Review of recent progress in sintering of solid-state batteries: Application and modelling

被引:0
|
作者
Sazvar, Amirreza [1 ]
Hajibandeh, Masoumeh [1 ]
Vafaei, Pariya [2 ]
Hosseinzadeh, Elham [3 ]
Jabbari, Masoud [4 ]
机构
[1] Iran Univ Sci & Technol, Sch Met & Mat Engn, Tehran, Iran
[2] Univ Tehran, Sch Met & Mat Engn, Tehran, Iran
[3] Univ York, Sch Phys Engn & Technol, York YO10 5DD, England
[4] Univ Leeds, Sch Mech Engn, Leeds LS2 9JT, England
关键词
Solid-state batteries; Cold sintering; Flash sintering; Modelling; LI-ION; THERMAL RUNAWAY; TEMPERATURE DISTRIBUTION; NUMERICAL-SIMULATION; ELECTROLYTES; LITHIUM; DIFFUSION; CONDUCTIVITY; SURFACE; FIELD;
D O I
10.1016/j.est.2024.113863
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
The increasing demand for advanced energy storage solutions has fuelled an increasing need for cutting- edge technologies that can provide high battery capacity, safety, and environmental sustainability. This comprehensive review article embarks on an exploration of the latest advances in solid-state batteries, offering a panoramic view of their evolution. Another pivotal aspect of this review is an in-depth analysis of recent advancements in battery materials sintering techniques, with a particular focus on cold sintering and flash sintering. By delving into the fundamental principles of sintering, we illustrate the substantial potential of these innovative methods in shaping the future of energy storage technologies. These techniques are instrumental in streamlining the manufacturing process of solid-state batteries, making them more efficient and sustainable. Additionally, the review extends its scope to encompass the modelling of these sintering processes, emphasising their helpful role in the sintering of solid-state batteries. Furthermore, the article ventures into the modelling of solid-state batteries, introducing the powerful tool of machine learning to enhance the understanding and optimisation of these advanced energy storage systems. This synergy between modelling and machine learning promises to expedite the development of robust and cost-effective solid-state batteries.
引用
收藏
页数:34
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