IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i18p6628-d911746.html
   My bibliography  Save this article

Adaptive Model Predictive Control for DAB Converter Switching Losses Reduction

Author

Listed:
  • Adriano Nardoto

    (Electrical Engineering Department, Federal Institute of Espírito Santo (IFES), BR101 Km 58, São Mateus 29932-540, Brazil)

  • Arthur Amorim

    (Electrical Engineering Department, Federal Institute of Espírito Santo (IFES), BR101 Km 58, São Mateus 29932-540, Brazil)

  • Nelson Santana

    (Electrical Engineering Department, Federal Institute of Espírito Santo (IFES), BR101 Km 58, São Mateus 29932-540, Brazil)

  • Emilio Bueno

    (Department of Electronics, Alcalá University (UAH), Plaza San Diego S/N, 28801 Madrid, Spain)

  • Lucas Encarnação

    (Department of Electrical Engineering, Federal University of Espírito Santo (UFES), Av. Fernando Ferrari, 514, Vitória 29075-910, Brazil)

  • Walbermark Santos

    (Department of Electrical Engineering, Federal University of Espírito Santo (UFES), Av. Fernando Ferrari, 514, Vitória 29075-910, Brazil)

Abstract

The solid-state transformer is the enabling technology for the future of electric power systems. The increasing relevance of this equipment demands higher standards for efficiency and losses reduction. The dual active bridge (DAB) topology is the most usual DC-DC converter used in the solid-state transformer, and is responsible for part of its switching losses. The traditional phase-shift modulation used on DAB converters presents significant switching losses during the operation with reduced loads. The alternative Triangular and Trapezoidal Modulations have been proposed in recent literature; however, there are limitations on the maximum power these techniques can deal with. This paper presents an adaptive model predictive control to select among these three techniques, according to the converter model, the one that minimizes the switching losses and allows the current demanded by the load. Moreover, an alternative cost function is proposed, including the output voltage and current. Through real-time simulation, using a 1000 V/600 V 12 kW DAB converter, it is shown that the proposed control is able to reduce the losses on the converter. Furthermore, the proposed control presents fast and accurate response, and precise transition between the modulation techniques.

Suggested Citation

  • Adriano Nardoto & Arthur Amorim & Nelson Santana & Emilio Bueno & Lucas Encarnação & Walbermark Santos, 2022. "Adaptive Model Predictive Control for DAB Converter Switching Losses Reduction," Energies, MDPI, vol. 15(18), pages 1-24, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:18:p:6628-:d:911746
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/18/6628/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/18/6628/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Pierpaolo Dini & Sergio Saponara, 2020. "Design of Adaptive Controller Exploiting Learning Concepts Applied to a BLDC-Based Drive System," Energies, MDPI, vol. 13(10), pages 1-20, May.
    2. Renner Sartório Camargo & Daniel Santamargarita Mayor & Alvar Mayor Miguel & Emilio José Bueno & Lucas Frizera Encarnação, 2020. "A Novel Cascaded Multilevel Converter Topology Based on Three-Phase Cells—CHB-SDC," Energies, MDPI, vol. 13(18), pages 1-25, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nguyen Ngoc Nam & Sung Hyun Kim, 2022. "Robust Tracking Control of Dual-Active-Bridge DC–DC Converters with Parameter Uncertainties and Input Saturation," Mathematics, MDPI, vol. 10(24), pages 1-19, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lucian Mihet-Popa & Sergio Saponara, 2021. "Power Converters, Electric Drives and Energy Storage Systems for Electrified Transportation and Smart Grid Applications," Energies, MDPI, vol. 14(14), pages 1-5, July.
    2. Juan Carlos Travieso-Torres & Manuel A. Duarte-Mermoud & Matías Díaz & Camilo Contreras-Jara & Francisco Hernández, 2022. "Closed-Loop Adaptive High-Starting Torque Scalar Control Scheme for Induction Motor Variable Speed Drives," Energies, MDPI, vol. 15(10), pages 1-15, May.
    3. Ze Jiang & Xiaoyan Huang & Wenping Cao, 2022. "RLS-Based Algorithm for Detecting Partial Demagnetization under Both Stationary and Nonstationary Conditions," Energies, MDPI, vol. 15(10), pages 1-17, May.
    4. Adeel Bashir & Sikandar Khan & Naveed Iqbal & Salem Bashmal & Sami Ullah & Fayyaz & Muhammad Usman, 2023. "A Review of the Various Control Algorithms for Trajectory Control of Unmanned Underwater Vehicles," Sustainability, MDPI, vol. 15(20), pages 1-21, October.
    5. Pierpaolo Dini & Sergio Saponara, 2022. "Review on Model Based Design of Advanced Control Algorithms for Cogging Torque Reduction in Power Drive Systems," Energies, MDPI, vol. 15(23), pages 1-29, November.
    6. Michal Vidlak & Lukas Gorel & Pavol Makys & Michal Stano, 2021. "Sensorless Speed Control of Brushed DC Motor Based at New Current Ripple Component Signal Processing," Energies, MDPI, vol. 14(17), pages 1-25, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:18:p:6628-:d:911746. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.