Charge Diagnostics and State Estimation of Battery Energy Storage Systems Through Transformer Models
With the continuous development of Artificial Intelligence (AI), designing accurate algorithms that provide diagnostics and maintenance of energy technologies is a challenging task in the energy transition domain. This research work focuses on the implementation of Transformer models for charge diag...
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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/10849558/ |
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| author | Rolando Antonio Gilbert Zequera Anton Rassolkin Toomas Vaimann Ants Kallaste |
| author_facet | Rolando Antonio Gilbert Zequera Anton Rassolkin Toomas Vaimann Ants Kallaste |
| author_sort | Rolando Antonio Gilbert Zequera |
| collection | DOAJ |
| description | With the continuous development of Artificial Intelligence (AI), designing accurate algorithms that provide diagnostics and maintenance of energy technologies is a challenging task in the energy transition domain. This research work focuses on the implementation of Transformer models for charge diagnostics and algorithm design of Battery Energy Storage Systems (BESSs). Experimentally, two Lithium-ion (Li-ion) battery cells were tested using a programmable DC electronic load to evaluate charge indicators, and 20 battery tests were performed for each cell. Filter, Wrapper, and Embedded methods are the techniques implemented to achieve Feature Selection and illustrate Key Performance Indicators (KPIs) in battery testing. Time series and state estimation are the Supervised Learning techniques executed for charge diagnostics and State of Charge (SOC) predictions. The results show remarkable performance metrics of the Transformer models, achieving over 94% accuracy in Model evaluation compared to traditional Deep Learning algorithms. |
| format | Article |
| id | doaj-art-d56c8d21d8014037b67552383c301953 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-d56c8d21d8014037b67552383c3019532025-01-31T23:04:26ZengIEEEIEEE Access2169-35362025-01-0113177331774410.1109/ACCESS.2025.353285810849558Charge Diagnostics and State Estimation of Battery Energy Storage Systems Through Transformer ModelsRolando Antonio Gilbert Zequera0https://orcid.org/0000-0002-2052-8481Anton Rassolkin1https://orcid.org/0000-0001-8035-3970Toomas Vaimann2https://orcid.org/0000-0003-0481-5066Ants Kallaste3https://orcid.org/0000-0001-6126-1878Department of Electrical Power Engineering and Mechatronics Department, Tallinn University of Technology, Tallinn, EstoniaDepartment of Electrical Power Engineering and Mechatronics Department, Tallinn University of Technology, Tallinn, EstoniaDepartment of Electrical Power Engineering and Mechatronics Department, Tallinn University of Technology, Tallinn, EstoniaDepartment of Electrical Power Engineering and Mechatronics Department, Tallinn University of Technology, Tallinn, EstoniaWith the continuous development of Artificial Intelligence (AI), designing accurate algorithms that provide diagnostics and maintenance of energy technologies is a challenging task in the energy transition domain. This research work focuses on the implementation of Transformer models for charge diagnostics and algorithm design of Battery Energy Storage Systems (BESSs). Experimentally, two Lithium-ion (Li-ion) battery cells were tested using a programmable DC electronic load to evaluate charge indicators, and 20 battery tests were performed for each cell. Filter, Wrapper, and Embedded methods are the techniques implemented to achieve Feature Selection and illustrate Key Performance Indicators (KPIs) in battery testing. Time series and state estimation are the Supervised Learning techniques executed for charge diagnostics and State of Charge (SOC) predictions. The results show remarkable performance metrics of the Transformer models, achieving over 94% accuracy in Model evaluation compared to traditional Deep Learning algorithms.https://ieeexplore.ieee.org/document/10849558/Deep learningneural networksbattery energy storage system |
| spellingShingle | Rolando Antonio Gilbert Zequera Anton Rassolkin Toomas Vaimann Ants Kallaste Charge Diagnostics and State Estimation of Battery Energy Storage Systems Through Transformer Models IEEE Access Deep learning neural networks battery energy storage system |
| title | Charge Diagnostics and State Estimation of Battery Energy Storage Systems Through Transformer Models |
| title_full | Charge Diagnostics and State Estimation of Battery Energy Storage Systems Through Transformer Models |
| title_fullStr | Charge Diagnostics and State Estimation of Battery Energy Storage Systems Through Transformer Models |
| title_full_unstemmed | Charge Diagnostics and State Estimation of Battery Energy Storage Systems Through Transformer Models |
| title_short | Charge Diagnostics and State Estimation of Battery Energy Storage Systems Through Transformer Models |
| title_sort | charge diagnostics and state estimation of battery energy storage systems through transformer models |
| topic | Deep learning neural networks battery energy storage system |
| url | https://ieeexplore.ieee.org/document/10849558/ |
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