| Adaptive Cruise Control(ACC)system plays a crucial role in intelligent driving system,and its longitudinal control ability is the basis for realizing unmanned driving.In order to further improve the safety and comfort of the ACC system,this paper takes the pure electric vehicle as the research object,studies the upper and lower controller,spacing strategy,mode division and switching strategy of its ACC system,and uses CarSim and Simulink for co-simulation.To verify the correctness and effectiveness of the proposed ACC system.The research content is detailed as follows.Firstly,in order to make the variable spacing strategy more reasonable and improve the comfort of ACC vehicle driving,this paper uses variable universe fuzzy control to improve the spacing strategy based on variable headway.Specifically,the double fuzzy controller is selected,the relative speed and spacing error are taken as the input of the two fuzzy controllers,the contraction-expansion factor is taken as the output of the fuzzy controller Ⅰ,and the relative speed coefficient in the variable headway is taken as the output of the fuzzy controller Ⅱ.The two fuzzy controllers are combined to realize the improvement of the spacing strategy of the variable universe fuzzy control.Through numerical simulation analysis,it is verified that the spacing strategy based on variable universe fuzzy control can effectively improve driving comfort while ensuring driving safety.Secondly,the multi-mode switching strategy of the ACC system is studied for the upper controller.In order to enhance the adaptability of ACC system to complex working conditions,the ACC vehicle driving state is divided into cruise control region,transition region and carfollowing region,and the control strategy and switching rules corresponding to each region are formulated.When the system was in the cruise region,the PID algorithm was used to complete the upper controller design of cruise mode.When the system is in the car-following region,based on the vehicle longitudinal kinematics Model,the Model Predictive Control(MPC)algorithm is used to calculate the optimal control of multi-objective and multi-constraint for the driving state,and the upper controller design of the car-following mode is completed.When the system is in the transition region,this paper proposes an ACC mode switching strategy that combines the longitudinal kinematic characteristics of the vehicle with the expected acceleration calculated from cruise and car-following modes.The switching strategy is designed by MPC algorithm to select the optimal control mode to switch,so as to reduce the acceleration fluctuation caused by cruise/car-following mode switching.At the same time,in order to enhance the safety of ACC vehicles,an emergency collision avoidance mode is designed,which adjusts the speed reduction according to the collision time and the vehicle speed.The effectiveness of the upper control algorithm and the rationality of the switching strategy are verified by numerical simulation of the steady state condition and the urgent condition.Thirdly,the lower layer controller was built based on PID algorithm.According to the vehicle data in CarSim,the vehicle dynamic model is established,the force of ACC vehicle under driving state is analyzed,the drive/brake controller is built,and the PID algorithm is used to adjust the control quantity,so that the vehicle can quickly and stably reach the desired control target.In addition,in order to reduce the frequent switching of the drive/brake controller,the controller switching strategy based on threshold is designed.Finally,the feasibility of the multi-mode ACC system is verified by the co-simulation of CarSim and Simulink.In CarSim,vehicle information,road environment and sensor parameters are adjusted,and the co-simulation of combined working condition and WLTC working condition is carried out.The simulation results prove the correctness of the upper and lower layer controller and the feasibility of the optimized switching strategy. |
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