Deep-learning based spatio-temporal generative model on assessing state-of-health for Li-ion batteries with partially-cycled profiles
Cited 0 times in
Cited 0 times in
- Title
- Deep-learning based spatio-temporal generative model on assessing state-of-health for Li-ion batteries with partially-cycled profiles
- Author
- Park, Seojoung; Lee, Hyunjun; Scott-Nevros, Zoe K. K.; Lim, Dongjun; Seo, Dong-Hwa; Choi, Yunseok; Lim, Hankwon; Kim, Donghyuk
- Issue Date
- 2023-04
- Publisher
- ROYAL SOC CHEMISTRY
- Citation
- MATERIALS HORIZONS, v.10, no.4, pp.1274 - 1281
- Abstract
- Accurately estimating the state-of-health (SOH) of lithium-ion batteries is emerging as a hot topic because of the rapid increase in electric appliance usage. However, versatile applicability to various battery compositions and diverse cycling conditions, and prediction only with partial data still remain challenges. In this paper, a Deep-learning-based Graphical approach to Estimation of Lithium-ion batteries SOH (D-GELS) was developed to predict the SOH covering three cathode materials, LiFePO4, LiNiCoAlO2, and LiNiCOMnO2. D-GELS shows an accurate performance for SOH prediction, less than 0.012 of RMSE, was predicted regardless of cathode materials, and its applicability was confirmed. Furthermore, D-GELS was capable of predicting the SOH using partially-cycled data, since less than 0.046 of RMSE was observed even with 50% of the image missing. When using partially-cycled profiles, significant economic benefits can be seen in used battery management, as the number of assessed batteries increases greatly, leading to cost savings.
- URI
- https://scholarworks.unist.ac.kr/handle/201301/62171
- DOI
- 10.1039/d3mh00013c
- ISSN
- 2051-6347
- Appears in Collections:
- CN_Journal Papers
ECHE_Journal Papers
- Files in This Item:
- There are no files associated with this item.
can give you direct access to the published full text of this article. (UNISTARs only)
Show full item record
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.