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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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
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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| Summary: | 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. |
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| ISSN: | 2050-7038 |