IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i24p6385-d1547121.html
   My bibliography  Save this article

SOC Equalization Control Method Considering SOH in DC–DC Converter Cascaded Energy Storage Systems

Author

Listed:
  • Shixian Bai

    (School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China)

  • Xiangqian Tong

    (School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China)

  • Xin Ma

    (Xi’an Weiguang Energy Technology Co., Ltd., Xi’an 710075, China)

  • Jie Zhou

    (School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China)

Abstract

In large-scale industrial and commercial energy storage systems, as well as ground power station energy storage systems, the trend is to adopt large-capacity battery cells to reduce system construction costs. It is essential to screen the consistency of battery cells during the initial design phase. In conventional energy storage systems, battery clusters utilize multiple batteries connected in series, which can lead to differential attenuation over time and inconsistent state of charge (SOC) among the batteries. The “barrel effect” diminishes the effective capacity of the energy storage system. To mitigate this issue, a DC–DC converter cascaded energy storage system has been developed, incorporating precise charge and discharge management for each battery module within a cluster. By implementing SOC equalization control at the module level, it mitigates the barrel effect and enables full utilization of each battery module’s charging and discharging capabilities, thereby enhancing the overall charge–discharge capacity of the energy storage system. However, when considering only the SOC equalizing factor, its effectiveness may be limited by constraints such as DC–DC converter power limitations and device voltage stress levels. Therefore, a novel SOC equalization control method that considers both SOH and SOC variations across battery modules is proposed here. Through a droop control methodology combined with closed-loop control implementation on eight DC–DC converter cascaded energy storage systems, we validate the improved effectiveness achieved by incorporating SOH-aware SOC equalization control. The energy storage system has the capability to enhance both charging and discharging capacities, achieving a remarkable increase of 1.85% every 10 min, thereby yielding significant economic advantages.

Suggested Citation

  • Shixian Bai & Xiangqian Tong & Xin Ma & Jie Zhou, 2024. "SOC Equalization Control Method Considering SOH in DC–DC Converter Cascaded Energy Storage Systems," Energies, MDPI, vol. 17(24), pages 1-16, December.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:24:p:6385-:d:1547121
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/24/6385/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/24/6385/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chaoran Li & Fei Xiao & Yaxiang Fan, 2019. "An Approach to State of Charge Estimation of Lithium-Ion Batteries Based on Recurrent Neural Networks with Gated Recurrent Unit," Energies, MDPI, vol. 12(9), pages 1-22, April.
    2. Nikolay Vikhorev & Andrey Kurkin & Dmitriy Aleshin & Danil Ulyanov & Maksim Konstantinov & Andrey Shalukho, 2023. "Battery Dynamic Balancing Method Based on Calculation of Cell Voltage Reference Value," Energies, MDPI, vol. 16(9), pages 1-17, April.
    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. Shehzar Shahzad Sheikh & Mahnoor Anjum & Muhammad Abdullah Khan & Syed Ali Hassan & Hassan Abdullah Khalid & Adel Gastli & Lazhar Ben-Brahim, 2020. "A Battery Health Monitoring Method Using Machine Learning: A Data-Driven Approach," Energies, MDPI, vol. 13(14), pages 1-16, July.
    2. Wenxian Duan & Chuanxue Song & Silun Peng & Feng Xiao & Yulong Shao & Shixin Song, 2020. "An Improved Gated Recurrent Unit Network Model for State-of-Charge Estimation of Lithium-Ion Battery," Energies, MDPI, vol. 13(23), pages 1-19, December.
    3. Li, Hao & Fu, Lijun & Long, Xinlin & Liu, Lang & Zeng, Ziqing, 2024. "A hybrid deep learning model for lithium-ion batteries state of charge estimation based on quantile regression and attention," Energy, Elsevier, vol. 294(C).
    4. Chen, Zheng & Zhao, Hongqian & Shu, Xing & Zhang, Yuanjian & Shen, Jiangwei & Liu, Yonggang, 2021. "Synthetic state of charge estimation for lithium-ion batteries based on long short-term memory network modeling and adaptive H-Infinity filter," Energy, Elsevier, vol. 228(C).
    5. Panagiotis Eleftheriadis & Spyridon Giazitzis & Sonia Leva & Emanuele Ogliari, 2023. "Data-Driven Methods for the State of Charge Estimation of Lithium-Ion Batteries: An Overview," Forecasting, MDPI, vol. 5(3), pages 1-24, September.
    6. Li, Zongxiang & Li, Liwei & Chen, Jing & Wang, Dongqing, 2024. "A multi-head attention mechanism aided hybrid network for identifying batteries’ state of charge," Energy, Elsevier, vol. 286(C).
    7. Giacomo Talluri & Gabriele Maria Lozito & Francesco Grasso & Carlos Iturrino Garcia & Antonio Luchetta, 2021. "Optimal Battery Energy Storage System Scheduling within Renewable Energy Communities," Energies, MDPI, vol. 14(24), pages 1-23, December.
    8. Bian, Chong & He, Huoliang & Yang, Shunkun, 2020. "Stacked bidirectional long short-term memory networks for state-of-charge estimation of lithium-ion batteries," Energy, Elsevier, vol. 191(C).
    9. S. Tamilselvi & S. Gunasundari & N. Karuppiah & Abdul Razak RK & S. Madhusudan & Vikas Madhav Nagarajan & T. Sathish & Mohammed Zubair M. Shamim & C. Ahamed Saleel & Asif Afzal, 2021. "A Review on Battery Modelling Techniques," Sustainability, MDPI, vol. 13(18), pages 1-26, September.
    10. Shuo Sun & Qianli Zhang & Junzhong Sun & Wei Cai & Zhiyong Zhou & Zhanlu Yang & Zongliang Wang, 2022. "Lead–Acid Battery SOC Prediction Using Improved AdaBoost Algorithm," Energies, MDPI, vol. 15(16), pages 1-20, August.
    11. Chen, Junxiong & Feng, Xiong & Jiang, Lin & Zhu, Qiao, 2021. "State of charge estimation of lithium-ion battery using denoising autoencoder and gated recurrent unit recurrent neural network," Energy, Elsevier, vol. 227(C).
    12. Marat Sadykov & Sam Haines & Mark Broadmeadow & Geoff Walker & David William Holmes, 2023. "Practical Evaluation of Lithium-Ion Battery State-of-Charge Estimation Using Time-Series Machine Learning for Electric Vehicles," Energies, MDPI, vol. 16(4), pages 1-34, February.
    13. Yang, Kuo & Tang, Yugui & Zhang, Shujing & Zhang, Zhen, 2022. "A deep learning approach to state of charge estimation of lithium-ion batteries based on dual-stage attention mechanism," Energy, Elsevier, vol. 244(PB).
    14. Kurucan, Mehmet & Özbaltan, Mete & Yetgin, Zeki & Alkaya, Alkan, 2024. "Applications of artificial neural network based battery management systems: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    15. Liu, Mengmeng & Xu, Jun & Jiang, Yihui & Mei, Xuesong, 2023. "Multi-dimensional features based data-driven state of charge estimation method for LiFePO4 batteries," Energy, Elsevier, vol. 274(C).
    16. Cai, Nian & Que, Xiaoping & Zhang, Xu & Feng, Weiguo & Zhou, Yinghong, 2024. "A deep learning framework for the joint prediction of the SOH and RUL of lithium-ion batteries based on bimodal images," Energy, Elsevier, vol. 302(C).
    17. Xie, Yanxin & Wang, Shunli & Zhang, Gexiang & Fan, Yongcun & Fernandez, Carlos & Blaabjerg, Frede, 2023. "Optimized multi-hidden layer long short-term memory modeling and suboptimal fading extended Kalman filtering strategies for the synthetic state of charge estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 336(C).
    18. Biao Yang & Yinshuang Wang & Yuedong Zhan, 2022. "Lithium Battery State-of-Charge Estimation Based on a Bayesian Optimization Bidirectional Long Short-Term Memory Neural Network," Energies, MDPI, vol. 15(13), pages 1-18, June.
    19. Siyi Tao & Bo Jiang & Xuezhe Wei & Haifeng Dai, 2023. "A Systematic and Comparative Study of Distinct Recurrent Neural Networks for Lithium-Ion Battery State-of-Charge Estimation in Electric Vehicles," Energies, MDPI, vol. 16(4), pages 1-17, February.
    20. Xinyu Gu & KW See & Yunpeng Wang & Liang Zhao & Wenwen Pu, 2021. "The Sliding Window and SHAP Theory—An Improved System with a Long Short-Term Memory Network Model for State of Charge Prediction in Electric Vehicle Application," Energies, MDPI, vol. 14(12), pages 1-15, June.

    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:17:y:2024:i:24:p:6385-:d:1547121. 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.