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Predictive analytics for prolonging lithium-ion battery lifespan through informed storage conditions

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  • Dwivedi, Shalini
  • Akula, Aparna
  • Pecht, Michael

Abstract

The capacity degradation of lithium-ion batteries occurs both during storage and operational usage. This paper investigates the capacity degradation of lithium-ion batteries during storage (calendar ageing) by analysing the interplay of storage temperature, state-of-charge (SOC), and time. Leveraging the machine learning techniques of Gaussian process regression and extreme gradient boosting (XGBoost), a predictive model is developed to characterize the degradation patterns. The study includes a sensitivity analysis of stress factors to identify their relative impact on degradation. The insights gained from this analysis are utilized to recommend optimal storage conditions, offering practical guidance for enhancing the durability and performance of lithium-ion batteries in real-world applications.

Suggested Citation

  • Dwivedi, Shalini & Akula, Aparna & Pecht, Michael, 2024. "Predictive analytics for prolonging lithium-ion battery lifespan through informed storage conditions," Energy, Elsevier, vol. 308(C).
    • Handle: RePEc:eee:energy:v:308:y:2024:i:c:s0360544224028275
    DOI: 10.1016/j.energy.2024.133052
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    References listed on IDEAS

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    Cited by:

    1. Song, Jingeun & Cha, Junepyo & Choi, Mingi, 2024. "A study on 5-cycle fuel economy prediction model of electric vehicles using numerical simulation," Energy, Elsevier, vol. 309(C).

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