Artificial Intelligence Opportunities to Diagnose Degradation Modes for Safety Operation in Lithium Batteries

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Date
2023-07-21
Author
Jaime Barquero, Edurne
Bekaert, Emilie
Olarte, Javier
Zulueta Guerrero, Ekaitz
López Guede, José Manuel ORCID
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  Estadisticas en RECOLECTA
(LA Referencia)

Batteries 9(7) : (2023) // Article ID 388
URI
http://hdl.handle.net/10810/62080
Abstract
The degradation and safety study of lithium-ion batteries is becoming increasingly important given that these batteries are widely used not only in electronic devices but also in automotive vehicles. Consequently, the detection of degradation modes that could lead to safety alerts is essential. Existing methodologies are diverse, experimental based, model based, and the new trends of artificial intelligence. This review aims to analyze the existing methodologies and compare them, opening the spectrum to those based on artificial intelligence (AI). AI-based studies are increasing in number and have a wide variety of applications, but no classification, in-depth analysis, or comparison with existing methodologies is yet available.
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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/ 4.0/).
Except where otherwise noted, this item's license is described as © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/ 4.0/).