Research On Energy Management Strategy Of Plug-in Hybrid Electric Vehicle Considering Fuel Economy And Battery Life

Plug-in Hybrid Electric Vehicles(PHEV)combine the advantages of hybrid vehicles and pure electric vehicles.The battery of PHEV can be charged by an external power supply.PHEV have a certain pure electric driving range,which reduces the demand for traditional fossil fuel and vehicle use cost.The impact of battery life loss on energy management strategy research cannot be ignored.Therefore,it is of great academic significance and application value to study the energy management strategy of plug-in hybrid vehicles considering fuel economy and battery life.In this paper,a dual-motor plug-in hybrid vehicle is taken as the object of study.Based on the parameter matching design of the plug-in hybrid vehicle,the system efficiency model and the power battery life model of the vehicle are combined to consider the energy management strategy.Fuel economy and battery life were optimized for the energy management strategy control parameters with the optimal vehicle driving cost.The specific research work is as follows:(1)The in-depth analysis of the powertrain structure and working characteristics of the dual-motor plug-in hybrid system is studied.Based on the longitudinal dynamics model of the vehicle,the theoretical calculation method is used to get the parameters of the main components.The numerical model of each power component is established with the experimental data of power components.Based on Matlab/Simulink,the vehicle dynamic simulation model was built to simulate the vehicle kinetic performance.The results show that the parameter matching results can meet the automobile dynamic quality.(2)Analyzing the power flow in each working mode of the plug-in hybrid vehicle,establishing the dynamic equation and the system efficiency mode under each working mode comprehensively considering the engine efficiency,motor efficiency,gear transmission efficiency,battery efficiency,etc.The impact of fuel economy impact factors on energy management strategy design is analyzed;analyzing the main influencing factors of power battery life,based on the existing battery life experience model,the battery life model for energy management strategy simulation is established.Analyzing the heat generation mechanism and heat dissipation mechanism of the power battery,a simple battery thermal model is built to calculate the battery temperature to reflect the changes of the power battery temperature during the actual driving process of the vehicle.Then the influences of battery life impact factors to the energy management strategy are analyzed.(3)The weighted objective function of vehicle system efficiency and instantaneous battery life attenuation is established.Instantaneously optimize the weighted objective function of each mode,to obtain the optimal weighted objective function graphs for each working mode,as well as the optimal torque distribution or power distribution for the engine and motor in each working mode.Through the comparison and analysis of the optimal weighted target values in each working mode,obtaining the working mode division area of the CD and CS stage and the energy management strategy based on combination of rules and instantaneous optimization is developed.Then the energy management strategies under different weight coefficients are simulated under integrated cycle conditions,and the effects of different weight coefficients on fuel economy and battery life attenuation are analyzed.(4)The energy management strategy under a certain weight coefficient is selected to consider the influence of frequent engine start and stop on the energy management strategy control results,and the engine frequent start and stop control strategy is introduced.Taking the driving cost of the whole vehicle as the objective function,the adaptive genetic algorithm is used to optimize the parameters of the control system under the comprehensive working conditions.The optimization results show that the optimized energy management strategy reduces the driving cost of the whole vehicle by 3.14% compared to the strategy before optimization,and effectively limits the frequent start and stop times of the engine.