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: Shuo Cheng
Format: Article
Language:English
Published: Wiley 2025-01-01
Series:International Transactions on Electrical Energy Systems
Online Access:http://dx.doi.org/10.1155/etep/2442893
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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.
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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
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