Battery state-of-health estimation using machine learning

Daniel-Ioan Stroe*, Xin Sui

*Kontaktforfatter

Publikation: Bidrag til bog/antologi/rapport/konference proceedingBidrag til bog/antologiForskningpeer review

Abstract

Over the years, lithium–ion batteries have developed as a key enabling technology for the green transition. Although many of these batteries’ characteristics, such as energy density, power capability, and cost, have gradually improved, uncertainties remain concerning their performance over their lifetimes. Thus, to ensure reliable and efficient battery operation, the battery's available performance, known as its state of health (SOH), must be known at every moment. This chapter introduces the most common battery SOH estimation methods, from direct measurements to deep neural networks, discussing their key performance metrics, advantages, and drawbacks.
OriginalsprogEngelsk
TitelControl of Power Electronic Converters and Systems : Volume 4
RedaktørerFrede Blaabjerg
Antal sider48
Vol/bind4
ForlagElsevier
Publikationsdato1 jan. 2024
Udgave1
Sider383-430
Kapitel13
ISBN (Trykt)9780323856232
ISBN (Elektronisk)9780323856225
DOI
StatusUdgivet - 1 jan. 2024

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