State of Health Estimation Procedure for Lithium-Ion Batteries Using Partial Discharge Data and Support Vector Regression
Battery aging is a complex phenomenon, and precise state of health (SoH) monitoring is essential for effective battery management. This paper presents a data-driven method for SoH estimation based on support vector regression (SVR), utilizing features built from both full and partial discharge capac...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/1/206 |
| _version_ | 1797358882424619008 |
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| author | Emil Petkovski Iacopo Marri Loredana Cristaldi Marco Faifer |
| author_facet | Emil Petkovski Iacopo Marri Loredana Cristaldi Marco Faifer |
| author_sort | Emil Petkovski |
| collection | DOAJ |
| description | Battery aging is a complex phenomenon, and precise state of health (SoH) monitoring is essential for effective battery management. This paper presents a data-driven method for SoH estimation based on support vector regression (SVR), utilizing features built from both full and partial discharge capacity curves, as well as battery temperature data. It provides an in-depth discussion of the novel features constructed from different voltage intervals. Moreover, three combinations of features were analyzed, demonstrating how their efficacy changes across different voltage ranges. Successful results were obtained using the full discharge capacity curves, built from the full interval of 2 to 3.4 V and achieving a mean <i>R</i><sup>2</sup> value of 0.962 for the test set, thus showcasing the adequacy of the selected SVR strategy. Finally, the features constructed from the full voltage range were compared with ones built from 10 small voltage ranges. Similar success was observed, evidenced by a mean <i>R</i><sup>2</sup> value ranging between 0.939 and 0.973 across different voltage ranges. This indicates the practical applicability of the developed models in real-world scenarios. The tuning and evaluation of the proposed models were carried out using a substantial dataset created by Toyota, consisting of 124 lithium iron phosphate batteries. |
| first_indexed | 2024-03-08T15:07:39Z |
| format | Article |
| id | doaj.art-a30a40c2c4ee4bee9d7e8207b8bad18f |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-08T15:07:39Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-a30a40c2c4ee4bee9d7e8207b8bad18f2024-01-10T14:56:14ZengMDPI AGEnergies1996-10732023-12-0117120610.3390/en17010206State of Health Estimation Procedure for Lithium-Ion Batteries Using Partial Discharge Data and Support Vector RegressionEmil Petkovski0Iacopo Marri1Loredana Cristaldi2Marco Faifer3Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, ItalyDepartment of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, ItalyDepartment of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, ItalyDepartment of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, ItalyBattery aging is a complex phenomenon, and precise state of health (SoH) monitoring is essential for effective battery management. This paper presents a data-driven method for SoH estimation based on support vector regression (SVR), utilizing features built from both full and partial discharge capacity curves, as well as battery temperature data. It provides an in-depth discussion of the novel features constructed from different voltage intervals. Moreover, three combinations of features were analyzed, demonstrating how their efficacy changes across different voltage ranges. Successful results were obtained using the full discharge capacity curves, built from the full interval of 2 to 3.4 V and achieving a mean <i>R</i><sup>2</sup> value of 0.962 for the test set, thus showcasing the adequacy of the selected SVR strategy. Finally, the features constructed from the full voltage range were compared with ones built from 10 small voltage ranges. Similar success was observed, evidenced by a mean <i>R</i><sup>2</sup> value ranging between 0.939 and 0.973 across different voltage ranges. This indicates the practical applicability of the developed models in real-world scenarios. The tuning and evaluation of the proposed models were carried out using a substantial dataset created by Toyota, consisting of 124 lithium iron phosphate batteries.https://www.mdpi.com/1996-1073/17/1/206lithium-ion batterybattery degradationprognosticsmachine learningSoH |
| spellingShingle | Emil Petkovski Iacopo Marri Loredana Cristaldi Marco Faifer State of Health Estimation Procedure for Lithium-Ion Batteries Using Partial Discharge Data and Support Vector Regression Energies lithium-ion battery battery degradation prognostics machine learning SoH |
| title | State of Health Estimation Procedure for Lithium-Ion Batteries Using Partial Discharge Data and Support Vector Regression |
| title_full | State of Health Estimation Procedure for Lithium-Ion Batteries Using Partial Discharge Data and Support Vector Regression |
| title_fullStr | State of Health Estimation Procedure for Lithium-Ion Batteries Using Partial Discharge Data and Support Vector Regression |
| title_full_unstemmed | State of Health Estimation Procedure for Lithium-Ion Batteries Using Partial Discharge Data and Support Vector Regression |
| title_short | State of Health Estimation Procedure for Lithium-Ion Batteries Using Partial Discharge Data and Support Vector Regression |
| title_sort | state of health estimation procedure for lithium ion batteries using partial discharge data and support vector regression |
| topic | lithium-ion battery battery degradation prognostics machine learning SoH |
| url | https://www.mdpi.com/1996-1073/17/1/206 |
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