Research Article
Investigation on ANFIS-GA controller for speed control of a BLDC fed hybrid source electric vehicle
@ARTICLE{10.4108/ew.4965, author={P. Jagadish Babu and A. Geetha}, title={Investigation on ANFIS-GA controller for speed control of a BLDC fed hybrid source electric vehicle}, journal={EAI Endorsed Transactions on Energy Web}, volume={11}, number={1}, publisher={EAI}, journal_a={EW}, year={2024}, month={1}, keywords={PV, Battery Pack, ANFIS, GA, H6, VSI, BLDC Motor}, doi={10.4108/ew.4965} }
- P. Jagadish Babu
A. Geetha
Year: 2024
Investigation on ANFIS-GA controller for speed control of a BLDC fed hybrid source electric vehicle
EW
EAI
DOI: 10.4108/ew.4965
Abstract
The BLDC (Brushless DC Motor) is utilized in electric vehicles, space missions, and mechanical applications. Neural Network Inference System reduces torque ripple for hybrid electric vehicle (PV-Battery) along with BLDC drive to achieve efficient speed performance and stability. A hybrid input source methodology is thus put forwarded to drive the stator currents giving exactly the expressed electromagnetic torque and counter-EMF harmonics. The torque and speed control technique are directed to neural network interference system, and H6 Voltage Source Inverter (H6 VSI) drives BLDC with a gate pulse signal. We examine how an ANFIS-GA torque controller may eliminate BLDC torque ripples under uninterrupted hybrid power supply in this work. MATLAB (Simulink) results show that Genetic Algorithm (GA) improves training of ANFIS better with varying set speed conditions. The ANFIS-GA controller outperforms challenging controllers under various BLDC motor driving load conditions, proving its efficiency.
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