Long Short-Term Memory Approach to Estimate Battery Remaining Useful Life Using Partial Data
Due to the increasing demand of electrical vehicles (EVs), prognostics of the battery state is of paramount importance. The nonlinearity of the signal (e.g. voltage) results in the complexity of analyzing the degradation of the battery. Aging characteristics extracted from the voltage, current, and...
| 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/9187619/ |
| _version_ | 1818446407226359808 |
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| author | Benvolence Chinomona Chunhui Chung Lien-Kai Chang Wei-Chih Su Mi-Ching Tsai |
| author_facet | Benvolence Chinomona Chunhui Chung Lien-Kai Chang Wei-Chih Su Mi-Ching Tsai |
| author_sort | Benvolence Chinomona |
| collection | DOAJ |
| description | Due to the increasing demand of electrical vehicles (EVs), prognostics of the battery state is of paramount importance. The nonlinearity of the signal (e.g. voltage) results in the complexity of analyzing the degradation of the battery. Aging characteristics extracted from the voltage, current, and temperature when the battery is fully charged/discharged were commonly used by previous researchers to determine the battery state. The drawbacks of the previous prediction algorithms are insufficient or irrelevant features to explicitly model the battery aging and the use of fully charged/discharged datasets, which might result in poor prediction accuracy. Therefore, this study proposes a feature selection technique to adequately select optimum statistical feature subset and the use of partial charge/discharge data to determine the battery remaining useful life (RUL) using Recurrent Neural Network - Long Short-Term Memory (RNN-LSTM). The proposed approach demonstrated exceptional RUL prediction results, with the root mean square error (RMSE) of 0.00286 and mean average error (MAE) of 0.00222 using partial discharge data. The proposed method shows prediction improvement in comparison with the use of full data and state-of-the-art outcomes from previous studies of the same open data from the National Aeronautics and Space Administration (NASA) prognostic battery data sets. |
| first_indexed | 2024-12-14T19:47:14Z |
| format | Article |
| id | doaj.art-54235202ac1545399f0fe8102a2c5402 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T19:47:14Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-54235202ac1545399f0fe8102a2c54022022-12-21T22:49:32ZengIEEEIEEE Access2169-35362020-01-01816541916543110.1109/ACCESS.2020.30225059187619Long Short-Term Memory Approach to Estimate Battery Remaining Useful Life Using Partial DataBenvolence Chinomona0https://orcid.org/0000-0003-3080-6286Chunhui Chung1https://orcid.org/0000-0003-1649-3121Lien-Kai Chang2Wei-Chih Su3Mi-Ching Tsai4https://orcid.org/0000-0003-0410-255XDepartment of Mechanical Engineering, National Cheng Kung University, Tainan, TaiwanDepartment of Mechanical Engineering, National Cheng Kung University, Tainan, TaiwanDepartment of Mechanical Engineering, National Cheng Kung University, Tainan, TaiwanNational Center for High-Performance Computing, National Applied Research Laboratories, Hsinchu, TaiwanDepartment of Mechanical Engineering, National Cheng Kung University, Tainan, TaiwanDue to the increasing demand of electrical vehicles (EVs), prognostics of the battery state is of paramount importance. The nonlinearity of the signal (e.g. voltage) results in the complexity of analyzing the degradation of the battery. Aging characteristics extracted from the voltage, current, and temperature when the battery is fully charged/discharged were commonly used by previous researchers to determine the battery state. The drawbacks of the previous prediction algorithms are insufficient or irrelevant features to explicitly model the battery aging and the use of fully charged/discharged datasets, which might result in poor prediction accuracy. Therefore, this study proposes a feature selection technique to adequately select optimum statistical feature subset and the use of partial charge/discharge data to determine the battery remaining useful life (RUL) using Recurrent Neural Network - Long Short-Term Memory (RNN-LSTM). The proposed approach demonstrated exceptional RUL prediction results, with the root mean square error (RMSE) of 0.00286 and mean average error (MAE) of 0.00222 using partial discharge data. The proposed method shows prediction improvement in comparison with the use of full data and state-of-the-art outcomes from previous studies of the same open data from the National Aeronautics and Space Administration (NASA) prognostic battery data sets.https://ieeexplore.ieee.org/document/9187619/Recurrent neural networklong short-term memoryremaining useful lifebattery management systemsfeature selection |
| spellingShingle | Benvolence Chinomona Chunhui Chung Lien-Kai Chang Wei-Chih Su Mi-Ching Tsai Long Short-Term Memory Approach to Estimate Battery Remaining Useful Life Using Partial Data IEEE Access Recurrent neural network long short-term memory remaining useful life battery management systems feature selection |
| title | Long Short-Term Memory Approach to Estimate Battery Remaining Useful Life Using Partial Data |
| title_full | Long Short-Term Memory Approach to Estimate Battery Remaining Useful Life Using Partial Data |
| title_fullStr | Long Short-Term Memory Approach to Estimate Battery Remaining Useful Life Using Partial Data |
| title_full_unstemmed | Long Short-Term Memory Approach to Estimate Battery Remaining Useful Life Using Partial Data |
| title_short | Long Short-Term Memory Approach to Estimate Battery Remaining Useful Life Using Partial Data |
| title_sort | long short term memory approach to estimate battery remaining useful life using partial data |
| topic | Recurrent neural network long short-term memory remaining useful life battery management systems feature selection |
| url | https://ieeexplore.ieee.org/document/9187619/ |
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