Towards machine-learning driven prognostics and health management of Li-ion batteries. A comprehensive review
Sahar Khaleghi,
Md Sazzad Hosen,
Joeri Van Mierlo and
Maitane Berecibar
Renewable and Sustainable Energy Reviews, 2024, vol. 192, issue C
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.
Keywords: Lithium-ion battery; State of health (SoH); Remaining useful life (RUL); Battery prognostics and health management; Machine learning techniques (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:rensus:v:192:y:2024:i:c:s1364032123010821
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DOI: 10.1016/j.rser.2023.114224
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