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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
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    References listed on IDEAS

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    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.
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    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. 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).
    3. 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).
    4. 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).
    5. 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).
    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.

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