IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i6p1660-d518874.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/6/1660/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/6/1660/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Naoya Shigeta & Seyed Ehsan Hosseini, 2020. "Sustainable Development of the Automobile Industry in the United States, Europe, and Japan with Special Focus on the Vehicles’ Power Sources," Energies, MDPI, vol. 14(1), pages 1-32, December.
    2. Lei, Zhenzhen & Qin, Datong & Hou, Liliang & Peng, Jingyu & Liu, Yonggang & Chen, Zheng, 2020. "An adaptive equivalent consumption minimization strategy for plug-in hybrid electric vehicles based on traffic information," Energy, Elsevier, vol. 190(C).
    3. Zou, Yuan & Liu, Teng & Liu, Dexing & Sun, Fengchun, 2016. "Reinforcement learning-based real-time energy management for a hybrid tracked vehicle," Applied Energy, Elsevier, vol. 171(C), pages 372-382.
    4. Bizon, Nicu, 2019. "Real-time optimization strategies of Fuel Cell Hybrid Power Systems based on Load-following control: A new strategy, and a comparative study of topologies and fuel economy obtained," Applied Energy, Elsevier, vol. 241(C), pages 444-460.
    5. Paska, Józef & Biczel, Piotr & Kłos, Mariusz, 2009. "Hybrid power systems – An effective way of utilising primary energy sources," Renewable Energy, Elsevier, vol. 34(11), pages 2414-2421.
    6. Nicu Bizon & Mihai Oproescu, 2018. "Experimental Comparison of Three Real-Time Optimization Strategies Applied to Renewable/FC-Based Hybrid Power Systems Based on Load-Following Control," Energies, MDPI, vol. 11(12), pages 1-32, December.
    7. Ioan-Sorin Sorlei & Nicu Bizon & Phatiphat Thounthong & Mihai Varlam & Elena Carcadea & Mihai Culcer & Mariana Iliescu & Mircea Raceanu, 2021. "Fuel Cell Electric Vehicles—A Brief Review of Current Topologies and Energy Management Strategies," Energies, MDPI, vol. 14(1), pages 1-29, January.
    8. Shehab Al-Sakkaf & Mahmoud Kassas & Muhammad Khalid & Mohammad A. Abido, 2019. "An Energy Management System for Residential Autonomous DC Microgrid Using Optimized Fuzzy Logic Controller Considering Economic Dispatch," Energies, MDPI, vol. 12(8), pages 1-25, April.
    9. Olabi, A.G. & Wilberforce, Tabbi & Abdelkareem, Mohammad Ali, 2021. "Fuel cell application in the automotive industry and future perspective," Energy, Elsevier, vol. 214(C).
    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. 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.

    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. Bizon, Nicu, 2019. "Efficient fuel economy strategies for the Fuel Cell Hybrid Power Systems under variable renewable/load power profile," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    2. Nicu Bizon & Alin Gheorghita Mazare & Laurentiu Mihai Ionescu & Phatiphat Thounthong & Erol Kurt & Mihai Oproescu & Gheorghe Serban & Ioan Lita, 2019. "Better Fuel Economy by Optimizing Airflow of the Fuel Cell Hybrid Power Systems Using Fuel Flow-Based Load-Following Control," Energies, MDPI, vol. 12(14), pages 1-17, July.
    3. Iqbal, Mehroze & Laurent, Julien & Benmouna, Amel & Becherif, Mohamed & Ramadan, Haitham S. & Claude, Frederic, 2022. "Ageing-aware load following control for composite-cost optimal energy management of fuel cell hybrid electric vehicle," Energy, Elsevier, vol. 254(PA).
    4. Nicu Bizon & Phatiphat Thounthong, 2021. "A Simple and Safe Strategy for Improving the Fuel Economy of a Fuel Cell Vehicle," Mathematics, MDPI, vol. 9(6), pages 1-29, March.
    5. Mojgan Fayyazi & Paramjotsingh Sardar & Sumit Infent Thomas & Roonak Daghigh & Ali Jamali & Thomas Esch & Hans Kemper & Reza Langari & Hamid Khayyam, 2023. "Artificial Intelligence/Machine Learning in Energy Management Systems, Control, and Optimization of Hydrogen Fuel Cell Vehicles," Sustainability, MDPI, vol. 15(6), pages 1-38, March.
    6. Liu, Zhao & Chen, Huicui & Peng, Lian & Ye, Xichen & Xu, Sichen & Zhang, Tong, 2022. "Feedforward-decoupled closed-loop fuzzy proportion-integral-derivative control of air supply system of proton exchange membrane fuel cell," Energy, Elsevier, vol. 240(C).
    7. Sun, Zhendong & Wang, Yujie & Chen, Zonghai & Li, Xiyun, 2020. "Min-max game based energy management strategy for fuel cell/supercapacitor hybrid electric vehicles," Applied Energy, Elsevier, vol. 267(C).
    8. Wei, Changyin & Chen, Yong & Li, Xiaoyu & Lin, Xiaozhe, 2022. "Integrating intelligent driving pattern recognition with adaptive energy management strategy for extender range electric logistics vehicle," Energy, Elsevier, vol. 247(C).
    9. Nicu Bizon & Valentin Alexandru Stan & Angel Ciprian Cormos, 2019. "Optimization of the Fuel Cell Renewable Hybrid Power System Using the Control Mode of the Required Load Power on the DC Bus," Energies, MDPI, vol. 12(10), pages 1-15, May.
    10. Emanuele Fedele & Luigi Pio Di Noia & Renato Rizzo, 2023. "Simple Loss Model of Battery Cables for Fast Transient Thermal Simulation," Energies, MDPI, vol. 16(7), pages 1-13, March.
    11. Zhang, Xiaoqing & Yang, Jiapei & Ma, Xiao & Zhuge, Weilin & Shuai, Shijin, 2022. "Modelling and analysis on effects of penetration of microporous layer into gas diffusion layer in PEM fuel cells: Focusing on mass transport," Energy, Elsevier, vol. 254(PA).
    12. Zheng Huang & Laisuo Su & Yunjie Yang & Linsong Gao & Xinyu Liu & Heng Huang & Yubai Li & Yongchen Song, 2023. "Three-Dimensional Simulation on the Effects of Different Parameters and Pt Loading on the Long-Term Performance of Proton Exchange Membrane Fuel Cells," Sustainability, MDPI, vol. 15(4), pages 1-22, February.
    13. Mohammed, Y.S. & Mustafa, M.W. & Bashir, N., 2013. "Status of renewable energy consumption and developmental challenges in Sub-Sahara Africa," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 453-463.
    14. Yang, Ningkang & Han, Lijin & Xiang, Changle & Liu, Hui & Li, Xunmin, 2021. "An indirect reinforcement learning based real-time energy management strategy via high-order Markov Chain model for a hybrid electric vehicle," Energy, Elsevier, vol. 236(C).
    15. Anvari-Moghaddam, Amjad & Rahimi-Kian, Ashkan & Mirian, Maryam S. & Guerrero, Josep M., 2017. "A multi-agent based energy management solution for integrated buildings and microgrid system," Applied Energy, Elsevier, vol. 203(C), pages 41-56.
    16. Najmi, Aezid-Ul-Hassan & Anyanwu, Ikechukwu S. & Xie, Xu & Liu, Zhi & Jiao, Kui, 2021. "Experimental investigation and optimization of proton exchange membrane fuel cell using different flow fields," Energy, Elsevier, vol. 217(C).
    17. Ahmed M. Nassef & Ahmed Handam, 2022. "Parameter Estimation-Based Slime Mold Algorithm of Photocatalytic Methane Reforming Process for Hydrogen Production," Sustainability, MDPI, vol. 14(5), pages 1-12, March.
    18. K. Arunprasath & S. Bathrinath & R. K. A. Bhalaji & Koppiahraj Karuppiah & Anish Nair, 2023. "An integrated approach to modelling of barriers in implementation of cellular manufacturing systems in production industries," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(4), pages 1370-1378, August.
    19. Belessiotis, George V. & Kontos, Athanassios G., 2022. "Plasmonic silver (Ag)-based photocatalysts for H2 production and CO2 conversion: Review, analysis and perspectives," Renewable Energy, Elsevier, vol. 195(C), pages 497-515.
    20. Ceran, Bartosz, 2019. "The concept of use of PV/WT/FC hybrid power generation system for smoothing the energy profile of the consumer," Energy, Elsevier, vol. 167(C), pages 853-865.

    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:14:y:2021:i:6:p:1660-:d:518874. 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.