A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Advanced Search

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 15.3, Top 1 (SCI Q1)
    CiteScore: 23.5, Top 2% (Q1)
    Google Scholar h5-index: 77， TOP 5
Turn off MathJax
Article Contents
Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2024  > In Press, Accepted Manuscript
M. Chen, L. Tao, J. Lou, and X. Luo, “Latent-factorization-of-tensors-incorporated battery cycle life prediction,” IEEE/CAA J. Autom. Sinica, 2024. doi: 10.1109/JAS.2024.124602
Citation: M. Chen, L. Tao, J. Lou, and X. Luo, “Latent-factorization-of-tensors-incorporated battery cycle life prediction,” IEEE/CAA J. Autom. Sinica, 2024. doi: 10.1109/JAS.2024.124602
shu

Latent-Factorization-of-Tensors-Incorporated Battery Cycle Life Prediction

doi: 10.1109/JAS.2024.124602
More Information
    Abstract
    • FullText(HTML)
  • loading
    • References(16)
  • [1]
    Y. Wang, K. Li, and Z. Chen, “Battery full life cycle management and health prognosis based on cloud service and broad learning,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 8, pp. 1540–1542, Aug. 2022. doi: 10.1109/JAS.2022.105779
    [2]
    X. Li, Y. Xu, N. Li, B. Yang, and Y. Lei, “Remaining useful life prediction with partial sensor malfunctions using deep adversarial networks,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 121–134, Jan. 2023. doi: 10.1109/JAS.2022.105935
    [3]
    S. Bamati and H. Chaoui, “Developing an online data-driven state of health estimation of lithium-ion batteries under random sensor measurement unavailability,” IEEE Trans. Transportation Electrifi cation, vol. 9, no. 1, pp. 1128–1141, Mar. 2023. doi: 10.1109/TTE.2022.3199115
    [4]
    R. Razavi-Far, S. Chakrabarti, M. Saif, and E. Zio, “An integrated imputation-prediction scheme for prognostics of battery data with missing observations,” Expert Systems With Applications, vol. 115, pp. 709–723, Jun. 2019.
    [5]
    N. Joshi, J. Channegowda, and C. Lingaraj, “Scalable imputation technique for effective computation of missing battery parameters,” Int. J. Energy Research, vol. 46, no. 15, pp. 22439–22453, Dec. 2022. doi: 10.1002/er.8548
    [6]
    C. Liow, H. Kang, S. Kim, M. Lee, A. Baucour, K. Bang, Y. Shim, J. Choe, G. Hwang, S. Cho, G. Park, J. Yeom, J. Agar, J. Yuk, J. Shin, H. Lee, H. Byon, E. Cho, and S. Hong, “Machine learning assisted synthesis of lithium-ion batteries cathode materials,” Nano Energy, vol. 98, p. 107214, Jul. 2022.
    [7]
    J. Ma, J. Cheng, F. Jiang, W. Chen, M. Wang, and C. Zhai, “A bi-directional missing data imputation scheme based on LSTM and transfer learning for building energy data,” Energy & Buildings, no. 216, p. 109941, Mar. 2020.
    [8]
    K. Liu, Z. Wei, C. Zhang, Y. Shang, R, Teodorescu, and Q. Han, “Towards long lifetime battery: AI-based manufacturing and management,” IEEE/CAA J. Autom.Sinica, vol. 9, no. 7, pp. 1139–1165, Jul. 2022. doi: 10.1109/JAS.2022.105599
    [9]
    H. Wu, X. Luo, M. Zhou, M. Rawa, K. Sedraoui, and A. Albeshri, “A PID-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 3, pp. 533–546, Mar. 2022. doi: 10.1109/JAS.2021.1004308
    [10]
    M. Chen, C. He, and X. Luo, “MNL: A highly-efficient model for large-scale dynamic weighted directed network representation,” IEEE Trans. Big Data, vol. 9, no. 3, pp. 889–903, Jun. 2023.
    [11]
    D. Wu and X. Luo, “Robust latent factor analysis for precise representation of high-dimensional and sparse data,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 4, pp. 796–805, Apr. 2021. doi: 10.1109/JAS.2020.1003533
    [12]
    X. Luo, Z. Wang, and M. Shang, “An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high dimensional and sparse data,” IEEE Trans. Syst. Man Cybern., vol. 51, no. 6, pp. 3522–3532, 2021.
    [13]
    K. A. Severson, P. M. Attia, N. Jin, N. Perkins, B. Jiang, Z. Yang, and R. D. Braatz, “Data-driven prediction of battery cycle life before capacity degradation,” Nature Energy, vol. 4, no. 5, pp. 383–391, 2019. doi: 10.1038/s41560-019-0356-8
    [14]
    Y. Luo and K. Lu, “An online state of health estimation technique for lithium-ion battery using artificial neural network and linear interpolation,” J. Energy Storage, vol. 52, p. 105062, 2022.
    [15]
    H. Zhang, Y. Su, F. Altafm, T. Wik, and S. Gros, “Interpretable battery cycle life range prediction using early cell degradation data, ” IEEE Trans. Transportation Electrification, vol. 9, no. 2, Jun. 2023.
    [16]
    S. Saxena, L. Ward, J. Kubal, W. Lu, S. Babinec, and N. Paulson, “A convolutional neural network model for battery capacity fade curve prediction using early life data,” J. Power Sources, vol. 542, p. 231736, Aug. 2022. doi: 10.1016/j.jpowsour.2022.231736
    • Related Articles
    • Supplements(0)

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(4)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (4) PDF downloads(3) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return