RUL prediction of lithium ion battery based on CEEMDAN-CNN BiLSTM model
With the wide application of lithium ion batteries, the importance of life prediction is also highlighted. The prediction of the remaining life of lithium ion battery is an important part of its health management, and accurate prediction can improve the safety of equipment. In this paper, a method f...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-10-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484723008648 |
| _version_ | 1797388787943211008 |
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| author | Xifeng Guo Kaize Wang Shu Yao Guojiang Fu Yi Ning |
| author_facet | Xifeng Guo Kaize Wang Shu Yao Guojiang Fu Yi Ning |
| author_sort | Xifeng Guo |
| collection | DOAJ |
| description | With the wide application of lithium ion batteries, the importance of life prediction is also highlighted. The prediction of the remaining life of lithium ion battery is an important part of its health management, and accurate prediction can improve the safety of equipment. In this paper, a method for predicting the residual life of lithium ion batteries based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), One-dimensional Convolutional Neural Network (1D CNN) and Bi-directional Long Short-Term Memory (BiLSTM) neural network is proposed. The capacity is selected as the health factor, and then CEEMDAN is used to decompose the complex and unstable data to obtain stable components. One-dimensional Convolutional Neural Network (1D CNN) is used to deeply mine the capacity data of lithium-ion batteries. Finally, BiLSTM neural network modeling is used to predict the remaining useful life (RUL) of lithium-ion batteries. The NASA data set is used for testing and prediction comparison with BiLSTM model and CNN-BiLSTM model. The prediction results show that CEEMDAN-CNN BiLSTM model has higher prediction accuracy. |
| first_indexed | 2024-03-08T22:45:54Z |
| format | Article |
| id | doaj.art-057bdac627b043608d7722f57cf48c82 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-03-08T22:45:54Z |
| publishDate | 2023-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-057bdac627b043608d7722f57cf48c822023-12-17T06:38:54ZengElsevierEnergy Reports2352-48472023-10-01912991306RUL prediction of lithium ion battery based on CEEMDAN-CNN BiLSTM modelXifeng Guo0Kaize Wang1Shu Yao2Guojiang Fu3Yi Ning4Shenyang Jianzhu University, Shenyang, 110168, ChinaCorresponding author.; Shenyang Jianzhu University, Shenyang, 110168, ChinaShenyang Jianzhu University, Shenyang, 110168, ChinaShenyang Jianzhu University, Shenyang, 110168, ChinaShenyang Jianzhu University, Shenyang, 110168, ChinaWith the wide application of lithium ion batteries, the importance of life prediction is also highlighted. The prediction of the remaining life of lithium ion battery is an important part of its health management, and accurate prediction can improve the safety of equipment. In this paper, a method for predicting the residual life of lithium ion batteries based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), One-dimensional Convolutional Neural Network (1D CNN) and Bi-directional Long Short-Term Memory (BiLSTM) neural network is proposed. The capacity is selected as the health factor, and then CEEMDAN is used to decompose the complex and unstable data to obtain stable components. One-dimensional Convolutional Neural Network (1D CNN) is used to deeply mine the capacity data of lithium-ion batteries. Finally, BiLSTM neural network modeling is used to predict the remaining useful life (RUL) of lithium-ion batteries. The NASA data set is used for testing and prediction comparison with BiLSTM model and CNN-BiLSTM model. The prediction results show that CEEMDAN-CNN BiLSTM model has higher prediction accuracy.http://www.sciencedirect.com/science/article/pii/S2352484723008648Lithium ion batteryRemaining service lifeCEEMDAN1D CNNBiLSTM |
| spellingShingle | Xifeng Guo Kaize Wang Shu Yao Guojiang Fu Yi Ning RUL prediction of lithium ion battery based on CEEMDAN-CNN BiLSTM model Energy Reports Lithium ion battery Remaining service life CEEMDAN 1D CNN BiLSTM |
| title | RUL prediction of lithium ion battery based on CEEMDAN-CNN BiLSTM model |
| title_full | RUL prediction of lithium ion battery based on CEEMDAN-CNN BiLSTM model |
| title_fullStr | RUL prediction of lithium ion battery based on CEEMDAN-CNN BiLSTM model |
| title_full_unstemmed | RUL prediction of lithium ion battery based on CEEMDAN-CNN BiLSTM model |
| title_short | RUL prediction of lithium ion battery based on CEEMDAN-CNN BiLSTM model |
| title_sort | rul prediction of lithium ion battery based on ceemdan cnn bilstm model |
| topic | Lithium ion battery Remaining service life CEEMDAN 1D CNN BiLSTM |
| url | http://www.sciencedirect.com/science/article/pii/S2352484723008648 |
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