Research On Shift Control Strategy Of Multi-speed Transmission Electric Drive Axle For Urban Logistics Vehicle

With the gradual aggravation of fuel vehicle emissions,electric vehicles that use clean fuel as their power source have ushered in a broader development platform,which makes the work scenarios of electric vehicles more diversified.At the same time,with the continuous development of the e-commerce industry,the logistics industry also ushered in rare development opportunities during this period.Compared with traditional fuel vehicles,pure electric vehicles have many advantages as urban logistics vehicles.This article is based on the Jilin Provincial Department of Science and Technology project ”Urban Logistics Vehicle Electric Drive Axle Development and Intelligent Control Key Technology Research”,through the multi-speed electric drive Research on the gear shift control strategy of the bridge,proposes an adaptive comprehensive shift control strategy based on man-vehicle-road multi-factors.For the three-speed transmission electric drive axle,which is the research object of this article,this article firstly analyzes its structural form and power transmission principle,and builds the driver model,motor model,transmission model and integral model based on the power flow of the whole vehicle.The vehicle system simulation model including the vehicle dynamics model provides theoretical support and simulation platform for intelligent decision-making of electric drive axle gears.Secondly,according to vehicle information and driver operation information,the driver’s driving and braking intentions are identified online based on fuzzy control theory;the road longitudinal gradient is identified online based on the least square method with genetic factors;based on the extended Kalman filter algorithm Online identification of car quality.Then,it analyzes the traditional best power shift schedule and the best economic shift schedule,draws on the evaluation index of traditional fuel vehicle power performance and economy,and puts forward the evaluation index of power performance and economic performance for the research object of this paper.The evaluation index is based on the particle swarm algorithm to solve the multi-objective optimization problem.On the basis of comprehensively considering the power and economy of the vehicle,the recognition results of each online identifier are used to adjust the comprehensive shifting schedule,so that the shifting schedule can be adaptively based on the driver’s operation information,vehicle information,and road slope information.Make appropriate adjustments to ensure that the car has sufficient power and to ensure the safety of the driver.Taking into account the impact and sliding work caused by the clutch’s separation and engagement during the shifting process,this paper is based on the genetic algorithm to solve the shifting time to meet the driver’s comfort and improve the service life of the clutch.Finally,based on the MATLAB/Simulink simulation platform,the shift control strategy formulated in this paper is tested and analyzed.According to the comprehensive performance evaluation index proposed in this paper-the concept of comprehensive degree,the performance of each shifting schedule is compared,and the parameter identification results are verified.The modified effect of the gear pattern enables the gear shift pattern to meet the diverse needs of drivers and the requirements of driving conditions to a certain extent.Based on the simulation test results,it is shown that the shift control strategy formulated in this paper can take into account the power and economy of the car,and can adaptively adjust the shift schedule through the online parameter identifier to choose a personalized driving strategy that suits the driver’s habits.Under the circumstances,the problem of insufficient climbing power is solved to a certain extent and the safety of the driver is improved at the same time.