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Towards machine-learning driven prognostics and health management of Li-ion batteries. A comprehensive review

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  • Khaleghi, Sahar
  • Hosen, Md Sazzad
  • Van Mierlo, Joeri
  • Berecibar, Maitane

Abstract

Prognostics and health management (PHM) has emerged as a vital research discipline for optimizing the maintenance of operating systems by detecting health degradation and accurately predicting their remaining useful life. In the context of lithium-ion batteries, PHM methodologies have gained significant attention due to their potential for enhancing battery maintenance and ensuring safe and reliable operation. Among the various approaches, data-driven methodologies, particularly those leveraging machine learning (ML) models, have gained interest for their accuracy and simplicity.

Suggested Citation

  • Khaleghi, Sahar & Hosen, Md Sazzad & Van Mierlo, Joeri & Berecibar, Maitane, 2024. "Towards machine-learning driven prognostics and health management of Li-ion batteries. A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    • Handle: RePEc:eee:rensus:v:192:y:2024:i:c:s1364032123010821
    DOI: 10.1016/j.rser.2023.114224
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    References listed on IDEAS

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    1. Sahar Khaleghi & Yousef Firouz & Maitane Berecibar & Joeri Van Mierlo & Peter Van Den Bossche, 2020. "Ensemble Gradient Boosted Tree for SoH Estimation Based on Diagnostic Features," Energies, MDPI, vol. 13(5), pages 1-16, March.
    2. Khaleghi, Sahar & Hosen, Md Sazzad & Karimi, Danial & Behi, Hamidreza & Beheshti, S. Hamidreza & Van Mierlo, Joeri & Berecibar, Maitane, 2022. "Developing an online data-driven approach for prognostics and health management of lithium-ion batteries," Applied Energy, Elsevier, vol. 308(C).
    3. Behi, Hamidreza & Karimi, Danial & Jaguemont, Joris & Gandoman, Foad Heidari & Kalogiannis, Theodoros & Berecibar, Maitane & Van Mierlo, Joeri, 2021. "Novel thermal management methods to improve the performance of the Li-ion batteries in high discharge current applications," Energy, Elsevier, vol. 224(C).
    4. Kristen A. Severson & Peter M. Attia & Norman Jin & Nicholas Perkins & Benben Jiang & Zi Yang & Michael H. Chen & Muratahan Aykol & Patrick K. Herring & Dimitrios Fraggedakis & Martin Z. Bazant & Step, 2019. "Data-driven prediction of battery cycle life before capacity degradation," Nature Energy, Nature, vol. 4(5), pages 383-391, May.
    5. Li, Yi & Zou, Changfu & Berecibar, Maitane & Nanini-Maury, Elise & Chan, Jonathan C.-W. & van den Bossche, Peter & Van Mierlo, Joeri & Omar, Noshin, 2018. "Random forest regression for online capacity estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 232(C), pages 197-210.
    6. Berecibar, M. & Gandiaga, I. & Villarreal, I. & Omar, N. & Van Mierlo, J. & Van den Bossche, P., 2016. "Critical review of state of health estimation methods of Li-ion batteries for real applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 572-587.
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