On Energy Management Strategy With Adaptability To Driving Conditions For Plug-in Hybrid Electric Vehicles

Due to the rapid development of transportation science and technology and the social situation in which energy is very scarce,people's requirements for fuel economy and comfort are becoming higher and higher for travel tools.Traditional fuel vehicles are no longer the mainstream of new vehicles,and a large number of hybrid vehicles have gradually replaced fuel vehicles.Plug-in hybrid electric vehicles(PHEVs)are also receiving attention due to their presence of an external power supply for external charging and the ability to have longer cruising range.Therefore,in recent years,academic research on plug-in hybrid vehicles has become more and more in-depth.This paper takes plug-in commuter hybrid vehicles as the research object,allocates energy to multiple power sources of hybrid vehicles.Under the condition of real-time interference of complex traffic information,two energy management strategies are designed to adapt to driving conditions.The main contents are as follows:Intelligent logic rule energy management strategy is designed based on particle swarm optimization(PSO): Firstly,for the fixed driving conditions,the charging state of the battery and the engine operating efficiency interval are the switching flags of different working modes,and the PSO is used to optimize the uncertain threshold parameters for improving the fuel economy of the vehicle.The logical rule energy management strategy based on PSO is designed.Then consider the real-time changes of traffic information such as speed and acceleration in reality,a large amount of historical traffic information was applied to the optimization algorithm,and an improved adaptive energy management strategy based on intelligent rules was proposed,which not only ensured fuel economy.The improvement in the average sense,and further improve the adaptability of the control strategy to different working conditions.Optimal energy management strategy integrated by adaptive neural fuzzy inference system(ANFIS)and adaptive equivalent consumption minimization strategy(A-ECMS)is designed: It mainly includes two-level discrete optimization design and one online integration implementation,Firstly,using the current traffic information and some historical data,off-line training of adaptive neural network fuzzy inference system results in a trained fuzzy inference system to plan and predict adaptive near-optimal SOC curves in advance for online applications.Then,according to the traffic congestion situation obtained from the historical traffic speed information data,the commute driving path is segmented,and the PSO algorithm is used to optimize the equivalence factor control parameter and the equivalent factor initial value in the ECMS offline process for each section.and then the optimization results of the equivalent factor control parameters are converted into several offline MAP maps;Finally,the integrated training FIS and the A-ECMS with optimized MAP are the online FIS-A-ECMS energy management control system,which can adjust the equivalence factor in the instantaneous optimization of fuel consumption in real time according to the actual traffic information to ensure the optimal fuel consumption in actual operation and adaptable to suit all driving conditions.In order to fully verify the effectiveness of the proposed control strategy and the adaptability to various working conditions,this paper uses the joint simulation platform built by MATLAB/Simulink and GT-SUITE to verify the simulation experiment and compare the simulation results with other energy management strategy for comparative analysis.