Research On Energy Optimization Management Strategy Of Parallel Plug-in Hybrid Electric Vehicle

Under the global background of continuous depletion of oil resources and increasing environmental pollution,development of pure fuel vehicles has been severely constrained,and countries have successively announced the ban on the sale of fuel vehicles.More and more companies have begun to carry out electrification layout,with hybrid vehicles and pure electric vehicles as the main development direction,but due to the limitations of battery technology,development of pure electric vehicles is not mature.The hybrid electric vehicle has a typical electromechanical coupling system,which has two power sources,an engine and a motor.Taking into consideration both driving force and fuel economy,it is currently the most practical research direction.Due to the complexity of power system structure,formulation of vehicle control strategy greatly affects fuel economy and exhaust emissions of vehicle.This article starts from the torque distribution of power source as a starting point.The main contents are as follows.Taking the parrallel plug-in hybrid electric vehicle(PHEV)as the research object,secondary development is carried out in vehicle simulation software to build a simulation model of vehicle power system.By analyzing the working characteristics of PHEV,main working mode is determined,and fuzzy control strategy is adopted to distribute torque of two power sources.In view of the subjectivity of fuzzy controller,this paper uses tabu search(TS)and immune particle swarm optimization(IPSO)to optimize some subjective parameters of fuzzy controller,with the lowest fuel consumption and SOC consumption The minimum fuel consumption and exhaust emissions are taken as the optimization goal,then fuzzy controller parameters under a certain driving cycle are finally determined to realize the energy distribution of power system.On this basis,considering that the type of PHEV driving cycles are changing,this dissertation summarizes the types of PHEV driving cycles as three typical types,and separately optimizes fuzzy control strategy offline under various driving cycles to get the optimal controller parameters vector.In addition,the support vector machine(SVM)is used to learn the working condition data,and an algorithm model that can distinguish the driving cycle type in real time is trained,and combined with fuzzy control strategy,a driving cycle adaptive fuzzy energy management strategy is proposed.In order to verify the effectiveness of the proposed energy management strategy,this strategy was compared with a fixed threshold-based charge depleting-charge sustaining energy management strategy.The simulation results verified that compared with charge depleting-charge sustaining energy management strategy,the adaptive fuzzy energy management strategy proposed in this dissertation have significantly reduced fuel consumption and pollutant emissions.