Recurrent Neural Networks for Estimating the State of Health of Lithium-Ion Batteries
Rapid technological changes and disruptive innovations have resulted in a significant shift in people’s behavior and requirements. Electronic gadgets, including smartphones, notebooks, and other devices, are indispensable to everyday routines. Consequently, the demand for high-capacity batteries has...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Batteries |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-0105/10/3/111 |
| _version_ | 1797242050322628608 |
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| author | Rafael S. D. Teixeira Rodrigo F. Calili Maria Fatima Almeida Daniel R. Louzada |
| author_facet | Rafael S. D. Teixeira Rodrigo F. Calili Maria Fatima Almeida Daniel R. Louzada |
| author_sort | Rafael S. D. Teixeira |
| collection | DOAJ |
| description | Rapid technological changes and disruptive innovations have resulted in a significant shift in people’s behavior and requirements. Electronic gadgets, including smartphones, notebooks, and other devices, are indispensable to everyday routines. Consequently, the demand for high-capacity batteries has surged, which has enabled extended device autonomy. An alternative approach to address this demand is battery swapping, which can potentially extend the battery life of electronic devices. Although battery sharing in electric vehicles has been well studied, smartphone applications still need to be explored. Crucially, assessing the batteries’ state of health (SoH) presents a challenge, necessitating consensus on the best estimation methods to develop effective battery swap strategies. This paper proposes a model for estimating the SoH curve of lithium-ion batteries using the state of charge curve. The model was designed for smartphone battery swap applications utilizing Gated Recurrent Unit (GRU) neural networks. To validate the model, a system was developed to conduct destructive tests on batteries and study their behavior over their lifetimes. The results demonstrated the high precision of the model in estimating the SoH of batteries under various charge and discharge parameters. The proposed approach exhibits low computational complexity, low cost, and easily measurable input parameters, making it an attractive solution for smartphone battery swap applications. |
| first_indexed | 2024-04-24T18:33:03Z |
| format | Article |
| id | doaj.art-1ea298b82d2a43029cee01b401408fb1 |
| institution | Directory Open Access Journal |
| issn | 2313-0105 |
| language | English |
| last_indexed | 2024-04-24T18:33:03Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Batteries |
| spelling | doaj.art-1ea298b82d2a43029cee01b401408fb12024-03-27T13:21:17ZengMDPI AGBatteries2313-01052024-03-0110311110.3390/batteries10030111Recurrent Neural Networks for Estimating the State of Health of Lithium-Ion BatteriesRafael S. D. Teixeira0Rodrigo F. Calili1Maria Fatima Almeida2Daniel R. Louzada3Postgraduate Programme in Metrology, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro 22451-900, RJ, BrazilPostgraduate Programme in Metrology, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro 22451-900, RJ, BrazilPostgraduate Programme in Metrology, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro 22451-900, RJ, BrazilPostgraduate Programme in Metrology, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro 22451-900, RJ, BrazilRapid technological changes and disruptive innovations have resulted in a significant shift in people’s behavior and requirements. Electronic gadgets, including smartphones, notebooks, and other devices, are indispensable to everyday routines. Consequently, the demand for high-capacity batteries has surged, which has enabled extended device autonomy. An alternative approach to address this demand is battery swapping, which can potentially extend the battery life of electronic devices. Although battery sharing in electric vehicles has been well studied, smartphone applications still need to be explored. Crucially, assessing the batteries’ state of health (SoH) presents a challenge, necessitating consensus on the best estimation methods to develop effective battery swap strategies. This paper proposes a model for estimating the SoH curve of lithium-ion batteries using the state of charge curve. The model was designed for smartphone battery swap applications utilizing Gated Recurrent Unit (GRU) neural networks. To validate the model, a system was developed to conduct destructive tests on batteries and study their behavior over their lifetimes. The results demonstrated the high precision of the model in estimating the SoH of batteries under various charge and discharge parameters. The proposed approach exhibits low computational complexity, low cost, and easily measurable input parameters, making it an attractive solution for smartphone battery swap applications.https://www.mdpi.com/2313-0105/10/3/111lithium-ion batterystate of healthstate of chargerecurrent neural networkgated recurrent unit neural networkdestructive tests |
| spellingShingle | Rafael S. D. Teixeira Rodrigo F. Calili Maria Fatima Almeida Daniel R. Louzada Recurrent Neural Networks for Estimating the State of Health of Lithium-Ion Batteries Batteries lithium-ion battery state of health state of charge recurrent neural network gated recurrent unit neural network destructive tests |
| title | Recurrent Neural Networks for Estimating the State of Health of Lithium-Ion Batteries |
| title_full | Recurrent Neural Networks for Estimating the State of Health of Lithium-Ion Batteries |
| title_fullStr | Recurrent Neural Networks for Estimating the State of Health of Lithium-Ion Batteries |
| title_full_unstemmed | Recurrent Neural Networks for Estimating the State of Health of Lithium-Ion Batteries |
| title_short | Recurrent Neural Networks for Estimating the State of Health of Lithium-Ion Batteries |
| title_sort | recurrent neural networks for estimating the state of health of lithium ion batteries |
| topic | lithium-ion battery state of health state of charge recurrent neural network gated recurrent unit neural network destructive tests |
| url | https://www.mdpi.com/2313-0105/10/3/111 |
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