Parameter Matching And Optimization Of Driving System For Urban Pure Electric Vehicle

With the shortage of petroleum resources and the increase of environmental pollution,people pay more and more attention to the effective use of resources and environmental protection.The emergence of automobiles has brought convenience to people's lives and caused harm to the environment.The development and promotion of new energy vehicles can reduce environmental pollution.New energy vehicles mainly include pure electric vehicles and hybrid vehicles.This article takes pure electric vehicles as the research object,and conducts in-depth research on the parameter matching and optimization methods of its drive system.Firstly,by analyzing the layout structure and working principle of pure electric vehicles,the kinematic characteristics of the whole vehicle are analyzed,and the driving balance equation and power balance equation are established,which lays a theoretical foundation for the parameter matching of pure electric vehicle drive systems.Secondly,according to the basic parameters of the vehicle and the two aspects of power and economy,the corresponding performance indicators are proposed,and use established mathematical model to perform parameter matching and calculation on the drive motor,battery and transmission system of the pure electric vehicle.Later,the vehicle model was built based on the CRUISE software,which included appropriate adjustments to the modules in the vehicle model according to the results of preliminary parameter matching,and established the corresponding mechanical,electrical and signal connections.Set up calculation tasks and perform simulations based on performance indicators,and analyze the simulation results to further verify the rationality of the preliminary parameter matching,that whether the dynamic performance and economic performance meet the design requirements.Finally,the particle swarm algorithm is studied and improved to make it have stronger global optimization capabilities.An objective function is established based on dynamics and economy,with dynamics as a constraint,and use an improved particle swarm algorithm to optimize the parameters of the drive system to find the optimal variable value to achieve the best performance of the entire vehicle.M file.Comparing the simulation results before and after optimization,the maximum speed and driving range have been enhanced,the energy consumption per 100 kilometers is reduced,and the energy utilization rate is improved.The optimized drive system parameters meet the requirements for the performance improvement of pure electric vehicles,and the feasibility of the improved particle swarm algorithm is proved.