Artificial Intelligence Opportunities to Diagnose Degradation Modes for Safety Operation in Lithium Batteries-Reference-Cited by-同舟云学术

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

Published:2023-07-21 Issue:7 Volume:9 Page:388
ISSN:2313-0105
Container-title:Batteries
language:en
Short-container-title:Batteries

Author:

Jaime-Barquero Edurne¹²,Bekaert Emilie²^ORCID,Olarte Javier²³,Zulueta Ekaitz¹,Lopez-Guede Jose Manuel¹^ORCID

Affiliation:

1. Department of Systems and Automatic Control, Faculty of Engineering of Vitoria-Gasteiz, University of the Basque Country (UPV/EHU), C/Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain

2. Center for Cooperative Research on Alternative Energies (CIC EnergiGUNE), Basque Research and Technology Alliance (BRTA), Parque Tecnológico de Alava, Albert Eisntein 48, 01510 Vitoria-Gasteiz, Spain

3. Bcare. C/Hernanos Lumiere 48, 01510 Miñano, Spain

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.

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Electrochemistry,Energy Engineering and Power Technology

Link

https://www.mdpi.com/2313-0105/9/7/388/pdf

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