Residual Life Prediction of Lithium Batteries Based on Data Mining

Lithium-ion batteries are an important part of smartphones, and their performance has a great impact on the life of the phone. The longevity of lithium-ion batteries is key to ensuring their reliability and extending their useful life. This paper built a lithium battery life prediction model and gre...

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Detalles Bibliográficos
Autores principales: Ma, Dandan, Qin, Xiangge
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9359824/
https://www.ncbi.nlm.nih.gov/pubmed/35958783
http://dx.doi.org/10.1155/2022/4520160
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author Ma, Dandan
Qin, Xiangge
author_facet Ma, Dandan
Qin, Xiangge
author_sort Ma, Dandan
collection PubMed
description Lithium-ion batteries are an important part of smartphones, and their performance has a great impact on the life of the phone. The longevity of lithium-ion batteries is key to ensuring their reliability and extending their useful life. This paper built a lithium battery life prediction model and grey model MDGM(1,1) based on data mining. Then, experimental data were selected for testing, and the prediction error reached 10.5% at the minimum. It showed that the prediction model had higher precision and could provide help for the prediction and development of mobile phone battery life.
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spelling pubmed-93598242022-08-10 Residual Life Prediction of Lithium Batteries Based on Data Mining Ma, Dandan Qin, Xiangge Comput Intell Neurosci Research Article Lithium-ion batteries are an important part of smartphones, and their performance has a great impact on the life of the phone. The longevity of lithium-ion batteries is key to ensuring their reliability and extending their useful life. This paper built a lithium battery life prediction model and grey model MDGM(1,1) based on data mining. Then, experimental data were selected for testing, and the prediction error reached 10.5% at the minimum. It showed that the prediction model had higher precision and could provide help for the prediction and development of mobile phone battery life. Hindawi 2022-06-13 /pmc/articles/PMC9359824/ /pubmed/35958783 http://dx.doi.org/10.1155/2022/4520160 Text en Copyright © 2022 Dandan Ma and Xiangge Qin. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Ma, Dandan
Qin, Xiangge
Residual Life Prediction of Lithium Batteries Based on Data Mining
title Residual Life Prediction of Lithium Batteries Based on Data Mining
title_full Residual Life Prediction of Lithium Batteries Based on Data Mining
title_fullStr Residual Life Prediction of Lithium Batteries Based on Data Mining
title_full_unstemmed Residual Life Prediction of Lithium Batteries Based on Data Mining
title_short Residual Life Prediction of Lithium Batteries Based on Data Mining
title_sort residual life prediction of lithium batteries based on data mining
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9359824/
https://www.ncbi.nlm.nih.gov/pubmed/35958783
http://dx.doi.org/10.1155/2022/4520160
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