Precise State-of-Charge Mapping via Deep Learning on Ultrasonic Transmission Signals for Lithium-Ion Batteries-Reference-Cited by-同舟云学术

Precise State-of-Charge Mapping via Deep Learning on Ultrasonic Transmission Signals for Lithium-Ion Batteries

Published:2023-02-03 Issue:6 Volume:15 Page:8217-8223
ISSN:1944-8244
Container-title:ACS Applied Materials & Interfaces
language:en
Short-container-title:ACS Appl. Mater. Interfaces

Author:

Huang Zhenyu¹²^ORCID,Zhou Yu²,Deng Zhe²^ORCID,Huang Kai¹,Xu Mingkang²,Shen Yue¹,Huang Yunhui¹^ORCID

Affiliation:

1. State Key Laboratory of Material Processing and Die & Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei430074, China

2. Wuxi Topsound Technology Co., Ltd., Wuxi, Jiangsu214000, China

Funder

National Natural Science Foundation of China

Publisher

American Chemical Society (ACS)

Subject

General Materials Science

Link

https://pubs.acs.org/doi/pdf/10.1021/acsami.2c22210

Reference34 articles.

1. Multiscale Phase Mapping of LiFePO4-Based Electrodes by Transmission Electron Microscopy and Electron Forward Scattering Diffraction

2. Nanoscale Imaging of Lithium Ion Distribution During In Situ Operation of Battery Electrode and Electrolyte

3. Cell design and image analysis for in situ Raman mapping of inhomogeneous state-of-charge profiles in lithium-ion batteries

4. Diagnostic Evaluation of Detrimental Phenomena in High-Power Lithium-Ion Batteries

5. Visualization of the State-of-Charge Distribution in a LiCoO2Cathode by In Situ Raman Imaging

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