State‐of‐health prediction for lithium‐ion batteries via electrochemical impedance spectroscopy and artificial neural networks-Reference-Cited by-同舟云学术

State‐of‐health prediction for lithium‐ion batteries via electrochemical impedance spectroscopy and artificial neural networks

Published:2020-08-04 Issue:5 Volume:2 Page:
ISSN:2578-4862
Container-title:Energy Storage
language:en
Short-container-title:Energy Storage

Author:

Li Yige¹,Dong Bo²,Zerrin Taner¹,Jauregui Evan³,Wang Xichao⁴,Hua Xia⁴,Ravichandran Dwaraknath²,Shang Ruoxu¹,Xie Jia⁴,Ozkan Mihrimah²⁵,Ozkan Cengiz S.¹⁶⁵^ORCID

Affiliation:

1. Materials Science and Engineering Program University of California Riverside California USA

2. Department of Electrical and Computer Engineering University of California Riverside California USA

3. Department of Chemical and Environmental Engineering University of California Riverside California USA

4. Department of Computer Science and Engineering University of California Riverside California USA

5. Department of Chemistry University of California Riverside California USA

6. Department of Mechanical Engineering University of California Riverside California USA

Publisher

Wiley

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/est2.186

Reference60 articles.

1. Issues and challenges facing rechargeable lithium batteries

2. Challenges for Rechargeable Li Batteries

3. Building better batteries

4. Promise and reality of post-lithium-ion batteries with high energy densities

5. Improved electrochemical performance of LiCoO2 electrodes for high-voltage operations by Ag thin film coating via magnetron sputtering

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