Intelligent Speed Regulation Control in Switched Reluctance Motor of Electric Vehicle Based on Neural Network Parameter Identification

Network Parameter ◽

Nonlinear Simulation ◽

Switched Reluctance ◽

Speed Regulation ◽

This paper studies the intelligent speed regulation control of switched reluctance motor of electric vehicle based on neural network parameter identification. Starting with the analysis of the performance of switched reluctance motor, the nonlinear flux linkage characteristic inversion model and torque characteristic model of switched reluctance motor are established based on BP neural network. This paper studies and improves the fast self configuration algorithm of BP neural network. Finally, the nonlinear simulation model of switched reluctance motor is established under Matlab/Simulink. The model can be used for further control research. In this paper, the integrated control method of instantaneous torque control based on torque observation and three-step commutation control is studied, and the simulation analysis is carried out. The results show that this method can effectively reduce the torque ripple of switched reluctance motor and improve the performance of its drive system.

Intelligent Speed Regulation Control of Switched Reluctance Motor of Electric Vehicle Based on Neural Network Parameter Identification

Forest Chemicals Review ◽

10.17762/jfcr.vi.195 ◽

2021 ◽

pp. 157-164

Author(s):

Xiaoliang Zhang

Keyword(s):

Electric Vehicle ◽

Electric Vehicles ◽

High Efficiency ◽

Good Control ◽

Regulation System ◽

Network Parameter ◽

Switched Reluctance ◽

Speed Regulation ◽

Based on the analysis of the related theories of switched reluctance motor, this paper designs and implements the intelligent speed regulation system of switched reluctance motor for electric vehicle. The speed regulation system has the characteristics of high efficiency and energy saving, wide speed regulation range and small starting current. The system also has the advantages of large starting torque, frequent start and stop, forward and reverse switching, high torque / inertia ratio and four quadrant operation. The system can provide a good solution for variable speed drive. The digital controller of the system is composed of TMS320F2812 DSP and EPM7128S CPLD, which can realize the starting, electric and braking functions of electric vehicles. Experiments show that the switched reluctance motor drive system for electric vehicle has good control performance and strong fault tolerance. The system can meet the requirements of various working conditions of electric vehicles and has broad development prospects.

Nonlinear Modeling of Switched Reluctance Motor Based on BP Neural Network

Third International Conference on Natural Computation (ICNC 2007) ◽

10.1109/icnc.2007.504 ◽

2007 ◽

Cited By ~ 7

Author(s):

Yan Cai ◽

Chao Gao

Keyword(s):

Neural Network ◽

Bp Neural Network ◽

Nonlinear Modeling ◽

Switched Reluctance ◽

Neural network based torque control of switched reluctance motor for hybrid electric vehicle propulsion at low speeds

2009 IEEE International Conference on Electro/Information Technology ◽

10.1109/eit.2009.5189653 ◽

2009 ◽

Cited By ~ 1

Author(s):

Dongyun Lu ◽

Narayan C. Kar

Keyword(s):

Neural Network ◽

Electric Vehicle ◽

Hybrid Electric Vehicle ◽

Torque Control ◽

Switched Reluctance ◽

Vehicle Propulsion ◽

Reluctance Motor ◽

Low Speeds ◽

Hybrid Electric

Proceedings of the 2nd International Conference on Electrical and Electronic Engineering (EEE 2019) ◽

The Design of BP Neural Network Modeling for Switched Reluctance Motor

10.2991/eee-19.2019.28 ◽

2019 ◽

Cited By ~ 1

Author(s):

Dong-kai Qiao ◽

Mei-qing Cai ◽

Guo-le Li

Keyword(s):

Neural Network ◽

Bp Neural Network ◽

Network Modeling ◽

Neural Network Modeling ◽

Switched Reluctance ◽

Position Sensorless Control System of SRM Based on Improved BP Neural Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.468-471.2187 ◽

2012 ◽

Vol 468-471 ◽

pp. 2187-2192 ◽

Cited By ~ 1

Author(s):

Li Xiao ◽

He Xu Sun ◽

Feng Gao

Keyword(s):

Neural Network ◽

Control System ◽

Bp Neural Network ◽

Computing Time ◽

Detection Accuracy ◽

Training Time ◽

Switched Reluctance ◽

Reluctance Motor ◽

Position Sensorless

Due to the shortcomings of long training time and slow convergence of BP neural network, this paper presents a new improved method that weight is no longer a constant but turned into a function of adjustable parameters. After the training of the improved BP neural network is completed, the network can map the nonlinear relationship between motor current, flux and rotor position. Based on the analysis of the unique structural properties of switched reluctance motor, this paper also proposes a method of greatly reducing the sample data to save computing time. Simulation results show that this method simplifies the complexity of the control system and improve detection accuracy, thus realize position sensorless detection of the switched reluctance motor.