Machine learning : an advanced platform for materials development and state prediction in lithium-ion batteries
Lithium-ion batteries (LIBs) are vital energy-storage devices in modern society. However, the performance and cost are still not satisfactory in terms of energy density, power density, cycle life, safety, etc. To further improve the performance of batteries, traditional “trial-and-error” processes r...
Main Authors: | Lv, Chade, Zhou, Xin, Zhong, Lixiang, Yan, Chunshuang, Srinivasan, Madhavi, Seh, Zhi Wei, Liu, Chuntai, Pan, Hongge, Li, Shuzhou, Wen, Yonggang, Yan, Qingyu |
---|---|
Other Authors: | School of Materials Science and Engineering |
Format: | Journal Article |
Language: | English |
Published: |
2022
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/154706 |
Similar Items
-
Metal extraction from spent lithium-ion batteries (LIBs) at high pulp density by environmentally friendly bioleaching process
by: Roy, Joseph Jegan, et al.
Published: (2021) -
Carbon necklace incorporated electroactive reservoir constructing flexible papers for advanced lithium-ion batteries
by: Du, Min, et al.
Published: (2020) -
Oxocarbon-functionalized graphene as a lithium-ion battery cathode : a first-principles investigation
by: Wang, Zicheng, et al.
Published: (2020) -
Mixed crystal FeFx submicron spheres loaded on fluorinated graphene as cathode materials for Lithium-Ion batteries
by: Qu, Jianying, et al.
Published: (2024) -
Lithium recovery from spent lithium-ion batteries leachate by chelating agents facilitated electrodialysis
by: Xing, Zheng, et al.
Published: (2023)