A Hybrid Deep Learning Method for the Estimation of the State of Health of Lithium-Ion Batteries
This paper proposes a method for estimating the state of health (SOH) of lithium-ion batteries (LIBs) using a combination of vision transformer (VIT) and gated recurrent unit (GRU) networks. The new scheme adopts a VIT to extract features from the battery measured data and incorporates a GRU network...
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | International Transactions on Electrical Energy Systems |
| Online Access: | http://dx.doi.org/10.1155/etep/2442893 |
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| _version_ | 1832590399918571520 |
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| author | Shuo Cheng |
| author_facet | Shuo Cheng |
| author_sort | Shuo Cheng |
| collection | DOAJ |
| description | This paper proposes a method for estimating the state of health (SOH) of lithium-ion batteries (LIBs) using a combination of vision transformer (VIT) and gated recurrent unit (GRU) networks. The new scheme adopts a VIT to extract features from the battery measured data and incorporates a GRU network to mitigate the limitations of the VIT caused by positional encoding. The resulting VIT-GRU network is designed to comprehensively capture information relevant to the battery SOH. Simulation experiments on the NASA dataset illustrate the notable results achieved by the VIT-GRU, with prediction root mean square error (RMSE) and mean absolute error (MAE) up to 0.54% and 0.38%, respectively, demonstrating the exceptional performance of the VIT-GRU network in SOH estimation. Compared to other complex deep learning (DL) methods, the VIT-GRU significantly outperforms them, according to the RMSE and MAE of the predicted values. |
| format | Article |
| id | doaj-art-73ea89e5a65b44e1afe11607ddfb6ded |
| institution | Kabale University |
| issn | 2050-7038 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Transactions on Electrical Energy Systems |
| spelling | doaj-art-73ea89e5a65b44e1afe11607ddfb6ded2025-01-24T00:00:04ZengWileyInternational Transactions on Electrical Energy Systems2050-70382025-01-01202510.1155/etep/2442893A Hybrid Deep Learning Method for the Estimation of the State of Health of Lithium-Ion BatteriesShuo Cheng0Department of ChemistryThis paper proposes a method for estimating the state of health (SOH) of lithium-ion batteries (LIBs) using a combination of vision transformer (VIT) and gated recurrent unit (GRU) networks. The new scheme adopts a VIT to extract features from the battery measured data and incorporates a GRU network to mitigate the limitations of the VIT caused by positional encoding. The resulting VIT-GRU network is designed to comprehensively capture information relevant to the battery SOH. Simulation experiments on the NASA dataset illustrate the notable results achieved by the VIT-GRU, with prediction root mean square error (RMSE) and mean absolute error (MAE) up to 0.54% and 0.38%, respectively, demonstrating the exceptional performance of the VIT-GRU network in SOH estimation. Compared to other complex deep learning (DL) methods, the VIT-GRU significantly outperforms them, according to the RMSE and MAE of the predicted values.http://dx.doi.org/10.1155/etep/2442893 |
| spellingShingle | Shuo Cheng A Hybrid Deep Learning Method for the Estimation of the State of Health of Lithium-Ion Batteries International Transactions on Electrical Energy Systems |
| title | A Hybrid Deep Learning Method for the Estimation of the State of Health of Lithium-Ion Batteries |
| title_full | A Hybrid Deep Learning Method for the Estimation of the State of Health of Lithium-Ion Batteries |
| title_fullStr | A Hybrid Deep Learning Method for the Estimation of the State of Health of Lithium-Ion Batteries |
| title_full_unstemmed | A Hybrid Deep Learning Method for the Estimation of the State of Health of Lithium-Ion Batteries |
| title_short | A Hybrid Deep Learning Method for the Estimation of the State of Health of Lithium-Ion Batteries |
| title_sort | hybrid deep learning method for the estimation of the state of health of lithium ion batteries |
| url | http://dx.doi.org/10.1155/etep/2442893 |
| work_keys_str_mv | AT shuocheng ahybriddeeplearningmethodfortheestimationofthestateofhealthoflithiumionbatteries AT shuocheng hybriddeeplearningmethodfortheestimationofthestateofhealthoflithiumionbatteries |