Research On Cooperative Adaptive Cruise Control Strategy For Electric Vehicle

With the rapid growth of car ownership,a series of problems have arisen,such as the increase in the number of traffic accidents and the increase in energy consumption.The adaptive cruise system is one of the advanced driving assistance systems of automobiles,which not only reduces the driving burden of the driver,but also reduces the occurrence of traffic accidents to a certain extent.In addition,the progress of new energy automobile can alleviate expenditure of energy sources and environmental pollution problems.This thesis takes pure electric vehicles as the research object,combined with the advantages of V2 X wireless communication technology to obtain information from surrounding vehicles,and conducts research on cooperative adaptive cruise control(CACC)strategy.The main research contents of the thesis are as follows:First of all,the overall design plan of the CACC system is clarified,including the communication topology of the queue,the driver priority control strategy,the cruise control/following mode switching strategy,and the hierarchical controller is briefly explained;a follow-up distance strategy that considers the overall movement trend of the queue is proposed,and theoretically proves the convergence and stability.Simulation and comparison experiments show that the strategy can not only dynamically adjust the distance to the workshop according to the information of the moving state of the front and rear vehicles,but also has a good convergence speed,and its comprehensive performance is good.Secondly,the vehicle dynamics model of the pure electric vehicle is built,which is mainly transformed from the vehicle model in the vehicle dynamics simulation software Carsim.The drive system is modified to be driven by motors,and completes the selection of the wheel hub motor type,parameter matching and mathematical modeling,etc.And the effectiveness of the motor is verified through simulation tests.Thirdly,through the analysis of the longitudinal dynamics of the vehicle,the lower-level controller is designed to convert the expected acceleration given by the upper-level into driving torque and braking pressure;in order to prevent the damage caused by the simultaneous working or frequent switching of the driving and braking systems,designs the drive/brake system switching strategy;and the simulation experiment verifies the controller’s exact tracking performance to the expected acceleration.Then,researches and improves the upper controller of the CACC system.Through the analysis of the longitudinal kinematics of the queue combined with the model predictive control(MPC)theory,the system’s state equations,performance indicators,predictive models and constraints are established;the fuzzy control theory is used to improve the original fixed-weight MPC algorithm so that it can dynamically adjust the weight parameters according to different working conditions.Two sets of comparative experiments show that the improved adaptive weight MPC algorithm ha s a significant improvement in safety u nder dangerous working conditions;under safe working conditions,passenger comfort is better.Finally,the reliability and effectiveness of the CACC system are verified under a variety of typical operating conditions.First,a single electric vehicle CACC system control model and a four-vehicle queue simulation test platform are built using Carsim/Simulink co-simulation;offline simulation tests are performed under five typical working conditions,and the simulation consequences reveal that the system can adapt well to diverse working conditions,thus verifying the reliability and validity of the control tactics.