IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i17p4577-d1736924.html
   My bibliography  Save this article

SOC Balancing Control Strategy for Multiple Storage Units Based on Battery Life Degradation Characteristics

Author

Listed:
  • Guiquan Chen

    (State Key Laboratory of Disaster Prevention & Reduction for Power Grid, Changsha University of Science & Technology, Changsha 410114, China)

  • Xiangyang Xia

    (State Key Laboratory of Disaster Prevention & Reduction for Power Grid, Changsha University of Science & Technology, Changsha 410114, China)

  • Dan Lu

    (State Key Laboratory of Disaster Prevention & Reduction for Power Grid, Changsha University of Science & Technology, Changsha 410114, China)

  • Ting Ouyang

    (Nanning Power Supply Bureau of Guangxi Power Grid Co., Ltd., Nanning 530023, China)

  • Xiaoyue Zhao

    (State Key Laboratory of Disaster Prevention & Reduction for Power Grid, Changsha University of Science & Technology, Changsha 410114, China)

  • Nanlan Wang

    (School of Computer and Electrical Engineering, Hunan University of Arts and Science, Changde 415000, China)

  • Naitong Liu

    (State Key Laboratory of Disaster Prevention & Reduction for Power Grid, Changsha University of Science & Technology, Changsha 410114, China)

  • Xianliang Luo

    (State Key Laboratory of Disaster Prevention & Reduction for Power Grid, Changsha University of Science & Technology, Changsha 410114, China)

  • Yichong Luo

    (State Key Laboratory of Disaster Prevention & Reduction for Power Grid, Changsha University of Science & Technology, Changsha 410114, China)

Abstract

To resolve the issue of state of charge (SOC) inconsistency among energy storage units under traditional equal-power allocation strategies, this paper proposes a multi-unit SOC balancing control strategy based on battery life degradation characteristics. Prior to system operation, the proposed strategy optimizes power distribution according to each unit’s state of health (SOH) and predefined depth of discharge (DOD), ensuring SOC balance at the end of each charge–discharge cycle. Simulation and experimental results demonstrate that, compared with traditional equal-power distribution control, the proposed strategy significantly improves capacity utilization and extends the overall system lifetime. For instance, in Simulation Scenario 1, the available capacity per cycle is increased by 8.14%, and the overall system lifetime is prolonged by 11.04%. Furthermore, the strategy eliminates the need for dynamic power redistribution, thus reducing communication overheads and effectively meeting engineering requirements for SOC balancing. This research provides valuable insights for the safe and economical operation of energy storage power stations.

Suggested Citation

  • Guiquan Chen & Xiangyang Xia & Dan Lu & Ting Ouyang & Xiaoyue Zhao & Nanlan Wang & Naitong Liu & Xianliang Luo & Yichong Luo, 2025. "SOC Balancing Control Strategy for Multiple Storage Units Based on Battery Life Degradation Characteristics," Energies, MDPI, vol. 18(17), pages 1-19, August.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:17:p:4577-:d:1736924
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/17/4577/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/17/4577/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Omar, Noshin & Monem, Mohamed Abdel & Firouz, Yousef & Salminen, Justin & Smekens, Jelle & Hegazy, Omar & Gaulous, Hamid & Mulder, Grietus & Van den Bossche, Peter & Coosemans, Thierry & Van Mierlo, J, 2014. "Lithium iron phosphate based battery – Assessment of the aging parameters and development of cycle life model," Applied Energy, Elsevier, vol. 113(C), pages 1575-1585.
    2. Guo, Fei & Wu, Xiongwei & Liu, Lili & Ye, Jilei & Wang, Tao & Fu, Lijun & Wu, Yuping, 2023. "Prediction of remaining useful life and state of health of lithium batteries based on time series feature and Savitzky-Golay filter combined with gated recurrent unit neural network," Energy, Elsevier, vol. 270(C).
    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. Berecibar, M. & Gandiaga, I. & Villarreal, I. & Omar, N. & Van Mierlo, J. & Van den Bossche, P., 2016. "Critical review of state of health estimation methods of Li-ion batteries for real applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 572-587.
    2. Onur Yuksel & Eduardo Blanco-Davis & Andrew Spiteri & David Hitchmough & Viknash Shagar & Maria Carmela Di Piazza & Marcello Pucci & Nikolaos Tsoulakos & Milad Armin & Jin Wang, 2025. "Optimising the Design of a Hybrid Fuel Cell/Battery and Waste Heat Recovery System for Retrofitting Ship Power Generation," Energies, MDPI, vol. 18(2), pages 1-40, January.
    3. Ester Vasta & Tommaso Scimone & Giovanni Nobile & Otto Eberhardt & Daniele Dugo & Massimiliano Maurizio De Benedetti & Luigi Lanuzza & Giuseppe Scarcella & Luca Patanè & Paolo Arena & Mario Cacciato, 2023. "Models for Battery Health Assessment: A Comparative Evaluation," Energies, MDPI, vol. 16(2), pages 1-34, January.
    4. Caleb Scarlett & Vivek Utgikar, 2025. "Exploring the Material Feasibility of a LiFePO 4 -Based Energy Storage System," Energies, MDPI, vol. 18(15), pages 1-15, August.
    5. Sun, Li & Walker, Paul & Feng, Kaiwu & Zhang, Nong, 2018. "Multi-objective component sizing for a battery-supercapacitor power supply considering the use of a power converter," Energy, Elsevier, vol. 142(C), pages 436-446.
    6. Ma, Jian & Xu, Shu & Shang, Pengchao & ding, Yu & Qin, Weili & Cheng, Yujie & Lu, Chen & Su, Yuzhuan & Chong, Jin & Jin, Haizu & Lin, Yongshou, 2020. "Cycle life test optimization for different Li-ion power battery formulations using a hybrid remaining-useful-life prediction method," Applied Energy, Elsevier, vol. 262(C).
    7. Li, Xiaopeng & Zhao, Minghang & Zhong, Shisheng & Li, Junfu & Fu, Song & Yan, Zhiqi, 2024. "BMSFormer: An efficient deep learning model for online state-of-health estimation of lithium-ion batteries under high-frequency early SOC data with strong correlated single health indicator," Energy, Elsevier, vol. 313(C).
    8. Danijel Pavković & Mihael Cipek & Zdenko Kljaić & Tomislav Josip Mlinarić & Mario Hrgetić & Davor Zorc, 2018. "Damping Optimum-Based Design of Control Strategy Suitable for Battery/Ultracapacitor Electric Vehicles," Energies, MDPI, vol. 11(10), pages 1-26, October.
    9. Shovon Goutam & Jean-Marc Timmermans & Noshin Omar & Peter Van den Bossche & Joeri Van Mierlo, 2015. "Comparative Study of Surface Temperature Behavior of Commercial Li-Ion Pouch Cells of Different Chemistries and Capacities by Infrared Thermography," Energies, MDPI, vol. 8(8), pages 1-18, August.
    10. Jiaming Zhou & Chunxiao Feng & Qingqing Su & Shangfeng Jiang & Zhixian Fan & Jiageng Ruan & Shikai Sun & Leli Hu, 2022. "The Multi-Objective Optimization of Powertrain Design and Energy Management Strategy for Fuel Cell–Battery Electric Vehicle," Sustainability, MDPI, vol. 14(10), pages 1-19, May.
    11. Tao, Laifa & Cheng, Yujie & Lu, Chen & Su, Yuzhuan & Chong, Jin & Jin, Haizu & Lin, Yongshou & Noktehdan, Azadeh, 2017. "Lithium-ion battery capacity fading dynamics modelling for formulation optimization: A stochastic approach to accelerate the design process," Applied Energy, Elsevier, vol. 202(C), pages 138-152.
    12. Brinkel, N.B.G. & Schram, W.L. & AlSkaif, T.A. & Lampropoulos, I. & van Sark, W.G.J.H.M., 2020. "Should we reinforce the grid? Cost and emission optimization of electric vehicle charging under different transformer limits," Applied Energy, Elsevier, vol. 276(C).
    13. Liao, Xiaolin & Sun, Peiyi & Xu, Mengqing & Xing, Lidan & Liao, Youhao & Zhang, Liping & Yu, Le & Fan, Weizhen & Li, Weishan, 2016. "Application of tris(trimethylsilyl)borate to suppress self-discharge of layered nickel cobalt manganese oxide for high energy battery," Applied Energy, Elsevier, vol. 175(C), pages 505-511.
    14. Hou, Jun & Song, Ziyou & Park, Hyeongjun & Hofmann, Heath & Sun, Jing, 2018. "Implementation and evaluation of real-time model predictive control for load fluctuations mitigation in all-electric ship propulsion systems," Applied Energy, Elsevier, vol. 230(C), pages 62-77.
    15. Bingxiang Sun & Xianjie Qi & Donglin Song & Haijun Ruan, 2023. "Review of Low-Temperature Performance, Modeling and Heating for Lithium-Ion Batteries," Energies, MDPI, vol. 16(20), pages 1-37, October.
    16. Tian, Zhaoming & Cao, Xiaoyu & Zeng, Bo & Guan, Xiaohong, 2025. "Adaptive infinite-horizon control of hybrid EV/FCEV charging hubs: A large-model based deep reinforcement learning approach," Applied Energy, Elsevier, vol. 390(C).
    17. Lyons, P.F. & Wade, N.S. & Jiang, T. & Taylor, P.C. & Hashiesh, F. & Michel, M. & Miller, D., 2015. "Design and analysis of electrical energy storage demonstration projects on UK distribution networks," Applied Energy, Elsevier, vol. 137(C), pages 677-691.
    18. Lybbert, M. & Ghaemi, Z. & Balaji, A.K. & Warren, R., 2021. "Integrating life cycle assessment and electrochemical modeling to study the effects of cell design and operating conditions on the environmental impacts of lithium-ion batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    19. Ruan, Jiageng & Walker, Paul D. & Watterson, Peter A. & Zhang, Nong, 2016. "The dynamic performance and economic benefit of a blended braking system in a multi-speed battery electric vehicle," Applied Energy, Elsevier, vol. 183(C), pages 1240-1258.
    20. Shengjin Tang & Chuanqiang Yu & Xue Wang & Xiaosong Guo & Xiaosheng Si, 2014. "Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Wiener Process with Measurement Error," Energies, MDPI, vol. 7(2), pages 1-28, January.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:18:y:2025:i:17:p:4577-:d:1736924. 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.