Research On Intelligent Energy Management Strategy Of Hybrid Electric Vehicle In Networked Environment

The proposal of intelligent transportation system opens up a new way for the automobile industry to solve the problems of environmental pollution and energy consumption.Intelligent networked vehicle technology is the mainstream of current research in automobile industry.In this paper,a layered Control system framework for energy management of series hybrid electric vehicle(HEV)in networked environment is designed with Model Predictive Control(MPC)as the main research method to improve fuel economy,traffic efficiency,safety and ride comfort.The main research work of this paper is as follows:Firstly,the layered control architecture of HEV in networked environment is constructed.In order to simulate the intelligent transportation environment,a networked environment model is built based on Matlab/Simulink software to realize the communication process between vehicles and traffic infrastructure(V2I)and between vehicles and vehicles(V2V).A Simulink and Carsim co-simulation platform was established to verify the upper target velocity optimization algorithm.Aiming at the selected hybrid electric vehicle configuration,a quasi-static mathematical model of the hybrid electric vehicle system was established based on Simulink software.Secondly,in order to reduce the number of vehicles waiting for parking at signal crossing and the resource consumption caused by engine idling,and improve the efficiency of vehicles,the Internet of vehicles technology is used to obtain the status of traffic light signals ahead and make planning in advance.In the network environment,the vehicle can plan the target speed range of the vehicle according to the signal lamp timing information obtained,driving in this range can avoid reaching the intersection and waiting in the red light time window.The basic principle of model predictive control and the solving process are described.Then,taking the total vehicle displacement and speed as state variables,and the vehicle acceleration as control variables,the longitudinal kinematics and dynamics models were established respectively for comparison.Based on the models,the MPC prediction models were constructed respectively,and the upper speed planning controller was designed to solve the optimal acceleration,so as to obtain the optimal speed.In the upper speed planning,there are two scenarios: no front vehicle and front vehicle.In the scene where the vehicle in front appears,the following distance of the vehicle is adjusted in real time by using the information of the displacement and speed of the vehicle in front.The approximate fuel consumption model was established by using the power balance equation of vehicle running,combined with the traffic light status information,a multi-objective optimization problem was established based on MPC,and the sequential quadratic programming(SQP)algorithm was used to solve the problem,and the optimal control quantity was obtained.Finally,on the basis of MPC speed planning algorithm,in order to ensure the vehicle power performance and improve fuel economy,the MPC based lower energy management algorithm is designed.According to the optimal speed calculated by the upper controller,the required power of the vehicle can be obtained under the condition that all constraints are satisfied.And with battery SOC as state variables,engine speed and generator torque as control variable,based on the energy of the MPC control algorithm,make the engine in most cases in the area with high efficiency,improve vehicle fuel economy while maintaining the battery SOC at a reasonable scope,and by using the dynamic programming algorithm for comparison,the performance of the validation of the MPC algorithm.