Research On Energy Management Control Strategy Of Hybrid Electric Vehicl

Energy depletion and environmental pollution are two issues that are of great importance to countries around the world today.While traditional cars are our daily means of transport and bring convenience to our lives,their massive use has led to a steady decline in global oil resources and the health hazards of vehicle exhaust emissions.The search for a new renewable energy vehicle to replace conventional fuel vehicles has become a global goal.Hybrid electric vehicle(HEV)is one of the types of new energy vehicles that have the advantages of both conventional fuel vehicles and pure electric vehicles and has become a rallying point for the world’s automotive industry.The energy management strategy is the core technology of HEVs,which plays a decisive role in the fuel economy,driving performance and exhaust emission performance of the vehicle.In this paper,a fuzzy energy management strategy based on working condition identification is carried out for parallel hybrid vehicles,and the specific research contents are as follows:(1)Establish a hybrid vehicle simulation model.The parallel HEV is selected as the target model,the principle analysis of the key parts of the vehicle is carried out and its simulation model is built.The working model of the hybrid vehicle is divided according to the way and direction of energy flow during driving.A CD-CS energy management strategy is designed and the validity of the model is verified in the simulation platform ADVISOR.(2)Design and optimisation of the fuzzy control strategy.A fuzzy controller with total demand torque and battery SOC as input and engine torque as output is designed to control the torque distribution of the vehicle.To address the subjectivity of the design of the affiliation function,this paper combines simulated annealing with an improved particle swarm algorithm to optimise the fuzzy control parameters.The optimal control parameters for this working condition are determined by taking the fuel consumption of 100 km,the change of battery SOC and the exhaust emission as the comprehensive indexes as the objective function,so as to achieve the optimal distribution of the energy source of the whole vehicle.(3)Research on the working condition identification strategy.Firstly,the characteristic parameters of the working conditions are processed,and the standard working conditions are classified through the method of system clustering,and three typical working conditions are obtained: urban working conditions,suburban working conditions and high-speed working conditions,and the method of principal component analysis is used to reduce the dimensionality of the characteristic parameters.Considering the variability of the actual driving conditions of the vehicles,a model-inthe-loop approach is adopted to construct an optimisation model for the parameters of the energy management strategy,and a library of optimisation parameters for the typical working conditions is established.A generalised regression neural network(GRNN)training data is used to build a condition recogniser,and the combination of the condition recogniser and the optimisation parameter library enables the vehicle model to select the corresponding optimisation parameters according to the current driving condition type.Simulation results show that the designed adaptive energy management strategy can reduce fuel consumption per 100 km,reduce exhaust emissions and extend battery life while ensuring the vehicle’s power performance.