Research On Motion Control Strategy Of Distributed Drive Electric Intelligent Vehicle

With the rapid development of electrification,intelligentization and network connection of automobiles,distributed drive electric intelligent vehicles become an overwhelmingly important direction of the automotive industry in the future with its advantages of environmental protection,safety,efficiency and intelligence.As one of the key technologies of intelligent vehicles,motion control is crucial to keep the vehicle tracking the reference trajectory steadily with high accuracy,especially in some complex conditions such as high-speed cornering,lane change overtaking,emergency obstacle avoidance,etc.,when the vehicle is usually nonlinear with strong longitudinal-lateral coupling,and the lateral force of tires is vulnerable to saturation,then the vehicle may deviate from the reference trajectory,and even has the risk of destabilization such as side slipping,skidding and rollover.Therefore,this paper conducts research on the motion control strategy of distributed drive electric intelligent vehicles,and mainly focuses on vehicle dynamics modeling,vehicle stability analysis and destabilization risk prediction,and multi-objective coordinated motion control of electric intelligent vehicles.To further improve tracking accuracy and vehicle active safety performance of the intelligent vehicle while tracking the reference trajectory.The specific work is as follows:(1)In order to establish an accurate and reliable vehicle model and serve as a simulation model basis for the development and validation of algorithms,a distributed drive electric vehicle model was built by jointly using Car Sim and Matlab/Simulink.Firstly,the vehicle body,steering system and suspension system models are built using Car Sim;the tire model and drive train model of Car Sim are replaced by the external Uni Tire tire model and hub motor model established in Simulink;the accuracy and reliability of the model are verified by comparing with the real vehicle test and tire test data.(2)To evaluate the state of the vehicle accurately and provide guidance to the controller design,a vehicle state supervision and risk prediction mechanism is established.Firstly,the changing law of the phase plane saddle point positions in the front and rear wheel sideslip angles under different driving conditions is investigated,and the stability boundary of the phase plane is parametrically described based on the saddle point positions,on the basis of which a quantitative stability evaluation index is designed to evaluate the vehicle yaw stability margin quantifiably.Secondly,we predict the vehicle state based on the eight-degree-of-freedom vehicle model,quantitatively assess the vehicle rollover risk using the predicted load transfer ratio index,and provide early warning when it exceeds the safety threshold.In this way,the real-time monitoring and risk prediction of vehicle states is achieved.(3)To solve the intelligent vehicle trajectory tracking and yaw stability coordination control problem under the condition with longitudinal-lateral motion coupling.Firstly,considering the influence of tire model accuracy on the real-time performance and control effect of the algorithm,a control-oriented Uni Tire-Ctrl model is established on the basis of the traditional Uni Tire model through theoretical derivation and appropriate model simplificatio n,which has the advantages of fewer parameters,compact expressions,and good real-time performance by concentrating the nonlinear and coupling characteristics of tires in the effective cornering stiffness,while retaining most of the tire characteristics.After that,the MPC lateral motion controller was designed based on the Uni Tire-Ctrl model,and soft constraints of tire sideslip angles are introduced to coordinate the tracking accuracy and yaw stability of the vehicle.In addition,the LQR is used to follow the target speed in the longitudinal direction,and the torque distribution is accomplished considering the tire adhesion utilization ratio.Finally,by conducting simulation tests in conventional and extreme combined working conditions,we found that the designed controller can effectively coordinate the tracking accuracy and vehicle stability based on the accurate tire lateral deflection stiffness information,and determine a reasonable control amount,which shows a higher tracking accuracy in conventional working conditions and can still control the vehicle to track the reference trajectory stably in extreme working conditions,and has a considerable control potential under extreme working conditions.(4)To further take into account the risk of ve hicle rollover instability,a multi-objective coordinated motion controller for electric intelligent vehicles considering yaw-rollover risk is designed.The priorities of trajectory tracking and yaw-rollover stability control are determined by incorporating the yaw stability evaluation index and the predicted load transfer ratio,and the weights of multi-objectives are scheduled adaptively accordingly to achieve multi-objective coordinated control by controlling the front-wheel steering angle and torque of the four wheels.Finally,by conducting emergency obstacle avoidance simulation tests on different road surfaces,we found that the designed controller can accurately evaluate the vehicle state and effectively coordinate the priority and weight of multi-objectives by incorporating the requirements of different working conditions,which makes the vehicle track the reference trajectory steadily,maintain the risk of vehicle rollover instability within the safety threshold and further improves the vehicle tracking accuracy and active safety performance and shows a good adaptability to working conditions.