Opportunities for battery aging mode diagnosis of renewable energy storage

Yunhong Che, Xiaosong Hu*, Remus Teodorescu*

*Corresponding author for this work

Research output: Contribution to journalComment/debateResearchpeer-review


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.

Original languageEnglish
Issue number7
Pages (from-to)1405-1407
Number of pages3
Publication statusPublished - 19 Jul 2023

Bibliographical note

Funding Information:
Y.C. and R.T. are supported by the “ SMART BATTERY” project, granted by Villum Foundation (222860), and X.H. is supported by the Natural Science Foundation of China (52111530194).

Publisher Copyright:
© 2023


Dive into the research topics of 'Opportunities for battery aging mode diagnosis of renewable energy storage'. Together they form a unique fingerprint.

Cite this