Research On Parameters Matching And Optimization Method For Parallel Hybrid Electric Vehicle

The issues of energy shortage and air pollution are deteriorating,with the rapid development of automobile industry.New energy vehicle is the dawn to get rid of the plight,and the hybrid electric vehicle is the most prominent one,which is a hot research topic in the world because of its advantages of long mileage,low energy consumption and less emission.With a HEV taken as the research object,the thesis mainly study on the parameters matching and optimization method.This thesis analyzed the advantages and disadvantages of different structure about power system and took the biaxial parallel as the HEV's structure.According to the design requirements of the target vehicle,the theoretical calculation and empirical formula were used to match the main components of the powertrain system.On the view of robustness and practicability,the thesis designed a parallel electric auxiliary control strategy,the parameters of which were also given.Based on the software ADVISOR,the main components were analyzed and the whole vehicle model was built and simulated,the result of which showed that the matching parameters of system meet the requirements of vehicle dynamic performance,which laid the foundation for follow research.According to the problem that there are too many variables in the HEV,optimal Latin hypercube of the design of experiment was used to select the key variables that affect the target response.Nine variables involved three systems were selected as the optimization variables on the basis of the Pareto figure and main effect figure of the experimental results.The optimization process of constrains conditions,objectives and algorithm were then analyzed in detail,after which,an intelligent optimization platform was built on the Isight,with the MIGA used to deal with the multi-objective optimization of HEV.However,there was the problem that the optimization process was inefficient,although the vehicle performance improved.The two optimization methods of combination optimization method and approximate model optimization method were presented based on optimization algorithm and the optimization model respectively,in order to overcome the disadvantages of low efficiency of the traditional optimization method.The combined optimization method combined MIGA algorithm and SQP algorithm,with response surface method used by the approximate model optimization method to fit the real vehicle model.After analyzing the principle and implementation process of the two improved optimization methods in detail,the HEV was optimized by using multi-objective optimization method,the result of which showed that the optimization efficiency of the two methods were both improved.However,there were some errors in the accuracy of the approximate model optimization method.Therefore,the optimization method of combinatorial optimization was finally taken as the optimization method for the research.The simulation results of the vehicle before and after the optimization were compared and analyzed,which showed that the fuel economy and emission performance of the vehicle had been greatly improved.In this thesis,a method of parameter optimization for HEV was proposed,which can be used as a reference for solving the optimization of multi system parameters and improving the efficiency of HEV.It has the practical value.