Research on the Yaw Stability Control Strategy of All-Wheel-Drive Electric Vehicle
|School||Shenyang University of Technology|
|Course||Power Electronics and Power Drives|
|Keywords||electric vehicle four-wheel drive yaw stability control variable universefuzzy control H_∞robust control adaptive compensation|
Four-wheel-drive electric vehicle has brought a new space for development by adopting the technique of wheel-motor direct traction. However, it is difficult to adapt the traditional chassis control. Most new generation of chassis control techniques adopt control strategies instead of mechanical structure to control vehicles by real-time control the state parameters. When under driving condition, the vehicle is affected by operating command, road condition or self-nonlinearity, the driving trajectory may seriously departure from the control command, even out of control. This thesis aims to control the stability of the four-wheel-drive vehicle which is subsidized by the project "Research on AWD Differential, Handing and Stability Control Key-technology of Double-fed Machine in Electric vehicles"(F12-277-1-11) of Shenyang Science Plan.Firstly, the thesis is started by analyzing the four-wheel-drive vehicle structure and the dynamic characteristics. A7freedom nonlinear dynamic model is established. And the model is linearized to a2freedom linear model. Linear and non-linear models are built to analyze the vehicle stability and to implement simulation.Secondly, according to the2freedom linear vehicle model, the thesis employs classical control theory to analyze how the yaw motion influences the operating stability. A fuzzy controller is control to dynamically adjust the yaw angle speed and achieve the stability control. Due to the fixed quantization level of fuzzy control, the control precision is low when the control command changes dramatically. Variable universe fuzzy control is deeper researched. The control precision of the system is enhanced though the input/output contraction-expansion factors. The simulation results show:the dynamic tracking capability of variable universe fuzzy control to the perturbation which is produced by high speed, large steering angle and the nonlinearity which is aroused by the change of road adhesion coefficient is better than fuzzy control and has higher control precision. The fuzzy control relies on the expert experience system, it can’t fully describe the driving condition with frequent changes and with bad adaptive ability. Simply yaw control cannot keep the vehicle stable during large steering angle. To solve this problem, the thesis regards the yaw angle speed and the sideslip angle as its control target, and designs an AFS/DYC integrated control system by adopting the robust control strategy. The system concurrently corrects the driving trajectory, avoids the vehicle out of control and enhances the robustness to serious nonlinearity of the vehicle under extreme state. The system is tested by simulation under fish-hook condition, the result shows that the control strategy can enhance the safe stability during high speed and large steering angle under terrible road condition and has good adaptation capacity to parametric variation of the vehicle.Finally, the rugged road arouses the load perturbation difference of the four-wheel motor, if the additional yaw moment distribution which is produced by yaw stability control has not been optimized enough, it may cause the saturation of the tire force. The problem may cause the imbalance of the wheel longitudinal driving force and the vehicle out of control. To solve the problem, the thesis adopts adaptive virtual compensation cooperative control to cooperate the torques of the four wheel motors. The control method can avoid the trajectory of vehicle seriously departure from the driving command. Under the condition of uprush of the load on arbitrary wheel when going straight or turning, the simulation results show the cooperate control strategy can dynamically balance the rotation rate difference of the four wheels and enhance the tracing capability of the vehicle to the driving command.