PCC Strategy And Verification Based On Model Predictive Control

China ’ s road transport industry is developed,heavy trucks provide most of the capacity for road transport.With the advancement of vehicle networking technology and auxiliary driving technology,the intelligent degree of heavy trucks is getting higher and higher.At present,the most widely used auxiliary driving system in heavy trucks is Cruise Control System(CCS).In order to make up for the shortage of constant speed cruise system in fuel saving,this paper proposes a predictive cruise control system(PCC)based on model predictive control(MPC).The system relies on the slope information of the road ahead of the vehicle to plan the cruise control action,aiming at reducing the fuel consumption of heavy vehicles,saving transportation costs,improving the intelligence of trucks and reducing the labor intensity of drivers.Firstly,this paper uses the electronic map of FAW Jiefang big data platform Jiefangxing and Advanced Driver Assistant Systems(ADAS)to obtain the geographic information of expressway.Taking the highway with large flow of heavy truck as the analysis sample,the terrain characteristics are analyzed,and the advantages of model predictive control algorithm are clarified.The requirements of drivers are studied and the design objectives of PCC system are formulated.The principle of model predictive control algorithm is studied,and its application in predictive cruise control is analyzed.According to the calculation requirements of the model predictive control algorithm,a polynomial engine fuel consumption model and a vehicle longitudinal dynamic model are established based on the relevant data of the vehicle studied in this paper,and the above two models are used as predictive models for prediction and calculation.The PCC strategy design performance index is analyzed,and the optimization method of nonlinear programming is selected to transform the control problem of predictive cruise into a nonlinear programming problem.The collision avoidance adjustment strategy is studied for the safety problems in the process of using predictive cruise.Then,according to the parameter data of a 6 × 4 tractor of FAW Jiefang studied in this paper,a tractor model with high reduction degree is established in Truck Sim.According to the relevant literature,an ideal road model conforming to the road construction standard is designed to analyze the control action of the predictive cruise system on specific road types by controlling variables.The high-speed road geographic information of specific sections is obtained by Jiefangxing and ADAS electronic map,and the accuracy error of altitude data in electronic map is processed.Using the processed road data,a road model simulating real high speed terrain is established.PCC controller is established in MATLAB using S-Funtion module.The S-Funtion module is used to encapsulate the commercial CCS control algorithm as the CCS controller model.Finally,MATLAB and Truck Sim are used for co-simulation.The controller model established in MATLAB is used to control the vehicle model in Truck Sim to observe the response of PCC system to different road input under different load.Taking CCS system as the comparison object,a variety of simulation conditions are set,and the advantages and disadvantages of the two systems are analyzed in detail through the simulation experimental data.The simulation data show that under various working conditions set in this paper,compared with CCS system,PCC system can reduce the fuel consumption of heavy vehicles to varying degrees.At the same time,on the road model with the same length,the average speed of the vehicle using PCC system is higher in most working conditions,and it can run the whole distance faster.Therefore,the predictive cruise system based on model predictive control algorithm in this paper achieves the expected design goals,and verifies the feasibility of model predictive control algorithm in predictive cruise control.