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Recent Progress of Deep Learning Methods for Health Monitoring of Lithium-Ion Batteries

Madani, Seyed Saeed 1; Ziebert, Carlos ORCID iD icon 1; Vahdatkhah, Parisa ; Sadrnezhaad, Sayed Khatiboleslam
1 Institut für Angewandte Materialien – Angewandte Werkstoffphysik (IAM-AWP), Karlsruher Institut für Technologie (KIT)

Abstract (englisch):

In recent years, the rapid evolution of transportation electrification has been propelled by the widespread adoption of lithium-ion batteries (LIBs) as the primary energy storage solution. The critical need to ensure the safe and efficient operation of these LIBs has positioned battery management systems (BMS) as pivotal components in this landscape. Among the various BMS functions, state and temperature monitoring emerge as paramount for intelligent LIB management. This review focuses on two key aspects of LIB health management: the accurate prediction of the state of health (SOH) and the estimation of remaining useful life (RUL). Achieving precise SOH predictions not only extends the lifespan of LIBs but also offers invaluable insights for optimizing battery usage. Additionally, accurate RUL estimation is essential for efficient battery management and state estimation, especially as the demand for electric vehicles continues to surge. The review highlights the significance of machine learning (ML) techniques in enhancing LIB state predictions while simultaneously reducing computational complexity. By delving into the current state of research in this field, the review aims to elucidate promising future avenues for leveraging ML in the context of LIBs. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000173232
Veröffentlicht am 08.08.2024
Originalveröffentlichung
DOI: 10.3390/batteries10060204
Scopus
Zitationen: 2
Web of Science
Zitationen: 2
Dimensions
Zitationen: 2
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Angewandte Materialien – Angewandte Werkstoffphysik (IAM-AWP)
Publikationstyp Zeitschriftenaufsatz
Publikationsdatum 13.06.2024
Sprache Englisch
Identifikator ISSN: 2313-0105
KITopen-ID: 1000173232
HGF-Programm 38.02.02 (POF IV, LK 01) Components and Cells
Erschienen in Batteries
Verlag MDPI
Band 10
Heft 6
Seiten Art.-Nr.: 204
Projektinformation HELIOS (EU, H2020, 963646)
Bemerkung zur Veröffentlichung This article belongs to the Special Issue Enhancement of Lithium-Ion and Post-lithium Batteries Safety: Fundamentals, Materials and Applications.
Vorab online veröffentlicht am 12.06.2024
Schlagwörter lithium-ion battery; transportation electrification; energy storage; electric vehicle; deep learning; state-of-health; machine learning
Nachgewiesen in Web of Science
Dimensions
Scopus
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