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A Novel Construction Method and Prediction Framework of Periodic Time Series: Application to State of Health Prediction of Lithium-Ion Batteries

Author

Listed:
  • Chunsheng Cui

    (School of Electrical and Control Engineering, North University of China, Taiyuan 030051, China)

  • Guangshu Xia

    (School of Electrical and Control Engineering, North University of China, Taiyuan 030051, China)

  • Chenyu Jia

    (School of Electrical and Control Engineering, North University of China, Taiyuan 030051, China)

  • Jie Wen

    (School of Electrical and Control Engineering, North University of China, Taiyuan 030051, China)

Abstract

Due to the time property of natural phenomena and human activities, time series are very common in our lives. The analysis and study of time series can help us to better understand the world, predict the future and make scientific decisions. Focusing on time series prediction, in this paper we propose a method of constructing non-periodic time series into periodic time series and design a framework for time series prediction based on the constructed periodic time series. The proposed construction method and prediction framework for the periodic time series are then applied to predict the state of health (SOH) of lithium-ion (Li-ion) batteries. The effectiveness of the proposed approach is verified and evaluated on publicly available datasets from the National Aeronautics and Space Administration (NASA), Ames Prognostics Center of Excellence (PCoE), and Center for Advanced Life Cycle Engineering (CALCE) of University of Maryland. The experimental results show that the early SOH prediction of Li-ion batteries can be improved by at least one order of magnitude on both the NASA and CALCE battery datasets when using the method proposed in this paper.

Suggested Citation

  • Chunsheng Cui & Guangshu Xia & Chenyu Jia & Jie Wen, 2025. "A Novel Construction Method and Prediction Framework of Periodic Time Series: Application to State of Health Prediction of Lithium-Ion Batteries," Energies, MDPI, vol. 18(6), pages 1-21, March.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:6:p:1438-:d:1612451
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    References listed on IDEAS

    as
    1. Lin, Chun-Pang & Cabrera, Javier & Yang, Fangfang & Ling, Man-Ho & Tsui, Kwok-Leung & Bae, Suk-Joo, 2020. "Battery state of health modeling and remaining useful life prediction through time series model," Applied Energy, Elsevier, vol. 275(C).
    2. Wang, Jian Qi & Du, Yu & Wang, Jing, 2020. "LSTM based long-term energy consumption prediction with periodicity," Energy, Elsevier, vol. 197(C).
    3. Holt, Charles C., 2004. "Forecasting seasonals and trends by exponentially weighted moving averages," International Journal of Forecasting, Elsevier, vol. 20(1), pages 5-10.
    4. Jia, Chenyu & Tian, Yukai & Shi, Yuanhao & Jia, Jianfang & Wen, Jie & Zeng, Jianchao, 2023. "State of health prediction of lithium-ion batteries based on bidirectional gated recurrent unit and transformer," Energy, Elsevier, vol. 285(C).
    5. Holt, Charles C., 2004. "Author's retrospective on 'Forecasting seasonals and trends by exponentially weighted moving averages'," International Journal of Forecasting, Elsevier, vol. 20(1), pages 11-13.
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