Artificial Neural Networks Approach for Reduced RMS Currents in Triple Active Bridge Converters

Ahmed A. Ibrahim; Andrea Zilio; Tarek Younis; Davide Biadene; Tommaso Caldognetto; Paolo Mattavelli

doi:10.1109/IECON49645.2022.9968804

Artificial Neural Networks Approach for Reduced RMS Currents in Triple Active Bridge Converters

Ahmed A. Ibrahim
, Andrea Zilio
, Tarek Younis
, Davide Biadene
, Tommaso Caldognetto
, Paolo Mattavelli

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

13 Scopus citations

Abstract

Isolated multi-port converters show the merits of hosting several sources and loads with different voltage and power ratings, allowing power routing among multiple ports with high power density. However, many degrees of freedom are available for modulation, and exploiting them for optimal converter operation is challenging. This paper proposes an artificial neural network (ANN) approach that minimizes the rms ports currents of a triple active bridge (TAB) converter for the entire range of operation. The ANN is trained to determine the optimum duty-cycles for total true rms current minimization. The effectiveness of the ANN implementation is shown by considering an experimental TAB converter prototype rated 5kW.

Original language	English
Title of host publication	IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society
Publisher	IEEE Computer Society
ISBN (Electronic)	9781665480253
DOIs	https://doi.org/10.1109/IECON49645.2022.9968804
State	Published - 2022
Externally published	Yes
Event	48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022 - Brussels, Belgium Duration: 17 Oct 2022 → 20 Oct 2022

Publication series

Name	IECON Proceedings (Industrial Electronics Conference)
Volume	2022-October
ISSN (Print)	2162-4704
ISSN (Electronic)	2577-1647

Conference

Conference	48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022
Country/Territory	Belgium
City	Brussels
Period	17/10/22 → 20/10/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

Artificial neural network (ANN)
multi-port converter
triple active bridge (TAB)

ASJC Scopus subject areas

Control and Systems Engineering
Electrical and Electronic Engineering

Access to Document

10.1109/IECON49645.2022.9968804

Cite this

Ibrahim, A. A., Zilio, A., Younis, T., Biadene, D., Caldognetto, T., & Mattavelli, P. (2022). Artificial Neural Networks Approach for Reduced RMS Currents in Triple Active Bridge Converters. In IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society (IECON Proceedings (Industrial Electronics Conference); Vol. 2022-October). IEEE Computer Society. https://doi.org/10.1109/IECON49645.2022.9968804

@inproceedings{c4ab5b63a20742f4addf1a94937daf26,

title = "Artificial Neural Networks Approach for Reduced RMS Currents in Triple Active Bridge Converters",

abstract = "Isolated multi-port converters show the merits of hosting several sources and loads with different voltage and power ratings, allowing power routing among multiple ports with high power density. However, many degrees of freedom are available for modulation, and exploiting them for optimal converter operation is challenging. This paper proposes an artificial neural network (ANN) approach that minimizes the rms ports currents of a triple active bridge (TAB) converter for the entire range of operation. The ANN is trained to determine the optimum duty-cycles for total true rms current minimization. The effectiveness of the ANN implementation is shown by considering an experimental TAB converter prototype rated 5kW.",

keywords = "Artificial neural network (ANN), multi-port converter, triple active bridge (TAB)",

author = "Ibrahim, \{Ahmed A.\} and Andrea Zilio and Tarek Younis and Davide Biadene and Tommaso Caldognetto and Paolo Mattavelli",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022 ; Conference date: 17-10-2022 Through 20-10-2022",

year = "2022",

doi = "10.1109/IECON49645.2022.9968804",

language = "English",

series = "IECON Proceedings (Industrial Electronics Conference)",

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Ibrahim, AA, Zilio, A, Younis, T, Biadene, D, Caldognetto, T & Mattavelli, P 2022, Artificial Neural Networks Approach for Reduced RMS Currents in Triple Active Bridge Converters. in IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society. IECON Proceedings (Industrial Electronics Conference), vol. 2022-October, IEEE Computer Society, 48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022, Brussels, Belgium, 17/10/22. https://doi.org/10.1109/IECON49645.2022.9968804

Artificial Neural Networks Approach for Reduced RMS Currents in Triple Active Bridge Converters. / Ibrahim, Ahmed A.; Zilio, Andrea; Younis, Tarek et al.
IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society. IEEE Computer Society, 2022. (IECON Proceedings (Industrial Electronics Conference); Vol. 2022-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Artificial Neural Networks Approach for Reduced RMS Currents in Triple Active Bridge Converters

AU - Ibrahim, Ahmed A.

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AU - Younis, Tarek

AU - Biadene, Davide

AU - Caldognetto, Tommaso

AU - Mattavelli, Paolo

PY - 2022

Y1 - 2022

N2 - Isolated multi-port converters show the merits of hosting several sources and loads with different voltage and power ratings, allowing power routing among multiple ports with high power density. However, many degrees of freedom are available for modulation, and exploiting them for optimal converter operation is challenging. This paper proposes an artificial neural network (ANN) approach that minimizes the rms ports currents of a triple active bridge (TAB) converter for the entire range of operation. The ANN is trained to determine the optimum duty-cycles for total true rms current minimization. The effectiveness of the ANN implementation is shown by considering an experimental TAB converter prototype rated 5kW.

AB - Isolated multi-port converters show the merits of hosting several sources and loads with different voltage and power ratings, allowing power routing among multiple ports with high power density. However, many degrees of freedom are available for modulation, and exploiting them for optimal converter operation is challenging. This paper proposes an artificial neural network (ANN) approach that minimizes the rms ports currents of a triple active bridge (TAB) converter for the entire range of operation. The ANN is trained to determine the optimum duty-cycles for total true rms current minimization. The effectiveness of the ANN implementation is shown by considering an experimental TAB converter prototype rated 5kW.

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KW - multi-port converter

KW - triple active bridge (TAB)

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M3 - Conference contribution

AN - SCOPUS:85143904929

T3 - IECON Proceedings (Industrial Electronics Conference)

BT - IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society

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Ibrahim AA, Zilio A, Younis T, Biadene D, Caldognetto T, Mattavelli P. Artificial Neural Networks Approach for Reduced RMS Currents in Triple Active Bridge Converters. In IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society. IEEE Computer Society. 2022. (IECON Proceedings (Industrial Electronics Conference)). doi: 10.1109/IECON49645.2022.9968804

Artificial Neural Networks Approach for Reduced RMS Currents in Triple Active Bridge Converters

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this