IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i2p507-d483084.html
   My bibliography  Save this article

Hybridizing Lead–Acid Batteries with Supercapacitors: A Methodology

Author

Listed:
  • Xi Luo

    (Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK)

  • Jorge Varela Barreras

    (Department of Mechanical Engineering, Imperial College London, London SW7 2AZ, UK)

  • Clementine L. Chambon

    (Department of Chemical Engineering, Imperial College London, London SW7 2AZ, UK)

  • Billy Wu

    (Dyson School of Design Engineering, Imperial College London, London SW7 2AZ, UK)

  • Efstratios Batzelis

    (Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK)

Abstract

Hybridizing a lead–acid battery energy storage system (ESS) with supercapacitors is a promising solution to cope with the increased battery degradation in standalone microgrids that suffer from irregular electricity profiles. There are many studies in the literature on such hybrid energy storage systems (HESS), usually examining the various hybridization aspects separately. This paper provides a holistic look at the design of an HESS. A new control scheme is proposed that applies power filtering to smooth out the battery profile, while strictly adhering to the supercapacitors’ voltage limits. A new lead–acid battery model is introduced, which accounts for the combined effects of a microcycle’s depth of discharge (DoD) and battery temperature, usually considered separately in the literature. Furthermore, a sensitivity analysis on the thermal parameters and an economic analysis were performed using a 90-day electricity profile from an actual DC microgrid in India to infer the hybridization benefit. The results show that the hybridization is beneficial mainly at poor thermal conditions and highlight the need for a battery degradation model that considers both the DoD effect with microcycle resolution and temperate impact to accurately assess the gain from such a hybridization.

Suggested Citation

  • Xi Luo & Jorge Varela Barreras & Clementine L. Chambon & Billy Wu & Efstratios Batzelis, 2021. "Hybridizing Lead–Acid Batteries with Supercapacitors: A Methodology," Energies, MDPI, vol. 14(2), pages 1-27, January.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:2:p:507-:d:483084
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/2/507/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/2/507/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Pinto, Cláudio & Barreras, Jorge V. & de Castro, Ricardo & Araújo, Rui Esteves & Schaltz, Erik, 2017. "Study on the combined influence of battery models and sizing strategy for hybrid and battery-based electric vehicles," Energy, Elsevier, vol. 137(C), pages 272-284.
    2. Kuperman, Alon & Aharon, Ilan, 2011. "Battery-ultracapacitor hybrids for pulsed current loads: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(2), pages 981-992, February.
    3. Li, Shuangqi & He, Hongwen & Su, Chang & Zhao, Pengfei, 2020. "Data driven battery modeling and management method with aging phenomenon considered," Applied Energy, Elsevier, vol. 275(C).
    4. Mellit, A. & Benghanem, M. & Kalogirou, S.A., 2007. "Modeling and simulation of a stand-alone photovoltaic system using an adaptive artificial neural network: Proposition for a new sizing procedure," Renewable Energy, Elsevier, vol. 32(2), pages 285-313.
    5. Narayan, Nishant & Papakosta, Thekla & Vega-Garita, Victor & Qin, Zian & Popovic-Gerber, Jelena & Bauer, Pavol & Zeman, Miroslav, 2018. "Estimating battery lifetimes in Solar Home System design using a practical modelling methodology," Applied Energy, Elsevier, vol. 228(C), pages 1629-1639.
    6. Zhang, Shuo & Xiong, Rui & Sun, Fengchun, 2017. "Model predictive control for power management in a plug-in hybrid electric vehicle with a hybrid energy storage system," Applied Energy, Elsevier, vol. 185(P2), pages 1654-1662.
    7. Han, Xiaojuan & Liang, Yubo & Ai, Yaoyao & Li, Jianlin, 2018. "Economic evaluation of a PV combined energy storage charging station based on cost estimation of second-use batteries," Energy, Elsevier, vol. 165(PA), pages 326-339.
    8. Arun Mambazhasseri Divakaran & Dean Hamilton & Krishna Nama Manjunatha & Manickam Minakshi, 2020. "Design, Development and Thermal Analysis of Reusable Li-Ion Battery Module for Future Mobile and Stationary Applications," Energies, MDPI, vol. 13(6), pages 1-22, March.
    9. Dufo-López, Rodolfo & Lujano-Rojas, Juan M. & Bernal-Agustín, José L., 2014. "Comparison of different lead–acid battery lifetime prediction models for use in simulation of stand-alone photovoltaic systems," Applied Energy, Elsevier, vol. 115(C), pages 242-253.
    Full references (including those not matched with items on IDEAS)

    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. Rawat, Rahul & Kaushik, S.C. & Lamba, Ravita, 2016. "A review on modeling, design methodology and size optimization of photovoltaic based water pumping, standalone and grid connected system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1506-1519.
    2. da Silva, Samuel Filgueira & Eckert, Jony Javorski & Corrêa, Fernanda Cristina & Silva, Fabrício Leonardo & Silva, Ludmila C.A. & Dedini, Franco Giuseppe, 2022. "Dual HESS electric vehicle powertrain design and fuzzy control based on multi-objective optimization to increase driving range and battery life cycle," Applied Energy, Elsevier, vol. 324(C).
    3. Jing, Wenlong & Lai, Chean Hung & Wong, Wallace S.H. & Wong, M.L. Dennis, 2018. "A comprehensive study of battery-supercapacitor hybrid energy storage system for standalone PV power system in rural electrification," Applied Energy, Elsevier, vol. 224(C), pages 340-356.
    4. Meng, Jinhao & Cai, Lei & Stroe, Daniel-Ioan & Ma, Junpeng & Luo, Guangzhao & Teodorescu, Remus, 2020. "An optimized ensemble learning framework for lithium-ion Battery State of Health estimation in energy storage system," Energy, Elsevier, vol. 206(C).
    5. Fang Zhou & Feng Xiao & Cheng Chang & Yulong Shao & Chuanxue Song, 2017. "Adaptive Model Predictive Control-Based Energy Management for Semi-Active Hybrid Energy Storage Systems on Electric Vehicles," Energies, MDPI, vol. 10(7), pages 1-21, July.
    6. Barelli, L. & Bidini, G. & Bonucci, F. & Castellini, L. & Fratini, A. & Gallorini, F. & Zuccari, A., 2019. "Flywheel hybridization to improve battery life in energy storage systems coupled to RES plants," Energy, Elsevier, vol. 173(C), pages 937-950.
    7. Ghorbanzadeh, Milad & Astaneh, Majid & Golzar, Farzin, 2019. "Long-term degradation based analysis for lithium-ion batteries in off-grid wind-battery renewable energy systems," Energy, Elsevier, vol. 166(C), pages 1194-1206.
    8. Díaz-González, Francisco & Sumper, Andreas & Gomis-Bellmunt, Oriol & Villafáfila-Robles, Roberto, 2012. "A review of energy storage technologies for wind power applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(4), pages 2154-2171.
    9. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Yang, Xiao & Fernandez, Carlos, 2023. "A hybrid probabilistic correction model for the state of charge estimation of lithium-ion batteries considering dynamic currents and temperatures," Energy, Elsevier, vol. 273(C).
    10. Wang, Bin & Xu, Jun & Cao, Binggang & Ning, Bo, 2017. "Adaptive mode switch strategy based on simulated annealing optimization of a multi-mode hybrid energy storage system for electric vehicles," Applied Energy, Elsevier, vol. 194(C), pages 596-608.
    11. Duan, Ditao & Poursoleiman, Roza, 2021. "Modified teaching-learning-based optimization by orthogonal learning for optimal design of an electric vehicle charging station," Utilities Policy, Elsevier, vol. 72(C).
    12. Mohamed Ali Zdiri & Tawfik Guesmi & Badr M. Alshammari & Khalid Alqunun & Abdulaziz Almalaq & Fatma Ben Salem & Hsan Hadj Abdallah & Ahmed Toumi, 2022. "Design and Analysis of Sliding-Mode Artificial Neural Network Control Strategy for Hybrid PV-Battery-Supercapacitor System," Energies, MDPI, vol. 15(11), pages 1-20, June.
    13. Muhammad Khalid, 2019. "A Review on the Selected Applications of Battery-Supercapacitor Hybrid Energy Storage Systems for Microgrids," Energies, MDPI, vol. 12(23), pages 1-34, November.
    14. Dong Yu & Weiming Zhang & Jianlin Li & Weilin Yang & Dezhi Xu, 2020. "Disturbance Observer-Based Prescribed Performance Fault-Tolerant Control for a Multi-Area Interconnected Power System with a Hybrid Energy Storage System," Energies, MDPI, vol. 13(5), pages 1-15, March.
    15. Zhu, Tao & Wills, Richard G.A. & Lot, Roberto & Ruan, Haijun & Jiang, Zhihao, 2021. "Adaptive energy management of a battery-supercapacitor energy storage system for electric vehicles based on flexible perception and neural network fitting," Applied Energy, Elsevier, vol. 292(C).
    16. F. Isorna Llerena & E. López González & J. J. Caparrós Mancera & F. Segura Manzano & J. M. Andújar, 2021. "Hydrogen vs. Battery-Based Propulsion Systems in Unipersonal Vehicles—Developing Solutions to Improve the Sustainability of Urban Mobility," Sustainability, MDPI, vol. 13(10), pages 1-16, May.
    17. Pinto, Edwin S. & Serra, Luis M. & Lázaro, Ana, 2020. "Evaluation of methods to select representative days for the optimization of polygeneration systems," Renewable Energy, Elsevier, vol. 151(C), pages 488-502.
    18. Zhou, Yuekuan & Cao, Sunliang & Hensen, Jan L.M., 2021. "An energy paradigm transition framework from negative towards positive district energy sharing networks—Battery cycling aging, advanced battery management strategies, flexible vehicles-to-buildings in," Applied Energy, Elsevier, vol. 288(C).
    19. Hai Lan & Jinfeng Dai & Shuli Wen & Ying-Yi Hong & David C. Yu & Yifei Bai, 2015. "Optimal Tilt Angle of Photovoltaic Arrays and Economic Allocation of Energy Storage System on Large Oil Tanker Ship," Energies, MDPI, vol. 8(10), pages 1-16, October.
    20. Pedrayes, Joaquín F. & Melero, Manuel G. & Cano, Jose M. & Norniella, Joaquín G. & Duque, Salvador B. & Rojas, Carlos H. & Orcajo, Gonzalo A., 2021. "Lambert W function based closed-form expressions of supercapacitor electrical variables in constant power applications," Energy, Elsevier, vol. 218(C).

    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:2:p:507-:d:483084. 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.