Prediction of the Remaining Useful Life of Lithium–Ion Batteries Based on Mode Decomposition and ED-LSTM
The prediction of remaining useful life (RUL) of lithium–ion batteries is key to the reliability assessment of batteries and affects safe application of batteries. This article introduces a CEEMDAN-RF-MHA-ED-LSTM method. Using CEEMDAN, the battery capacity data were decomposed to obtain intrinsic mo...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
|
| Series: | Batteries |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-0105/11/3/86 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850204889970376704 |
|---|---|
| author | Bingzeng Song Guangzhao Yue Dong Guo Hanming Wu Yonghai Sun Yuhua Li Bin Zhou |
| author_facet | Bingzeng Song Guangzhao Yue Dong Guo Hanming Wu Yonghai Sun Yuhua Li Bin Zhou |
| author_sort | Bingzeng Song |
| collection | DOAJ |
| description | The prediction of remaining useful life (RUL) of lithium–ion batteries is key to the reliability assessment of batteries and affects safe application of batteries. This article introduces a CEEMDAN-RF-MHA-ED-LSTM method. Using CEEMDAN, the battery capacity data were decomposed to obtain intrinsic mode functions (IMFs), and the weight of each IMF was obtained via the random forest (RF) algorithm. The LSTM neural network was used, the encoder–decoder (ED) structure was introduced, the multi-head attention (MHA) mechanism was used to construct a network model, and the particle swarm optimization (PSO) algorithm was used to optimize the model parameters. Each IMF was input into the model, and the obtained forecast results were weighted and reconstructed to obtain the final forecast data. This method was validated on the battery dataset released by NASA. Compared with that of the single LSTM model, the mean absolute error of the proposed method decreases by 74%, 62%, 71%, and 55% on the No. 05, 06, 07, and 18th battery datasets, respectively. The root mean square error decreased by 72%, 59%, 70%, and 54%, and the mean absolute percent error decreased by 75%, 65%, 71%, and 58%, respectively. This method can accurately predict battery RUL. |
| format | Article |
| id | doaj-art-c5395ab15de14f9e9e052db2cbfa4de9 |
| institution | OA Journals |
| issn | 2313-0105 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Batteries |
| spelling | doaj-art-c5395ab15de14f9e9e052db2cbfa4de92025-08-20T02:11:12ZengMDPI AGBatteries2313-01052025-02-011138610.3390/batteries11030086Prediction of the Remaining Useful Life of Lithium–Ion Batteries Based on Mode Decomposition and ED-LSTMBingzeng Song0Guangzhao Yue1Dong Guo2Hanming Wu3Yonghai Sun4Yuhua Li5Bin Zhou6School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, ChinaSchool of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, ChinaSchool of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, ChinaNational Engineering Laboratory for Mobile Source Emission Control Technology, Tianjin 300300, ChinaJinan Heating Group Co., Ltd., Jinan 250011, ChinaCollege of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian 271017, ChinaState Key Lab of Intelligent Transportation System, Beijing 100088, ChinaThe prediction of remaining useful life (RUL) of lithium–ion batteries is key to the reliability assessment of batteries and affects safe application of batteries. This article introduces a CEEMDAN-RF-MHA-ED-LSTM method. Using CEEMDAN, the battery capacity data were decomposed to obtain intrinsic mode functions (IMFs), and the weight of each IMF was obtained via the random forest (RF) algorithm. The LSTM neural network was used, the encoder–decoder (ED) structure was introduced, the multi-head attention (MHA) mechanism was used to construct a network model, and the particle swarm optimization (PSO) algorithm was used to optimize the model parameters. Each IMF was input into the model, and the obtained forecast results were weighted and reconstructed to obtain the final forecast data. This method was validated on the battery dataset released by NASA. Compared with that of the single LSTM model, the mean absolute error of the proposed method decreases by 74%, 62%, 71%, and 55% on the No. 05, 06, 07, and 18th battery datasets, respectively. The root mean square error decreased by 72%, 59%, 70%, and 54%, and the mean absolute percent error decreased by 75%, 65%, 71%, and 58%, respectively. This method can accurately predict battery RUL.https://www.mdpi.com/2313-0105/11/3/86lithium–ion batterylife predictionmodal decompositionparticle swarm optimization algorithmmodal decompositionlong short-term memory network |
| spellingShingle | Bingzeng Song Guangzhao Yue Dong Guo Hanming Wu Yonghai Sun Yuhua Li Bin Zhou Prediction of the Remaining Useful Life of Lithium–Ion Batteries Based on Mode Decomposition and ED-LSTM Batteries lithium–ion battery life prediction modal decomposition particle swarm optimization algorithm modal decomposition long short-term memory network |
| title | Prediction of the Remaining Useful Life of Lithium–Ion Batteries Based on Mode Decomposition and ED-LSTM |
| title_full | Prediction of the Remaining Useful Life of Lithium–Ion Batteries Based on Mode Decomposition and ED-LSTM |
| title_fullStr | Prediction of the Remaining Useful Life of Lithium–Ion Batteries Based on Mode Decomposition and ED-LSTM |
| title_full_unstemmed | Prediction of the Remaining Useful Life of Lithium–Ion Batteries Based on Mode Decomposition and ED-LSTM |
| title_short | Prediction of the Remaining Useful Life of Lithium–Ion Batteries Based on Mode Decomposition and ED-LSTM |
| title_sort | prediction of the remaining useful life of lithium ion batteries based on mode decomposition and ed lstm |
| topic | lithium–ion battery life prediction modal decomposition particle swarm optimization algorithm modal decomposition long short-term memory network |
| url | https://www.mdpi.com/2313-0105/11/3/86 |
| work_keys_str_mv | AT bingzengsong predictionoftheremainingusefullifeoflithiumionbatteriesbasedonmodedecompositionandedlstm AT guangzhaoyue predictionoftheremainingusefullifeoflithiumionbatteriesbasedonmodedecompositionandedlstm AT dongguo predictionoftheremainingusefullifeoflithiumionbatteriesbasedonmodedecompositionandedlstm AT hanmingwu predictionoftheremainingusefullifeoflithiumionbatteriesbasedonmodedecompositionandedlstm AT yonghaisun predictionoftheremainingusefullifeoflithiumionbatteriesbasedonmodedecompositionandedlstm AT yuhuali predictionoftheremainingusefullifeoflithiumionbatteriesbasedonmodedecompositionandedlstm AT binzhou predictionoftheremainingusefullifeoflithiumionbatteriesbasedonmodedecompositionandedlstm |