Research On Adaptive Cruise Control Of Pure Electric Vehicles Based On Multi-objective Optimization

With the continuous increase of car ownership in recent years,a series of social problems such as traffic congestion,frequent traffic accidents,and environmental pollution have followed.In order to solve the above problems,"intelligence,networking,electrification" has become the development direction of the automotive industry.Vehicle adaptive cruise control is one of many advanced driving assistance technologies,which can reduce the burden on drivers and improve driving safety.In recent years,it has received widespread attention from domestic and foreign automakers and research institutions.The system characteristics of pure electric vehicles are different from traditional fuel vehicles.Therefore,based on the system characteristics of pure electric vehicles,this paper adopts a layered system architecture to carry out the adaptive cruise control of the safety car spacing model,the upper layer motion decision algorithm and the lower layer execution control algorithm.Design.Analysis and modeling of pure electric vehicles.In this paper,front-drive is selected as the target vehicle configuration,a simplified permanent magnet synchronous motor model is established,the external characteristics and efficiency characteristics of the motor are analyzed,and the battery model is established by the internal resistance model method.Aiming at the braking energy recovery system,the factors affecting the braking energy recovery are analyzed,the front and rear axle braking force distribution strategy is designed,and the whole vehicle model is built using Carsim according to the target vehicle parameters.Using the data collected by the real car to verify the accuracy of the built vehicle model,the results show that the relative error of the vehicle speed is3.75% and the error of power consumption per 100 kilometers is 4.26%.It can be seen that the established vehicle model has high accuracy and can be used for pure electric Development of vehicle adaptive cruise control strategy.Based on model predictive control theory,a multi-objective optimization strategy for adaptive cruise control of pure electric vehicles is designed.Policy algorithm.Firstly,the safety car spacing model is designed according to the time interval of the fixed workshop,and then the kinematics model of the vehicle following process is established.For the safety,followability,economy and comfort of the following process,the design is designed In order to determine the performance index and constraint conditions,the weight coefficient of the corresponding index is determined by analytic hierarchy process.Theperformance indicators and constraints are derived,and finally the multi-objective optimization problem is transformed into a constrained quadratic programming problem for solution.A longitudinal control algorithm for a pure electric vehicle is designed to make the vehicle acceleration follow the expected acceleration.Firstly,the overall framework of the longitudinal control algorithm for pure electric vehicles is introduced.The driving and braking mode switching strategies are designed.The hysteresis threshold is introduced to avoid frequent switching between the two modes.Then,a feedforward plus BP-PID feedback control law based on vehicle longitudinal dynamics is designed.During braking,the braking force of the front axle and the rear axle is distributed according to the braking force distribution strategy.The front axle hydraulic brake dynamically supplements the power of the electric mechanism Deficiencies.And compared with the feedforward plus PID feedback algorithm,the results show that the longitudinal control algorithm proposed in this paper improves the IAE index by 22.6%and the ISE index by 19.6%,which can achieve more accurate expected acceleration tracking.Finally,a control system strategy model was built in Simulink,and an intelligent driving environment was built in Carsim.The cruise mode,start-stop mode and NEDC mode are selected to simulate the control strategy proposed in this paper.Compared with the LQR control algorithm in the NEDC operating conditions,the simulation results show that under the premise of meeting the safety of following the car,the algorithm proposed in this paper improves the followability of the LQR algorithm by22.6% and the economy by 2.9%.Multi-goal requirements during vehicle driving.There are 74 figures,16 tables and 66 references in this dissertation.