Long Short-Term Memory With Attention Mechanism for State of Charge Estimation of Lithium-Ion Batteries
Evaluating the state-of-charge of the battery's current cycle is one of the major tasks in the charge management of rechargeable batteries. We propose a long short-term memory model with an attention mechanism to estimate the charging status of two lithium-ion batteries. Data from three dynamic...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9096356/ |
| _version_ | 1818733144844533760 |
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| author | Tadele Mamo Fu-Kwun Wang |
| author_facet | Tadele Mamo Fu-Kwun Wang |
| author_sort | Tadele Mamo |
| collection | DOAJ |
| description | Evaluating the state-of-charge of the battery's current cycle is one of the major tasks in the charge management of rechargeable batteries. We propose a long short-term memory model with an attention mechanism to estimate the charging status of two lithium-ion batteries. Data from three dynamic tests such as dynamic stress test, supplemental federal test procedure-driving schedule, and federal urban driving schedule are used to evaluate our model at different temperatures. One dataset or two datasets are used as the training data, and the other datasets are used as the test data. The model achieves the predictive root mean square errors of 0.9593, 0.8714, and 0.9216 at three different temperatures for the FUDS dataset. Moreover, the predictive RMSE of the proposed model is lower than 1.41 for all our experiments. We use the Monte Carlo dropout technique to verify the robust of the proposed model. |
| first_indexed | 2024-12-17T23:44:48Z |
| format | Article |
| id | doaj.art-e73ee0f4cf6a4ccca013fbb6676a743d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T23:44:48Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e73ee0f4cf6a4ccca013fbb6676a743d2022-12-21T21:28:21ZengIEEEIEEE Access2169-35362020-01-018941409415110.1109/ACCESS.2020.29956569096356Long Short-Term Memory With Attention Mechanism for State of Charge Estimation of Lithium-Ion BatteriesTadele Mamo0https://orcid.org/0000-0003-1364-510XFu-Kwun Wang1https://orcid.org/0000-0003-4563-945XDepartment of Industrial Management, National Taiwan University of Science and Technology, Taipei, TaiwanDepartment of Industrial Management, National Taiwan University of Science and Technology, Taipei, TaiwanEvaluating the state-of-charge of the battery's current cycle is one of the major tasks in the charge management of rechargeable batteries. We propose a long short-term memory model with an attention mechanism to estimate the charging status of two lithium-ion batteries. Data from three dynamic tests such as dynamic stress test, supplemental federal test procedure-driving schedule, and federal urban driving schedule are used to evaluate our model at different temperatures. One dataset or two datasets are used as the training data, and the other datasets are used as the test data. The model achieves the predictive root mean square errors of 0.9593, 0.8714, and 0.9216 at three different temperatures for the FUDS dataset. Moreover, the predictive RMSE of the proposed model is lower than 1.41 for all our experiments. We use the Monte Carlo dropout technique to verify the robust of the proposed model.https://ieeexplore.ieee.org/document/9096356/Attention mechanismlithium-ion batterylong short-term memorystate-of-charge |
| spellingShingle | Tadele Mamo Fu-Kwun Wang Long Short-Term Memory With Attention Mechanism for State of Charge Estimation of Lithium-Ion Batteries IEEE Access Attention mechanism lithium-ion battery long short-term memory state-of-charge |
| title | Long Short-Term Memory With Attention Mechanism for State of Charge Estimation of Lithium-Ion Batteries |
| title_full | Long Short-Term Memory With Attention Mechanism for State of Charge Estimation of Lithium-Ion Batteries |
| title_fullStr | Long Short-Term Memory With Attention Mechanism for State of Charge Estimation of Lithium-Ion Batteries |
| title_full_unstemmed | Long Short-Term Memory With Attention Mechanism for State of Charge Estimation of Lithium-Ion Batteries |
| title_short | Long Short-Term Memory With Attention Mechanism for State of Charge Estimation of Lithium-Ion Batteries |
| title_sort | long short term memory with attention mechanism for state of charge estimation of lithium ion batteries |
| topic | Attention mechanism lithium-ion battery long short-term memory state-of-charge |
| url | https://ieeexplore.ieee.org/document/9096356/ |
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