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Prediction of lithium-ion battery SOC based on the fusion of MHA and ConvolGRU

If the charging state of the lithium-ion battery can be accurately predicted, overcharge and overdischarge of the battery can be avoided, and the service life of the battery can be improved. In order to improve the prediction accuracy of SOC, a prediction method combined with convolutional layer, mu...

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Detalles Bibliográficos
Autores principales: Tang, Pei, Hua, Jusen, Wang, Pengchen, QU, Zhonghui, Jiang, Minnan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10545700/
https://www.ncbi.nlm.nih.gov/pubmed/37783740
http://dx.doi.org/10.1038/s41598-023-43858-5
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author Tang, Pei
Hua, Jusen
Wang, Pengchen
QU, Zhonghui
Jiang, Minnan
author_facet Tang, Pei
Hua, Jusen
Wang, Pengchen
QU, Zhonghui
Jiang, Minnan
author_sort Tang, Pei
collection PubMed
description If the charging state of the lithium-ion battery can be accurately predicted, overcharge and overdischarge of the battery can be avoided, and the service life of the battery can be improved. In order to improve the prediction accuracy of SOC, a prediction method combined with convolutional layer, multi-head attention mechanism and gated cycle unit is proposed to extract data feature information from different dimensions of space and time. Using the data set of the University of Maryland, we simulated the battery in real vehicle operating conditions at different temperatures (0 °C, 25 °C, 45 °C). The test results showed that the mean absolute error, root mean square error and maximum prediction error of the model were 0.53%, 0.67% and 0.4% respectively. The results show that the model can predict SOC accurately. At the same time, the comparison with other prediction models shows that the prediction accuracy of this model is the highest.
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spelling pubmed-105457002023-10-04 Prediction of lithium-ion battery SOC based on the fusion of MHA and ConvolGRU Tang, Pei Hua, Jusen Wang, Pengchen QU, Zhonghui Jiang, Minnan Sci Rep Article If the charging state of the lithium-ion battery can be accurately predicted, overcharge and overdischarge of the battery can be avoided, and the service life of the battery can be improved. In order to improve the prediction accuracy of SOC, a prediction method combined with convolutional layer, multi-head attention mechanism and gated cycle unit is proposed to extract data feature information from different dimensions of space and time. Using the data set of the University of Maryland, we simulated the battery in real vehicle operating conditions at different temperatures (0 °C, 25 °C, 45 °C). The test results showed that the mean absolute error, root mean square error and maximum prediction error of the model were 0.53%, 0.67% and 0.4% respectively. The results show that the model can predict SOC accurately. At the same time, the comparison with other prediction models shows that the prediction accuracy of this model is the highest. Nature Publishing Group UK 2023-10-02 /pmc/articles/PMC10545700/ /pubmed/37783740 http://dx.doi.org/10.1038/s41598-023-43858-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Tang, Pei
Hua, Jusen
Wang, Pengchen
QU, Zhonghui
Jiang, Minnan
Prediction of lithium-ion battery SOC based on the fusion of MHA and ConvolGRU
title Prediction of lithium-ion battery SOC based on the fusion of MHA and ConvolGRU
title_full Prediction of lithium-ion battery SOC based on the fusion of MHA and ConvolGRU
title_fullStr Prediction of lithium-ion battery SOC based on the fusion of MHA and ConvolGRU
title_full_unstemmed Prediction of lithium-ion battery SOC based on the fusion of MHA and ConvolGRU
title_short Prediction of lithium-ion battery SOC based on the fusion of MHA and ConvolGRU
title_sort prediction of lithium-ion battery soc based on the fusion of mha and convolgru
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10545700/
https://www.ncbi.nlm.nih.gov/pubmed/37783740
http://dx.doi.org/10.1038/s41598-023-43858-5
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