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A conditional random field based feature learning framework for battery capacity prediction
This paper proposes a network model framework based on long and short-term memory (LSTM) and conditional random field (CRF) to promote Li-ion battery capacity prediction results. The model uses LSTM to extract temporal features from the data and CRF to build a transfer matrix to enhance temporal fea...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9345946/ https://www.ncbi.nlm.nih.gov/pubmed/35918374 http://dx.doi.org/10.1038/s41598-022-17455-x |
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author | Wang, Hai-Kun Zhang, Yang Huang, Mohong |
author_facet | Wang, Hai-Kun Zhang, Yang Huang, Mohong |
author_sort | Wang, Hai-Kun |
collection | PubMed |
description | This paper proposes a network model framework based on long and short-term memory (LSTM) and conditional random field (CRF) to promote Li-ion battery capacity prediction results. The model uses LSTM to extract temporal features from the data and CRF to build a transfer matrix to enhance temporal feature learning for long serialization prediction of lithium battery feature sequence data. The NASA PCOE lithium battery dataset is selected for the experiments, and control tests on LSTM temporal feature extraction modules, including recurrent neural network (RNN), gated recurrent unit (GRU), bi-directional gated recurrent unit (BiGRU) and bi-directional long and short term memory (BiLSTM) networks, are designed to test the adaptability of the CRF method to different temporal feature extraction modules. Compared with previous Li-ion battery capacity prediction methods, the network model framework proposed in this paper achieves better prediction results in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE) metrics. |
format | Online Article Text |
id | pubmed-9345946 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-93459462022-08-04 A conditional random field based feature learning framework for battery capacity prediction Wang, Hai-Kun Zhang, Yang Huang, Mohong Sci Rep Article This paper proposes a network model framework based on long and short-term memory (LSTM) and conditional random field (CRF) to promote Li-ion battery capacity prediction results. The model uses LSTM to extract temporal features from the data and CRF to build a transfer matrix to enhance temporal feature learning for long serialization prediction of lithium battery feature sequence data. The NASA PCOE lithium battery dataset is selected for the experiments, and control tests on LSTM temporal feature extraction modules, including recurrent neural network (RNN), gated recurrent unit (GRU), bi-directional gated recurrent unit (BiGRU) and bi-directional long and short term memory (BiLSTM) networks, are designed to test the adaptability of the CRF method to different temporal feature extraction modules. Compared with previous Li-ion battery capacity prediction methods, the network model framework proposed in this paper achieves better prediction results in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE) metrics. Nature Publishing Group UK 2022-08-02 /pmc/articles/PMC9345946/ /pubmed/35918374 http://dx.doi.org/10.1038/s41598-022-17455-x Text en © The Author(s) 2022 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 Wang, Hai-Kun Zhang, Yang Huang, Mohong A conditional random field based feature learning framework for battery capacity prediction |
title | A conditional random field based feature learning framework for battery capacity prediction |
title_full | A conditional random field based feature learning framework for battery capacity prediction |
title_fullStr | A conditional random field based feature learning framework for battery capacity prediction |
title_full_unstemmed | A conditional random field based feature learning framework for battery capacity prediction |
title_short | A conditional random field based feature learning framework for battery capacity prediction |
title_sort | conditional random field based feature learning framework for battery capacity prediction |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9345946/ https://www.ncbi.nlm.nih.gov/pubmed/35918374 http://dx.doi.org/10.1038/s41598-022-17455-x |
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