Deep neural network‐driven in‐situ detection and quantification of lithium plating on anodes in commercial lithium‐ion batteries-Reference-Cited by-同舟云学术

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Deep neural network‐driven in‐situ detection and quantification of lithium plating on anodes in commercial lithium‐ion batteries

Published:2022-09-06 Issue:1 Volume:5 Page:
ISSN:2567-3173
Container-title:EcoMat
language:en
Short-container-title:EcoMat

Author:

Tian Yu¹,Lin Cheng¹,Li Hailong²,Du Jiuyu³,Xiong Rui¹^ORCID

Affiliation:

1. School of Mechanical Engineering Beijing Institute of Technology Beijing China

2. School of Business, Society and Engineering Mälardalen University Västerås Sweden

3. State Key Laboratory of Automotive Safety and Energy Tsinghua University Beijing China

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Materials Science (miscellaneous),Physical and Theoretical Chemistry,Chemistry (miscellaneous)

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/eom2.12280

Reference48 articles.

1. Optimal energy management strategy of a novel hybrid dual-motor transmission system for electric vehicles

2. The state of the art on preheating lithium-ion batteries in cold weather

3. Toward a Safer Battery Management System: A Critical Review on Diagnosis and Prognosis of Battery Short Circuit

4. Recent progress in lithium-ion battery thermal management for a wide range of temperature and abuse conditions

5. Detecting undesired lithium plating on anodes for lithium-ion batteries – A review on the in-situ methods

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3. Insight Understanding of External Pressure on Lithium Plating in Commercial Lithium‐Ion Batteries;Advanced Functional Materials;2024-07

4. High sensitivity detection of lithium plating in high-energy lithium-ion batteries based on time-domain distribution relaxation times analysis;Energy Storage Materials;2024-05

5. Rapid warm-up strategy for lithium-ion batteries based on temperature uniformity model of sandwich self-heating structure in multiple scenarios;Journal of Power Sources;2024-04

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