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

D. Z. Kuang,

Zhenpo Wang, Yiwen Zhao

и другие.

Journal of Power Sources, Год журнала: 2025, Номер 645, С. 237176 - 237176

Опубликована: Апрель 26, 2025

Язык: 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

и другие.

eScience, Год журнала: 2025, Номер unknown, С. 100371 - 100371

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

7

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

Tao Pan,

Yujie Li,

Ziqing Yao

и другие.

Carbon Neutralization, Год журнала: 2025, Номер 4(1)

Опубликована: Янв. 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.

Язык: Английский

Процитировано

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

и другие.

Energy & Fuels, Год журнала: 2025, Номер unknown

Опубликована: Март 13, 2025

Язык: Английский

Процитировано

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

и другие.

eTransportation, Год журнала: 2025, Номер unknown, С. 100420 - 100420

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

1

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

и другие.

Chemical Engineering Journal, Год журнала: 2024, Номер unknown, С. 155846 - 155846

Опубликована: Сен. 1, 2024

Язык: Английский

Процитировано

5

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

C. N. Pratheeba,

S. Praveen Kumar

Electrical Engineering, Год журнала: 2025, Номер unknown

Опубликована: Янв. 12, 2025

Язык: Английский

Процитировано

0

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

и другие.

Energy, Год журнала: 2025, Номер unknown, С. 134848 - 134848

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0

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

Binbin Yang,

Nan Chen

и другие.

Energy storage materials, Год журнала: 2025, Номер unknown, С. 104173 - 104173

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0

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

и другие.

eTransportation, Год журнала: 2025, Номер unknown, С. 100426 - 100426

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

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

и другие.

Journal of Power Sources, Год журнала: 2025, Номер 645, С. 237176 - 237176

Опубликована: Апрель 26, 2025

Язык: Английский

Процитировано

0