Estimating the health status of lithium-ion batteries using deep learning method based on informer model DOI

D. Z. Kuang,

Zhenpo Wang, Yiwen Zhao

et al.

Journal of Power Sources, Journal Year: 2025, Volume and Issue: 645, P. 237176 - 237176

Published: April 26, 2025

Language: Английский

Revisiting the Critical Role of Metallic Ash Elements in the Development of Hard Carbon for Advancing Sodium-Ion Battery Applications DOI Creative Commons

Chun Wu,

Wenjie Huang, Yinghao Zhang

et al.

eScience, Journal Year: 2025, Volume and Issue: unknown, P. 100371 - 100371

Published: Jan. 1, 2025

Language: Английский

Citations

7
Research Advances on Lithium‐Ion Batteries Calendar Life Prognostic Models DOI Creative Commons

Tao Pan,

Yujie Li,

Ziqing Yao

et al.

Carbon Neutralization, Journal Year: 2025, Volume and Issue: 4(1)

Published: Jan. 1, 2025

ABSTRACT In military reserve power supplies, there is an urgent need for superior secondary batteries to replace conventional primary batteries, and lithium‐ion (LIBs) emerge as one of the best choices due their exceptional performance. The life LIBs includes cycle calendar life, with spanning from years decades. Accurate prediction crucial optimizing deployment maintenance in applications. Model‐based prognostics are usually established estimate using accelerated aging methods under various storage conditions. This review firstly outlines general prognostic workflow LIBs, analyzes degradation mechanisms, summarizes influencing factors; then, we introduce models, evolving simplistic empirical models (EMs) nonempirical mechanistic (MMs) based on LIB knowledge then traditional hybrid empirical‐mechanistic (trad‐EMMs). Finally, data‐driven (DDMs) machine learning (ML) discussed limitation methods, pure knowledge‐integrated establishing a comprehensive framework assessment. To our knowledge, this paper presents first field, summarizing offering some insights into future model development directions. can facilitate researchers analysis prolongation, thereby better serving application national economic life.

Language: Английский

Citations

1
Review on Direct Methanol Fuel Cells: Bridging the Gap between Theory and Application for Sustainable Energy Solutions DOI
Siti Hasanah Osman, Siti Kartom Kamarudin, Norazuwana Shaari

et al.

Energy & Fuels, Journal Year: 2025, Volume and Issue: unknown

Published: March 13, 2025

Language: Английский

Citations

1
State Estimation of Lithium-Ion Batteries via Physics-Machine Learning Combined Methods: A Methodological Review and Future Perspectives DOI
Hanqing Yu, Hongcai Zhang, Zhengjie Zhang

et al.

eTransportation, Journal Year: 2025, Volume and Issue: unknown, P. 100420 - 100420

Published: April 1, 2025

Language: Английский

Citations

1
Multiscale modeling of catalyst deactivation in dry methane reforming DOI
Satchit Nagpal, Chi H. Lee, Niranjan Sitapure

et al.

Chemical Engineering Journal, Journal Year: 2024, Volume and Issue: unknown, P. 155846 - 155846

Published: Sept. 1, 2024

Language: Английский

Citations

5
Hybrid HGRN-SCSO technique for enhanced prediction of remaining useful life in EV batteries DOI

C. N. Pratheeba,

S. Praveen Kumar

Electrical Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 12, 2025

Language: Английский

Citations

0
Toward fast multi-scale state estimation for retired battery reusing via Pareto-efficient DOI
Songtao Ye, Dou An, Chun Wang

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 134848 - 134848

Published: Feb. 1, 2025

Language: Английский

Citations

0
Li2CO3 Contamination in Garnet Solid Electrolyte: Origins, Impacts, and Mitigation Strategies DOI
Ning Shi,

Binbin Yang,

Nan Chen

et al.

Energy storage materials, Journal Year: 2025, Volume and Issue: unknown, P. 104173 - 104173

Published: March 1, 2025

Language: Английский

Citations

0
Towards Digitized Electrochemical Power Source for Electric Vehicles DOI
Jiangong Zhu, Wentao Xu, Siyi Tao

et al.

eTransportation, Journal Year: 2025, Volume and Issue: unknown, P. 100426 - 100426

Published: April 1, 2025

Language: Английский

Citations

0
Estimating the health status of lithium-ion batteries using deep learning method based on informer model DOI

D. Z. Kuang,

Zhenpo Wang, Yiwen Zhao

et al.

Journal of Power Sources, Journal Year: 2025, Volume and Issue: 645, P. 237176 - 237176

Published: April 26, 2025

Language: Английский

Citations

0