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Design And Study Of Fuel Cell Hybrid Forklift Power System

Posted on:2019-02-11Degree:MasterType:Thesis
Country:ChinaCandidate:Y C XuFull Text:PDF
GTID:2322330542460788Subject:Vehicle engineering
Abstract/Summary:PDF Full Text Request
Fuel cell hybrid power forklift as a green truck,using hydrogen as fuel,long continuous working time,fuel supply fast,has the advantages of low radiation to human body,high work efficiency,no noise pollution and pollutant discharge.To solve the problem of the low work efficiency and environment pollution of traditional internal combustion forklift and the long charging time and short working time of electric forklift,developing new energy sources forklift of storage batteries and fuel cells will be the future development direction of forklift trucks.Firstly,the advantages and disadvantages of various power combinations are compared and analyzed,the dynamic hybrid structure of fuel cell and battery is determined,the selection and parameter matching of the main components in the driving and hoisting system are completed.Then,using the method of forward simulation model and Matlab/Simulink software,a vehicle computer simulation model of fuel cell hybrid power forklift is established,provides tool support for the simulation analysis of the following energy management strategy and economic optimization.Secondly,according to the working characteristics of the forklift truck and energy management objectives,division based on the forklift work mode,making energy allocation strategy under various working modes,rules and logic control strategy is designed,The simulation is carried out under the circulation condition of the design of the forklift unique characteristics.Finally,the control parameters of the vehicle controller are optimized by genetic algorithm toolbox;simulation results show that the genetic algorithm optimization method can realize the optimization of the selected control parameters,reduce fuel consumption,improve economic forklift.
Keywords/Search Tags:Fuel Cell Forklift, Parameter Matching, Energy Management Strategy, Genetic Algorithm
PDF Full Text Request
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