A Novel Sliding Mode Fuzzy Control based on SVM for Electric Vehicles Propulsion System

Boumédiène, Abdelmadjid; Abdellah, Laoufi

doi:10.37936/ecti-eec.2012102.170386

Cited by 5 publications

(1 citation statement)

References 25 publications

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“…This methodology expands the best exhibitions without load unsettling influence impacts. Sliding stage FLC incorporating with the SVM (space vector modulation) has been anticipated by [9] for the IM (induction motor) drives which are utilized in the electric vehicle impulsion configuration. To obtain and withstand the sliding stage and constancy of the configuration, the FLC has been exercised with the Lyapunov direct method.…”

Section: Introductionmentioning

confidence: 99%

ANN-based direct torque control scheme of an IM drive for a wide range of speed operation

Jeyashanthi¹,

Banu²

2021

NNW

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Induction motor (IM) drives with direct torque control (DTC) enable fast torque response without the need for complex orientation conversions or inner loop current loop. In the speed estimation responses, however, there is a significant level of torque ripple. The voltage source inverter adds acoustic noise and needs a high sampling frequency since it operates at a high and variable switching frequency. This work describes an ANN-based DTC technique for controlling the speed of an IM drive over a large speed range. To achieve good dynamic performance of induction motor drive, the ANN-based speed controller will replace the speed controller, switching tables, and hysteresis comparators. The neural network was trained using the back-propagation algorithm. The goal of a neural speed controller is to improve the system ability to respond quickly to changes in process variables while also mitigating the impacts of external perturbations. The projected ANN based DTC considerably and simply tracks the reference speed thus improves the efficiency of speed-torque of induction motors with quicker responses for rapid varying of speed reference and torque as that of Electric Vehicles in any uneven roads circumstances. MATLAB/Simulink software is used to evaluate the drive performance for both transient and dynamic operations. The proposed control performance is simulated and compared to a DTC-based traditional PI speed controller. In comparison to PI, the results show that ANN has better and faster effects. The torque ripple gets reduced by 1.5 % in ANN (artificial neural network) controller compared to PI controller. The THD (total harmonic distortion) is reduced by 6.38 % from PI controller to ANN controller.

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Section: Introductionmentioning

confidence: 99%

ANN-based direct torque control scheme of an IM drive for a wide range of speed operation

Jeyashanthi¹,

Banu²

2021

NNW

View full text Add to dashboard Cite

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Artificial neural networks applied on induction motor drive for an electric vehicle propulsion system

2021

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This article presents an artificial neural network (ANN) for loss minimization of direct torque-controlled induction motor (IM)-driven electric vehicles. The IM drive can consume more power than it needs to be, especially when it is operating under conditions under full load. The proposed architecture and its control strategies use ANN to control the amplitude starting current and save more power. The performance of these controllers was verified by simulation using the MATLAB/SIMULINK package, and the results showed good and high performance in the time domain response and rapid rejection of the system disturbance compared to the conventional proportional-integral derivative (PID) controller. Thus, the core loss of IM greatly reduces, thus improving the efficiency of the driving system. Finally, the experimental results were validated which were highly consistent with the simulation results using the DSPACE MicroLabBox controller.

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Direct torque control versus indirect field-oriented control of induction motors for electric vehicle applications

Aktaş

Awaili

Ehsani

et al. 2020

Engineering Science and Technology, an International Journal

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A Novel Sliding Mode Fuzzy Control based on SVM for Electric Vehicles Propulsion System

Cited by 5 publications

References 25 publications

ANN-based direct torque control scheme of an IM drive for a wide range of speed operation

ANN-based direct torque control scheme of an IM drive for a wide range of speed operation

Artificial neural networks applied on induction motor drive for an electric vehicle propulsion system

Direct torque control versus indirect field-oriented control of induction motors for electric vehicle applications

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