| Energy conservation and environmental protection are the theme of today's world development.Pure electric vehicles have the advantages of zero emissions and high energy utilization.The development of pure electric vehicles is an important way to achieve transportation transformation and an important starting point for the country to achieve carbon neutrality goals.However,the mileage anxiety of pure electric vehicles restricts its promotion and application.The vehicle curb mass and power system parameters are closely related to the energy consumption of the vehicle;energy recovery technology can effectively improve the vehicle economy and extend the driving range.In addition,during the braking process of the vehicle,the body will produce pitching movement,and the active suspension can weaken the pitching movement of the body.These problems affect the driving range or ride comfort of the vehicle,so it is necessary to carry out research on energy recovery technology and active suspension control technology.Studies in this dissertation were supported by the national key research and development project "High-performance pure electric sports utility vehicle(SUV)development"(project number: 2018YFB0106100).With the goal of improving the energy recovery efficiency and ride comfort of the vehicle,research on the parameter design method of four-wheel drive pure electric vehicle,regenerative braking control and vehicle vertical motion suppression strategies have been carried out.The main research contents are as follows:(1)In response to the problem that it is difficult to obtain dynamic index constraints due to unknown vehicle curb mass,,a mass closed-loop algorithm was proposed and the convergence of the algorithm was proved.On this basis,a parameters closed-loop optimization design method of four-wheel drive pure electric vehicle was proposed,which merged mass closed-loop algorithm,dynamic programming algorithm and genetic algorithm.Among them,the dynamic programming algorithm was used to obtain the power consumption of the vehicle,and the genetic algorithm integrated the mass closed-loop algorithm and the dynamic programming algorithm.The simulation results showed that the proposed parameters design method could obtain suitable curb mass and power system parameters.This method provided a reference for the forward development of electric vehicles.(2)Aiming at the problem of efficient energy recovery in the braking process of vehicles,a predictive control strategy combining adaptive cubic index prediction and two-stage dynamic programming is proposed.The adaptive cubic index prediction predicted the driving data by mining the driving data—vehicle velocity and braking intensity,and provided parameter support for the two-stage dynamic programming.Two-stage dynamic programming algorithm,with the goal of maximizing vehicle energy recovery,combined with vehicle velocity and braking intensity,optimized the control parameters for electric vehicles—front motor torque,rear motor torque,front wheel cylinder pressure and rear wheel cylinder pressure to control vehicle operation.Compared with the ideal braking force distribution strategy and the multi-stage braking force distribution strategy,the energy recovery efficiency of the vehicle was improved.(3)During the braking process of the vehicle,the longitudinal movement of the vehicle will cause the pitching movement of the vehicle body,resulting in the deterioration of the ride comfort of the vehicle.In response to this problem,an equivalent half-vehicle model considering the influence of braking intensity was established.A model predictive control strategy was proposed,and the Lyapunov stability theory was used to prove the stability of the model predictive system.In this strategy,the vertical velocities of front body,rear body,front wheel and rear wheel were taken as the control objectives,and the quadratic optimization method was adopted to obtain the control force of front and rear suspension to suppressing the vertical motion of vehicle.Compared with the double loop control,the root-mean-square of front body vertical velocity,rear body vertical velocity,body pitch angle and body pitch angle velocity were reduced by more than 70%,which verified that the model predictive control strategy improved the comfort of the vehicle.In addition,it was verified that the model predictive control strategy has certain robustness through the simulation.(4)In order to simultaneously propose the energy recovery efficiency and ride comfort of the vehicle,based on the analysis of vehicle longitudinal-vertical interaction variables,a longitudinal-vertical coupling dynamic model in the braking process was established.This model considered the influence of braking intensity on the vertical movement of the vehicle and the influence of vertical movement on the wheel load.On this basis,a comprehensive control strategy adopting model predictive control to restrain the vehicle's vertical movement and neuro-fuzzy control to control the vehicle's longitudinal movement was proposed.In order to obtain the neuro-fuzzy control,a neuro-fuzzy optimization framework was proposed,which provided data support for the neuro-fuzzy control.The simulation results showed that the proposed comprehensive control strategy increased battery energy recovery by 8.33%,and reduced the maximum vehicle body velocity and acceleration by 65.37% and 24.33%,respectively.(5)Based on the electro-hydraulic compound braking test bench,the torque coupling principle of the bench system was analyzed,the motor torque test program was developed,and the relationship between the duty cycle of the proportional pressure reducing valve and the wheel cylinder pressure was calibrated.The verification of the part simulation results in the predictive control strategy and the comprehensive control strategy were carried out to verify the control effect of the proposed control strategies. |
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