ANFIS with PI Control of Interleaved DC-DC Converter for EV Charging Systems

In this paper, multiphase interleaved converters for electric vehicles are constructed and controlled using the Neuro Fuzzy control technology, also known as ANFIS. This method is typically employed to keep output voltages constant despite some input voltage instability. This research intends to sti...

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Bibliographic Details
Published in:	2024 IEEE 4th International Conference on Sustainable Energy and Future Electric Transportation (SEFET) pp. 1 - 5
Main Authors:	Gatla, Ranjith Kumar, Kasireddy, Idamakanti, Rao, D S Naga Malleswara, Narukullapti, Bharath Kumar, Kumar, Devineni Gireesh, Babu, P Chandra
Format:	Conference Proceeding
Language:	English
Published:	IEEE 31-07-2024
Subjects:	Electric vehicle Fuzzy Neuro control strategy of the interleaved three-phase dc/dc converter Mathematical models modular interleaved convertor Neural networks quick charging Regulation System performance Testing Training Transient analysis Transient response Transportation Voltage control
Online Access:	Get full text
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Summary:	In this paper, multiphase interleaved converters for electric vehicles are constructed and controlled using the Neuro Fuzzy control technology, also known as ANFIS. This method is typically employed to keep output voltages constant despite some input voltage instability. This research intends to stimulate and investigate the application of fuzzy neural controls and multiphase converters in the recharging of electric vehicles. The DC-DC converter system, which supplies stable power for the electric automobile, is subject to this control strategy. Also, the system's short-term operating performance is improved by this technique. This DC-DC fast charging for electric vehicle arrangement, which also uses the ANFIS-PI control technique to boost efficiency and shorten charging time, indicates the ripple input voltage and ripple output current. The principles discussed earlier make it evident how to test and design and MATLAB simulations are shown.
DOI:	10.1109/SEFET61574.2024.10718069