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^*

^*Kontaktforfatter

Publikation: Bidrag til tidsskrift › Kommentar/debat › Forskning › peer review

4 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.

Originalsprog	Engelsk
Tidsskrift	Joule
Vol/bind	7
Udgave nummer	7
Sider (fra-til)	1405-1407
Antal sider	3
ISSN	2542-4785
DOI	https://doi.org/10.1016/j.joule.2023.06.014
Status	Udgivet - 19 jul. 2023

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Publisher Copyright:
© 2023

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10.1016/j.joule.2023.06.014

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1 Afsluttet
1 Igangværende

CROSBAT: SMART BATTERY
Teodorescu, R. (PI (principal investigator)), Stroe, D.-I. (CoPI), Che, Y. (Projektdeltager), Zheng, Y. (Projektdeltager), Kulkarni, A. (Projektdeltager), Sui, X. (Projektdeltager), Vilsen, S. B. (Projektdeltager), Bharadwaj, P. (Projektdeltager), Weinreich, N. A. (Projektdeltager), Christensen, M. D. (Projektkoordinator) & Steffensen, B. (Projektkoordinator)
Villum Fonden
01/09/2021 → 31/08/2027
Projekter: Projekt › Forskning
State of Health Estimation and Prediction for Lithium-ion Batteries Based on Transfer Learning
Che, Y. (PI (principal investigator)), Teodorescu, R. (Supervisor) & Sui, X. (Supervisor)
01/12/2021 → 31/12/2023
Projekter: Projekt › Ph.d.-projekt

4 Citationer
1 Ph.d.-afhandling

State of Health Estimation and Prediction for Lithium-ion Batteries Based on Transfer Learning
Che, Y., 2023, Aalborg Universitetsforlag.
Publikation: Ph.d.-afhandling

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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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Opportunities for battery aging mode diagnosis of renewable energy storage

Abstract

Bibliografisk note

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AUB Link

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Fingeraftryk

Projekter

CROSBAT: SMART BATTERY

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

Publikation

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

Citationsformater