Research On Lateral Stability Of Distributed Drive Electric Vehicle Based On ASOSM Algorithm

In the actual driving process of distributed drive electric vehicles,due to the large mass of hub motor and high unsprung mass,the vehicle is easy to lose control at high speed,leading to traffic accidents.Therefore,to reduce such traffic accidents,this paper proposes an adaptive second-order sliding mode controller to improve vehicle stability.Firstly,we build the vehicle model.The principal purpose of the two-degree-of-freedom model is to obtain the ideal state value as a reference and as the input value of the observer.We used the seven-degree-of-freedom as the control basis of the controller,and then the magic tire formula is used to simulate the tire characteristics to improve simulation accuracy.The driver model is also an important part.Considering the sideslip characteristics of the vehicle during driving,this article uses an Ackerman steering to simulate,and through the optimal driver,preview model to ensure that the vehicle can follow a predetermined trajectory.Secondly,to improve the lateral stability of distributed drive electric vehicles,this paper designs a super-twist sliding mode observer to realize the problem of difficulty in obtaining the sideslip angle,and reduce the estimation error of each state parameter;For stability control,chattering occurs in the first-order sliding mode control.It is difficult to determine the parameters and the boundary of the uncertainty of external interference in the second-order sliding mode control.To solve these problems,in the upper-level controller,a second-order sliding mode controller based on the adaptive law is realized by adding the high-frequency term of the first derivative of the pre-designed switching surface.The control gain can be calculated by the adaptive method,and parameters of the vehicle system are estimated by the adaptive law in real-time.The stability is proved by the Lyapunov function,and the additional yaw moment to correct the vehicle stability is obtained under the condition of unknown parameters and uncertainty of external interference.Furthermore,we allocated the additional yaw moment obtained by the upper controller to each wheel legitimately obtained.In the lower controller,we allocate the additional torque by the secondary planning method,so that the optimization can be achieved when all constraints are met.The objective function reaches the optimal value,that is,to minimize the utilization rate of tires.It not only has a fast calculation speed but also can handle multiple constraint conditions to ensure the real-time dynamic response,thereby improving the efficiency of torque distribution.Finally,we use the Matlab/Simulink and Car Sim co-simulation under the double-shifting high-low working condition and the serpentine condition.Compared with the simulation results of FOSM,FOSM-sat,and SOSM,the ASOSM algorithm not only responds quickly but also has better robustness in reducing the impact of parameter uncertainty of system without sacrificing system robustness.Its main advantage is that it not only reduces chattering but also tracks the uncertainty boundary error well under the adaptive law,showing that the controller has good control performance.Among them,the super-twist sliding mode observer has shown good results of observation,and the observation results of the sideslip angle and yaw rate are consistent with reality.The research results in this paper show that the MAE value of the yaw rate under the adaptive second-order sliding mode controller is reduced by 72.9%,73.2%,and 46.8%respectively compared with FOSM,FOSM-sat,and SOSM.Compared with FOSM,FOSMsat,and SOSM,the MAE value of the x-y displacement trajectory of ASOSM is respectively reduced by 49.6%,49.4%,and 39.1%.We have significantly improved the lateral stability of the vehicle,making the vehicle safer in driving.