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Optimal Adaptive Gain LQR-Based Energy Management Strategy for Battery–Supercapacitor Hybrid Power System

Author

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  • Seydali Ferahtia

    (Laboratoire d’Analyse des Signaux et Systémes, Department of Electrical Engineering, University of M’sila, M’sila 28000, Algeria)

  • Ali Djeroui

    (Electrical Engineering Laboratory, Department of Electrical Engineering, University of M’sila, M’sila 28000, Algeria)

  • Tedjani Mesbahi

    (ICube CNRS (UMR 7357) INSA Strasbourg, University of Strasbourg, 67000 Strasbourg, France)

  • Azeddine Houari

    (IREENA Laboratory, University of Nantes, 44602 Saint-Nazaire, France)

  • Samir Zeghlache

    (Laboratoire d’Analyse des Signaux et Systémes, Department of Electrical Engineering, University of M’sila, M’sila 28000, Algeria)

  • Hegazy Rezk

    (College of Engineering at Wadi Addawaser, Prince Sattam BinAbdulaziz University, Al-Kharj 11991, Saudi Arabia
    Electrical Engineering Department, Faculty of Engineering, Minia University, Minia 61111, Egypt)

  • Théophile Paul

    (ICube CNRS (UMR 7357) INSA Strasbourg, University of Strasbourg, 67000 Strasbourg, France)

Abstract

This paper aims at presenting an energy management strategy (EMS) based upon optimal control theory for a battery–supercapacitor hybrid power system. The hybrid power system consists of a lithium-ion battery and a supercapacitor with associated bidirectional DC/DC converters. The proposed EMS aims at computing adaptive gains using the salp swarm algorithm and load following control technique to assign the power reference for both the supercapacitor and the battery while achieving optimal performance and stable voltage. The DC/DC converter model is derived utilizing the first-principles method and computes the required gains to achieve the desired power. The fact that the developed algorithm takes disturbances into account increases the power elements’ life expectancies and supplies the power system with the required power.

Suggested Citation

  • Seydali Ferahtia & Ali Djeroui & Tedjani Mesbahi & Azeddine Houari & Samir Zeghlache & Hegazy Rezk & Théophile Paul, 2021. "Optimal Adaptive Gain LQR-Based Energy Management Strategy for Battery–Supercapacitor Hybrid Power System," Energies, MDPI, vol. 14(6), pages 1-16, March.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:6:p:1660-:d:518874
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Gabriel R. Broday & Luiz A. C. Lopes & Gilney Damm, 2022. "Exact Feedback Linearization of a Multi-Variable Controller for a Bi-Directional DC-DC Converter as Interface of an Energy Storage System," Energies, MDPI, vol. 15(21), pages 1-26, October.
    2. Pranta Das & Shuvra Prokash Biswas & Sudipto Mondal & Md Rabiul Islam, 2023. "Frequency Fluctuation Mitigation in a Single-Area Power System Using LQR-Based Proportional Damping Compensator," Energies, MDPI, vol. 16(12), pages 1-18, June.
    3. Ferahtia, Seydali & Djeroui, Ali & Rezk, Hegazy & Houari, Azeddine & Zeghlache, Samir & Machmoum, Mohamed, 2022. "Optimal control and implementation of energy management strategy for a DC microgrid," Energy, Elsevier, vol. 238(PB).
    4. Hartani, Mohamed Amine & Rezk, Hegazy & Benhammou, Aissa & Hamouda, Messaoud & Abdelkhalek, Othmane & Mekhilef, Saad & Olabi, A.G., 2023. "Proposed frequency decoupling-based fuzzy logic control for power allocation and state-of-charge recovery of hybrid energy storage systems adopting multi-level energy management for multi-DC-microgrid," Energy, Elsevier, vol. 278(C).
    5. Ferahtia, Seydali & Rezk, Hegazy & Abdelkareem, Mohammad Ali & Olabi, A.G., 2022. "Optimal techno-economic energy management strategy for building’s microgrids based bald eagle search optimization algorithm," Applied Energy, Elsevier, vol. 306(PB).
    6. Gabriel R. Broday & Gilney Damm & William Pasillas-Lépine & Luiz A. C. Lopes, 2021. "A Unified Controller for Multi-State Operation of the Bi-Directional Buck–Boost DC-DC Converter," Energies, MDPI, vol. 14(23), pages 1-21, November.

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