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Research On Regenerative Braking System Of Brushless DC Motor Based On Fuzzy Neural Network

Posted on:2020-08-26Degree:MasterType:Thesis
Country:ChinaCandidate:N XiangFull Text:PDF
GTID:2392330599959806Subject:Engineering
Abstract/Summary:PDF Full Text Request
With the rapid development of economy,the environment pollution problem has attracted more attention among them,the fuel automobile exhaust pollution is a major concern.And in recent years,global political and economic turmoil,oil crisis looming.In response to the energy crisis and environmental pollution countries increased the development of electric vehicles.But the range of electric vehicles still falls short of people's requirements,and regenerative braking technology,which can effectively extend the range,has attracted more attention from researchers.This paper first analyze the principle of BLDCM(Brushless Direct Current Motor)structure.On this basis,the influence factors and principles of regenerative braking control for electric vehicles are discussed.In order to provide a stable initial speed,a double closed-loop speed control system is designed.Then the regenerative braking control of electric vehicle is studied,A controller based on T-S(Takagi-Sugeno)fuzzy neural network control strategy is designed.For the controller,the difference of the feedback current and the given current and speed are selected as inputs,and PWM pulse width adjustment amount is selected as output,and the BP(Back Propagation)neural network is used to adjust the controller input membership functions and fuzzy rules adaptively.An electric vehicle regenerative braking control system model is designed and the simulation experiments were done with the controller.Finally,the controller controlled by infineon TC1782 is designed.Simulation test was carried out on dSPACE semi-physical simulation platform.The work completed in this paper is as follows:(1)The principle and mathematical model of BLDCM for electric vehicle are studied,and the principle of regenerative braking control system is analyzed.Before realizing the regenerative braking control system,the BLDCM driving control system was built based on Matlab/Simulink.The system is a double closed loop speed control system,its speed loop is fuzzy PID control,and the current loop is PI control.The system drives the motor to the speed required for regenerative braking.(2)For BLDCM PWM modulation mode in the regenerative braking system analysis principle and mathematical deduction,choose to use a higher energy recovery rate of half bridge modulation method.(3)Mathematical analysis and derivation of regenerative braking control strategy based on T-S fuzzy neural network were carried out,and a regenerative braking controlsystem based on T-S fuzzy neural network was built on Matlab/Simulink platform.The simulation results show that the energy recovery of the fuzzy control strategy based on T-S is obviously higher than that of fuzzy control,and the design goal is achieved.(4)In order to verify the effect of the control strategy in the actual controller,the regenerative braking controller based on infineon TC1782 chip was designed and the software program was written.Then,the regenerative braking control system in addition to the controller is built on the dSPACE platform,and the controller is connected to the HIL simulation cabinet for semi-physical simulation.The innovation of this paper:(1)Analysis of principle of regenerative braking control technology,to improve the traditional fuzzy control strategy of regenerative braking,designed the regenerative braking based on t-s fuzzy neural network control system,and contrast and fuzzy regenerative braking control system,the experiment proved that the designed system can effectively increase the energy recovery rate.(2)Building design based on t-s fuzzy neural network regenerative braking control system hardware-in-the-loop simulation platform,the hardware design based on infineon TC1782 regenerative braking controller chip,and design the corresponding software program.Verify the validity of the designed system.
Keywords/Search Tags:Electric Vehicle, BLDCM, Regenerative Braking, dSPACE
PDF Full Text Request
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