Opportunities for battery aging mode diagnosis of renewable energy storage

Yunhong Che, Xiaosong Hu*, Remus Teodorescu*

*Kontaktforfatter

Publikation: Bidrag til tidsskriftKommentar/debatForskningpeer review

2 Citationer (Scopus)

Abstract

Lithium-ion batteries are key energy storage technologies to promote the global clean energy process, particularly in power grids and electrified transportation. However, complex usage conditions and lack of precise measurement make it difficult for battery health estimation under field applications, especially for aging mode diagnosis. In a recent issue of Nature Communications, Dubarry et al. shed light on this issue by investigating the solution based on machine learning and battery digital twins. They achieved aging modes diagnosis of photovoltaics-connected batteries working for 2 years with more than 10,000 degradation paths under different seasons and cloud shading conditions.

OriginalsprogEngelsk
TidsskriftJoule
Vol/bind7
Udgave nummer7
Sider (fra-til)1405-1407
Antal sider3
ISSN2542-4785
DOI
StatusUdgivet - 19 jul. 2023

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