State Estimation And Handling Stability Control Of Distributed Drive Electric Vehicles

Accurate acquisition of state parameters is an important aspect of vehicle handling and stability control research.Vigorously developing electric vehicles is a widely recognized international technical route to alleviate the energy crisis and reduce environmental pollution.The four wheel torque of distributed drive electric vehicles is accurately and independently controllable,and the motor response is rapid.It is the chassis system with the most potential for achieving vehicle handling and stability within the full range of operating conditions.This paper takes distributed drive electric vehicles as the research object,and conducts relevant research work on vehicle state estimation methods,active front wheel steering,and direct yaw torque integrated control.The specific content is as follows:Tire force estimation method based on sliding mode observer.Establish tire dynamics model,vehicle dynamics model,and drive motor mathematical model,and establish a simulation model by configuring the vehicle body model in Carsim software.A sliding mode observer for longitudinal and lateral tire forces is designed using sliding mode theory,and a force distribution rule for four wheel lateral tires is designed based on the tire vertical force formula.Aiming at the chattering problem of sliding mode observer systems,considering the characteristics of each state parameter estimation,different forms of saturation functions are designed to replace symbolic functions to weaken the chattering of sliding mode systems.The effectiveness of the observer is verified through simulation experiments.A vehicle state estimation method based on adaptive square root volume Kalman filtering algorithm.Traditional Kalman filtering algorithms are difficult to apply to nonlinear systems.Therefore,a square root volumetric Kalman filtering algorithm is used to overcome the problem that the positive definiteness of the matrix in the volumetric Kalman filtering algorithm is easily damaged.Considering the impact of vehicle state parameters and road adhesion coefficient changes on estimation accuracy,an adaptive square root volumetric Kalman filtering algorithm is proposed to design a vehicle state estimator,Solve the problem of constant process noise and measurement noise.Through simulation experiments,the effectiveness and estimation accuracy of the algorithm are verified.Integrated model predictive control for active front wheel steering and direct yaw moment combined with vehicle stability phase plane.The vehicle handling and stability control system is designed using a hierarchical structure.The upper controller aims to track the ideal yaw rate and center of mass sideslip angle,and adopts a model predictive control algorithm to design an integrated controller for vehicle active front wheel steering and direct yaw torque.The degree of vehicle stability is judged based on the centroid sideslip angle being parallel,and it is introduced into the objective function of the model predictive controller to adjust the output active angle and additional yaw torque in real time.The lower controller is used to achieve the generalized yaw torque determined by the upper controller.Design a torque optimization allocation objective function that integrates tire utilization rate and tire slip rate,and verify it through simulation experiments under different driving conditions.