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Research On The Coordinated Control Strategy Of AFS And DYC For Distributed Drive Electric Vehicles

Posted on:2020-07-18Degree:MasterType:Thesis
Country:ChinaCandidate:C LiuFull Text:PDF
GTID:2432330575458862Subject:Vehicle Engineering
Abstract/Summary:PDF Full Text Request
At present,the development of electric vehicles has become an important method for the vehicle industry to achieve energy conservation and emission reduction,active safety and driving comfort of vehicles are the research focus of automobile’s development.Distributed drive electric vehicles based on the unique structure and performance characteristics of wheel hub motor,which have great potential advantages in vehicle stability control.In order to further improve the driving stability of distributed drive electric vehicles,a coordinated control strategy based on active front steering(AFS)and direct yaw control(DYC)was designed in this paper.By reasonably distributing the working strength and time sequence of AFS sub-systems and DYC sub-systems,not only can the driving stability be improved,but also the tire wear can be reduced and the driving comfort can be improved.Firstly,the vehicle dynamic model was constructed,and the influence of the wheels’force on the vehicle’s yaw movement was analysed.In addition,the working principle and performance of AFS and DYC were introduced.Then,the influences of AFS and DYC on vehicle stability were discussed respectively,which have laid a theoretical foundation for the design of coordinated control strategy.Secondly,the coordinated control strategy of AFS and DYC was designed,which consisted of two parts:(1)A stability judgeing method based on sideslip angle and sideslip angular velocity(β-β)phase plane was proposed.The phase plane stability boundary coefficient was adjusted by the road adhesion coefficient,which divided the coordination control region reasonably,so as to judge whether the vehicle was unstable and switched the appropriate subsystem.(2)Considering that the driver’s response was not timely under extreme conditions,and there was a time delay between the driver’s behavior and the actuator,the vehicle would miss the best control opportunity,which would destabilize the vehicle.A driving state prediction algorithm based on data stream mining technology and Markov theory was proposed.The fuzzy control theory which had good robustness was applied,the driving state in the future were made as the inputs,which could determine the weight coefficients of AFS and DYC in advance and effectively avoid the potential danger of entering the unstable state.Then,the nonlinear 3-dof vehicle model was used as the reference model,and AFS control strategy was designed based on model predictive control(MPC)theory.At the same time,in order to avoid no solution in the calculation process and improve the convergence speed of the solution process,the relaxation factor was introduced.The quadratic programming algorithm was adopted to find the optimal solution,so as to adjust the front wheel angle in real time to follow the desired path.In order to make uq for the deficiency of AFS control strategy,the feedforward-feedback DYC control strategy was designed.A variable weight coefficient LQR feedback control strategy was proposed,and the matrix weight coefficient could be adjusted in real time according to the vehicle steering state in the future and the road adhesion coefficient at the present time.The additional yaw moment was used to distribute the torque of each drive wheel by means of load transfer,which could improve the driving stability of vehicles.Finally,the accuracy and real-time of the prediction algorithm were verified according to different and actual driving cycle data.The simulation experiments of typical working conditions were carried out based on commercial dynamics simulation software to verify the effectiveness of the coordinated control strategy.The simulation results showed that the coordinated control method of AFS and DYC proposed had better effect of driving stability control,which provided a new method for the design of vehicle active safety.
Keywords/Search Tags:distributed drive, coordination control, data stream mining, Markov, MPC
PDF Full Text Request
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