Mixing High-Frequency Bands Based on Wavelet Decomposition for Long-Term State-of-Charge Forecasting of Lithium-Ion Batteries
Although state-of-charge (SoC) forecasting has received considerable attention, long-term prediction remains a challenging task due to disrupted temporal dependencies and the neglect of battery signal characteristics. In this study, we propose a novel deep learning-based long-term SoC forecasting me...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11050379/ |
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| author | Yeseo Joo Chihyeon Choi Sangho Lee Youngdoo Son |
| author_facet | Yeseo Joo Chihyeon Choi Sangho Lee Youngdoo Son |
| author_sort | Yeseo Joo |
| collection | DOAJ |
| description | Although state-of-charge (SoC) forecasting has received considerable attention, long-term prediction remains a challenging task due to disrupted temporal dependencies and the neglect of battery signal characteristics. In this study, we propose a novel deep learning-based long-term SoC forecasting method that effectively captures temporal dynamics while preserving temporal order, thereby improving long-term predictive performance. Our approach first decomposes battery signals into low- and high-frequency bands using the discrete wavelet transform, enabling separate analyses of steady-state trends and localized events. Then, we introduce a feed-forward attention mechanism that selectively emphasizes informative high-frequency bands while suppressing irrelevant noise. Finally, we integrate the low- and high-frequency features generated solely by linear transformations that help maintain temporal structure and improve long-term forecasting accuracy. A series of experiments on a lithium-ion battery dataset demonstrate the superiority of the proposed method by achieving outstanding performance in long-term SoC forecasting. |
| format | Article |
| id | doaj-art-db8c0139e5194d6cba0e4e7ed0311c0e |
| institution | Matheson Library |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-db8c0139e5194d6cba0e4e7ed0311c0e2025-07-03T23:00:32ZengIEEEIEEE Access2169-35362025-01-011311167011168010.1109/ACCESS.2025.358321611050379Mixing High-Frequency Bands Based on Wavelet Decomposition for Long-Term State-of-Charge Forecasting of Lithium-Ion BatteriesYeseo Joo0https://orcid.org/0009-0002-7558-2642Chihyeon Choi1https://orcid.org/0009-0002-7325-1655Sangho Lee2https://orcid.org/0000-0002-7784-8515Youngdoo Son3https://orcid.org/0000-0002-1912-5853Department of Industrial and Systems Engineering, Dongguk University-Seoul, Seoul, Republic of KoreaDepartment of Industrial and Systems Engineering, Dongguk University-Seoul, Seoul, Republic of KoreaDepartment of Industrial and Systems Engineering, Dongguk University-Seoul, Seoul, Republic of KoreaDepartment of Industrial and Systems Engineering, Dongguk University-Seoul, Seoul, Republic of KoreaAlthough state-of-charge (SoC) forecasting has received considerable attention, long-term prediction remains a challenging task due to disrupted temporal dependencies and the neglect of battery signal characteristics. In this study, we propose a novel deep learning-based long-term SoC forecasting method that effectively captures temporal dynamics while preserving temporal order, thereby improving long-term predictive performance. Our approach first decomposes battery signals into low- and high-frequency bands using the discrete wavelet transform, enabling separate analyses of steady-state trends and localized events. Then, we introduce a feed-forward attention mechanism that selectively emphasizes informative high-frequency bands while suppressing irrelevant noise. Finally, we integrate the low- and high-frequency features generated solely by linear transformations that help maintain temporal structure and improve long-term forecasting accuracy. A series of experiments on a lithium-ion battery dataset demonstrate the superiority of the proposed method by achieving outstanding performance in long-term SoC forecasting.https://ieeexplore.ieee.org/document/11050379/State-of-charge forecastinglithium-ion batterieswavelet transformattention mechanismslinear transformations |
| spellingShingle | Yeseo Joo Chihyeon Choi Sangho Lee Youngdoo Son Mixing High-Frequency Bands Based on Wavelet Decomposition for Long-Term State-of-Charge Forecasting of Lithium-Ion Batteries IEEE Access State-of-charge forecasting lithium-ion batteries wavelet transform attention mechanisms linear transformations |
| title | Mixing High-Frequency Bands Based on Wavelet Decomposition for Long-Term State-of-Charge Forecasting of Lithium-Ion Batteries |
| title_full | Mixing High-Frequency Bands Based on Wavelet Decomposition for Long-Term State-of-Charge Forecasting of Lithium-Ion Batteries |
| title_fullStr | Mixing High-Frequency Bands Based on Wavelet Decomposition for Long-Term State-of-Charge Forecasting of Lithium-Ion Batteries |
| title_full_unstemmed | Mixing High-Frequency Bands Based on Wavelet Decomposition for Long-Term State-of-Charge Forecasting of Lithium-Ion Batteries |
| title_short | Mixing High-Frequency Bands Based on Wavelet Decomposition for Long-Term State-of-Charge Forecasting of Lithium-Ion Batteries |
| title_sort | mixing high frequency bands based on wavelet decomposition for long term state of charge forecasting of lithium ion batteries |
| topic | State-of-charge forecasting lithium-ion batteries wavelet transform attention mechanisms linear transformations |
| url | https://ieeexplore.ieee.org/document/11050379/ |
| work_keys_str_mv | AT yeseojoo mixinghighfrequencybandsbasedonwaveletdecompositionforlongtermstateofchargeforecastingoflithiumionbatteries AT chihyeonchoi mixinghighfrequencybandsbasedonwaveletdecompositionforlongtermstateofchargeforecastingoflithiumionbatteries AT sangholee mixinghighfrequencybandsbasedonwaveletdecompositionforlongtermstateofchargeforecastingoflithiumionbatteries AT youngdooson mixinghighfrequencybandsbasedonwaveletdecompositionforlongtermstateofchargeforecastingoflithiumionbatteries |