State-of-Health Prediction of Lithium-Ion Batteries Using Exponential Smoothing Transformer With Seasonal and Growth Embedding
In the world of modern energy, Lithium-Ion batteries reign supreme, offering rechargeability, sustainability, and long-term energy storage. However, their lifespan is not infinite, calling for accurate prediction of remaining life under various conditions. Deep learning shines in this domain, with t...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10412053/ |
| _version_ | 1797335537394122752 |
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| author | Muhammad Rifqi Fauzi Novanto Yudistira Wayan Firdaus Mahmudy |
| author_facet | Muhammad Rifqi Fauzi Novanto Yudistira Wayan Firdaus Mahmudy |
| author_sort | Muhammad Rifqi Fauzi |
| collection | DOAJ |
| description | In the world of modern energy, Lithium-Ion batteries reign supreme, offering rechargeability, sustainability, and long-term energy storage. However, their lifespan is not infinite, calling for accurate prediction of remaining life under various conditions. Deep learning shines in this domain, with the Transformer architecture blossoming as a powerful tool for time series forecasting. This research dives into data collection, processing, model design, training, and evaluation, making key methodological contributions to battery life prediction. Notably, the SGEformer model, a Transformer enhanced with growth and seasonal embedding, emerges as a groundbreaking innovation. Comparing SGEformer to ETSformer, Informer, Reformer, Transformer, and LSTM reveals its unique strengths. With an impressive MSE score of 0.000117, SGEformer establishes itself as a highly effective tool for battery life prediction, highlighting the value of growth and seasonal embedding in boosting accuracy. This research propels the state-of-the-art Lithium-Ion battery state-of-health prediction, offering a robust methodological foundation for precise and reliable forecasts. Code can be accessed at <uri>https://github.com/MRifqiFz/SGEformer</uri>. |
| first_indexed | 2024-03-08T08:39:49Z |
| format | Article |
| id | doaj.art-e4b438a5d6ee4922a3daa693b4d9528c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T08:39:49Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e4b438a5d6ee4922a3daa693b4d9528c2024-02-02T00:02:35ZengIEEEIEEE Access2169-35362024-01-0112146591467010.1109/ACCESS.2024.335773610412053State-of-Health Prediction of Lithium-Ion Batteries Using Exponential Smoothing Transformer With Seasonal and Growth EmbeddingMuhammad Rifqi Fauzi0Novanto Yudistira1https://orcid.org/0000-0001-5330-5930Wayan Firdaus Mahmudy2https://orcid.org/0000-0002-0965-206XDepartment of Informatics Engineering, Faculty of Computer Science, Brawijaya University, Kota Malang, IndonesiaDepartment of Informatics Engineering, Faculty of Computer Science, Brawijaya University, Kota Malang, IndonesiaDepartment of Informatics Engineering, Faculty of Computer Science, Brawijaya University, Kota Malang, IndonesiaIn the world of modern energy, Lithium-Ion batteries reign supreme, offering rechargeability, sustainability, and long-term energy storage. However, their lifespan is not infinite, calling for accurate prediction of remaining life under various conditions. Deep learning shines in this domain, with the Transformer architecture blossoming as a powerful tool for time series forecasting. This research dives into data collection, processing, model design, training, and evaluation, making key methodological contributions to battery life prediction. Notably, the SGEformer model, a Transformer enhanced with growth and seasonal embedding, emerges as a groundbreaking innovation. Comparing SGEformer to ETSformer, Informer, Reformer, Transformer, and LSTM reveals its unique strengths. With an impressive MSE score of 0.000117, SGEformer establishes itself as a highly effective tool for battery life prediction, highlighting the value of growth and seasonal embedding in boosting accuracy. This research propels the state-of-the-art Lithium-Ion battery state-of-health prediction, offering a robust methodological foundation for precise and reliable forecasts. Code can be accessed at <uri>https://github.com/MRifqiFz/SGEformer</uri>.https://ieeexplore.ieee.org/document/10412053/Time series forecastinglithium-ion batteryseasonal embeddinggrowth embeddingtransformerstate-of-health (SoH) |
| spellingShingle | Muhammad Rifqi Fauzi Novanto Yudistira Wayan Firdaus Mahmudy State-of-Health Prediction of Lithium-Ion Batteries Using Exponential Smoothing Transformer With Seasonal and Growth Embedding IEEE Access Time series forecasting lithium-ion battery seasonal embedding growth embedding transformer state-of-health (SoH) |
| title | State-of-Health Prediction of Lithium-Ion Batteries Using Exponential Smoothing Transformer With Seasonal and Growth Embedding |
| title_full | State-of-Health Prediction of Lithium-Ion Batteries Using Exponential Smoothing Transformer With Seasonal and Growth Embedding |
| title_fullStr | State-of-Health Prediction of Lithium-Ion Batteries Using Exponential Smoothing Transformer With Seasonal and Growth Embedding |
| title_full_unstemmed | State-of-Health Prediction of Lithium-Ion Batteries Using Exponential Smoothing Transformer With Seasonal and Growth Embedding |
| title_short | State-of-Health Prediction of Lithium-Ion Batteries Using Exponential Smoothing Transformer With Seasonal and Growth Embedding |
| title_sort | state of health prediction of lithium ion batteries using exponential smoothing transformer with seasonal and growth embedding |
| topic | Time series forecasting lithium-ion battery seasonal embedding growth embedding transformer state-of-health (SoH) |
| url | https://ieeexplore.ieee.org/document/10412053/ |
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