IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i1p125-d125505.html
   My bibliography  Save this article

Cloud-Based Battery Condition Monitoring and Fault Diagnosis Platform for Large-Scale Lithium-Ion Battery Energy Storage Systems

Author

Listed:
  • Taesic Kim

    (Department of Electrical Engineering and Computer Science, Texas A&M University-Kingsville, MSC 192, 700 University Blvd, Kingsville, TX 78363, USA)

  • Darshan Makwana

    (Department of Electrical Engineering and Computer Science, Texas A&M University-Kingsville, MSC 192, 700 University Blvd, Kingsville, TX 78363, USA)

  • Amit Adhikaree

    (Department of Electrical Engineering and Computer Science, Texas A&M University-Kingsville, MSC 192, 700 University Blvd, Kingsville, TX 78363, USA)

  • Jitendra Shamjibhai Vagdoda

    (Department of Electrical Engineering and Computer Science, Texas A&M University-Kingsville, MSC 192, 700 University Blvd, Kingsville, TX 78363, USA)

  • Young Lee

    (Department of Electrical Engineering and Computer Science, Texas A&M University-Kingsville, MSC 192, 700 University Blvd, Kingsville, TX 78363, USA)

Abstract

Performance of the current battery management systems is limited by the on-board embedded systems as the number of battery cells increases in the large-scale lithium-ion (Li-ion) battery energy storage systems (BESSs). Moreover, an expensive supervisory control and data acquisition system is still required for maintenance of the large-scale BESSs. This paper proposes a new cloud-based battery condition monitoring and fault diagnosis platform for the large-scale Li-ion BESSs. The proposed cyber-physical platform incorporates the Internet of Things embedded in the battery modules and the cloud battery management platform. Multithreads of a condition monitoring algorithm and an outlier mining-based battery fault diagnosis algorithm are built in the cloud battery management platform (CBMP). The proposed cloud-based condition monitoring and fault diagnosis platform is validated by using a cyber-physical testbed and a computational cost analysis for the CBMP. Therefore, the proposed platform will support the on-board health monitoring and provide an intelligent and cost-effective maintenance of the large-scale Li-ion BESSs.

Suggested Citation

  • Taesic Kim & Darshan Makwana & Amit Adhikaree & Jitendra Shamjibhai Vagdoda & Young Lee, 2018. "Cloud-Based Battery Condition Monitoring and Fault Diagnosis Platform for Large-Scale Lithium-Ion Battery Energy Storage Systems," Energies, MDPI, vol. 11(1), pages 1-15, January.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:1:p:125-:d:125505
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/1/125/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/1/125/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Changwen Zheng & Yunlong Ge & Ziqiang Chen & Deyang Huang & Jian Liu & Shiyao Zhou, 2017. "Diagnosis Method for Li-Ion Battery Fault Based on an Adaptive Unscented Kalman Filter," Energies, MDPI, vol. 10(11), pages 1-14, November.
    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. Zhang, Guangxu & Wei, Xuezhe & Tang, Xuan & Zhu, Jiangong & Chen, Siqi & Dai, Haifeng, 2021. "Internal short circuit mechanisms, experimental approaches and detection methods of lithium-ion batteries for electric vehicles: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    2. Dai, Haifeng & Jiang, Bo & Hu, Xiaosong & Lin, Xianke & Wei, Xuezhe & Pecht, Michael, 2021. "Advanced battery management strategies for a sustainable energy future: Multilayer design concepts and research trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    3. Xiong, Rui & Sun, Wanzhou & Yu, Quanqing & Sun, Fengchun, 2020. "Research progress, challenges and prospects of fault diagnosis on battery system of electric vehicles," Applied Energy, Elsevier, vol. 279(C).
    4. Su, Shaosen & Li, Wei & Garg, Akhil & Gao, Liang, 2022. "An adaptive boosting charging strategy optimization based on thermoelectric-aging model, surrogates and multi-objective optimization," Applied Energy, Elsevier, vol. 312(C).
    5. Lin, Yu-Hsiu & Shen, Ting-Yu, 2023. "Novel cell screening and prognosing based on neurocomputing-based multiday-ahead time-series forecasting for predictive maintenance of battery modules in frequency regulation-energy storage systems," Applied Energy, Elsevier, vol. 351(C).
    6. Jiong Yang & Fanyong Cheng & Maxwell Duodu & Miao Li & Chao Han, 2022. "High-Precision Fault Detection for Electric Vehicle Battery System Based on Bayesian Optimization SVDD," Energies, MDPI, vol. 15(22), pages 1-20, 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. Xiong, Rui & Sun, Wanzhou & Yu, Quanqing & Sun, Fengchun, 2020. "Research progress, challenges and prospects of fault diagnosis on battery system of electric vehicles," Applied Energy, Elsevier, vol. 279(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:11:y:2018:i:1:p:125-:d:125505. 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.