Parameter Matching And Simulation Optimization Research Of Powertrain System For Range-Extended Electric Vehicle

With the situation of energy crisis,environmental contamination and global warming becoming more and more severe,the traditional automotive industry faces severe challenge.In addition to further upgrading of traditional automotive technology to achieve energy saving and emission reduction,the development of more energy-efficient and environment-friendly new energy vehicles has become an inevitable trend of automotive industry reform.But at this stage the vehicle power battery is limited by its energy density and cost,and it’s pretty difficult to have a great breakthrough in the short term,which limits the pure electric vehicle market-oriented process and industrial development.As a new type of new energy vehicles which fills the gap between the internal combustion engine vehicle and the pure electric vehicle,range-extender electric vehicle(REEV)can effectively reduce energy consumption on the basis of ensuring driving range,and has more advantages and broader development prospects at the present stage.Therefore,REEV has been the research hotspot in the field of automotive field at home and abroad.This paper focuses on powertrain parameter matching for REEV,and studies the control strategy,and the validity of parameter matching and control strategy is simulated and verified by ADVISOR.Finally,the multi-objective genetic algorithm is used to optimize powertrain parameter.The main contents are as follows:1.Introduce the powertrain composition and structure of REEV in detail,analyze the operation modes of the vehicle,the characteristics and the powertrain performance of REEV.According to the basic parameters and the performance indexes of the vehicle,the key parts of the powertrain system,such as the driving motor,the power battery and the extended range,have been selected and matched.2.Based on the operation pattern and parameter matching results of the REEV,the control strategy is studied,and adopt the power follow-up control strategy with better fuel economy and power,and design the principle of the control strategy for the REEV,and formulate the control strategies of REEV,including the pure electric mode control strategy,the extended mode control strategy,the hybrid drive mode control strategy,the regenerative braking control strategy and the power battery control strategy.3.According to the powertrain parameters matching results and the control strategy,the vehicle dynamic model,power battery model,drive motor model,final drive model and control strategy model are established in MATLAB/Simulink environment.Finally,in order to verify the validity of parameter matching and control strategy,the vehicle is simulated and analyzed on the basis of ADVISOR.4.The multi-objective genetic algorithm is used to optimize the performance of the vehicle.Set fuel consumption per kilometer and the acceleration time from 0 to 100km/h as the objective function,the maximum vehicle speed and the maximum climbing as the constraint function to optimize the final drive ratio.The optimization results show that the multi-objective genetic algorithm improves the economy of the vehicle under the premise of guaranteeing the vehicle’s dynamic performance.The optimization is reasonable and feasible and the result is effective.