speed are respectively taken as the control targets,based on the logic threshold theory.The slip ratio factor is introduced into the differential controller to design a high attachment priority controller and a low attachment priority controller.Based on this,from the perspective of vehicle analysis,with the goal of vehicle stability,an integrated control strategy was designed based on the fuzzy control theory,and the effectiveness of the aforementioned controller was compared and simulated under different simulation conditions.