Opportunities for battery aging mode diagnosis of renewable energy storage

Yunhong Che; Xiaosong Hu; Remus Teodorescu

doi:10.1016/j.joule.2023.06.014

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 journal › Comment/debate › Research › peer-review

4 Citations (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.

Original language	English
Journal	Joule
Volume	7
Issue number	7
Pages (from-to)	1405-1407
Number of pages	3
ISSN	2542-4785
DOIs	https://doi.org/10.1016/j.joule.2023.06.014
Publication status	Published - 19 Jul 2023

Bibliographical note

FPublisher Copyright:
© 2023

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10.1016/j.joule.2023.06.014

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title = "Opportunities for battery aging mode diagnosis of renewable energy storage",

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.",

author = "Yunhong Che and Xiaosong Hu and Remus Teodorescu",

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T1 - Opportunities for battery aging mode diagnosis of renewable energy storage

AU - Che, Yunhong

AU - Hu, Xiaosong

AU - Teodorescu, Remus

PY - 2023/7/19

Y1 - 2023/7/19

N2 - 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.

AB - 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.

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U2 - 10.1016/j.joule.2023.06.014

DO - 10.1016/j.joule.2023.06.014

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SP - 1405

EP - 1407

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ER -

Opportunities for battery aging mode diagnosis of renewable energy storage

Abstract

Bibliographical note

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CROSBAT: SMART BATTERY

State of Health Estimation and Prediction for Lithium-ion Batteries Based on Transfer Learning

State of Health Estimation and Prediction for Lithium-ion Batteries Based on Transfer Learning

Cite this

Opportunities for battery aging mode diagnosis of renewable energy storage

Abstract

Bibliographical note

Access to Document

AUB Link

Other files and links

Fingerprint

Projects

CROSBAT: SMART BATTERY

State of Health Estimation and Prediction for Lithium-ion Batteries Based on Transfer Learning

Research output

State of Health Estimation and Prediction for Lithium-ion Batteries Based on Transfer Learning

Cite this