Identification of cell chemistries in lithium-ion batteries: Improving the assessment for recycling and second-life
Recycling and second life of lithium-ion batteries are vital for lowering the growing resource demand of sectors like mobility or home energy storage. However, an often-overlooked issue is the sometimes-unknown cell chemistry of batteries entering the end-of-life. In this work, a machine learning ba...
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Elsevier
2025-01-01
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| Series: | Energy and AI |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666546824001344 |
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| author | Christopher Wett Jörg Lampe Dominik Görick Thomas Seeger Bugra Turan |
| author_facet | Christopher Wett Jörg Lampe Dominik Görick Thomas Seeger Bugra Turan |
| author_sort | Christopher Wett |
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| description | Recycling and second life of lithium-ion batteries are vital for lowering the growing resource demand of sectors like mobility or home energy storage. However, an often-overlooked issue is the sometimes-unknown cell chemistry of batteries entering the end-of-life. In this work, a machine learning based approach for the identification of lithium-ion battery cathode chemistries is presented. First, an initial measurement boundary determination is introduced. Using the Python Battery Mathematical Modelling (PyBaMM) framework, synthetical partial open circuit voltage (OCV) charge and discharge curves are generated with an electrochemical single particle model for three different cathode chemistries and the initial state of charge and state of health values as well as the initial capacities are varied. The dV/dQ characteristics are chosen as features and four machine learning algorithms are trained on different lengths of OCV curves. The trade-off between achievable accuracy and the number of OCV steps showed that an increasing accuracy correlates with a higher step number. While extremely small charge and discharge capacities per step did not yield sufficient testing accuracies, capacities starting from 0.2 Ah per step up to 0.6 Ah per step showed increasingly good results with an accuracy of up to 89.3 % for 0.5 Ah and 15 OCV steps. Additionally, the approach was validated by classifying experimental data. The results especially demonstrate the effectiveness of the approach to distinguish between lithium iron phosphate (LFP) and lithium nickel manganese cobalt (NMC) cells. |
| format | Article |
| id | doaj-art-28f06afbcbe14f3b86d388b636f8c5be |
| institution | Kabale University |
| issn | 2666-5468 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
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| series | Energy and AI |
| spelling | doaj-art-28f06afbcbe14f3b86d388b636f8c5be2025-01-27T04:22:22ZengElsevierEnergy and AI2666-54682025-01-0119100468Identification of cell chemistries in lithium-ion batteries: Improving the assessment for recycling and second-lifeChristopher Wett0Jörg Lampe1Dominik Görick2Thomas Seeger3Bugra Turan4Rheinische Hochschule Köln University of Applied Sciences, Vogelsanger Str. 295, 50825, Cologne, North-Rhine-Westphalia, Germany; Corresponding author.Rheinische Hochschule Köln University of Applied Sciences, Vogelsanger Str. 295, 50825, Cologne, North-Rhine-Westphalia, GermanyRheinische Hochschule Köln University of Applied Sciences, Vogelsanger Str. 295, 50825, Cologne, North-Rhine-Westphalia, GermanyUniversity of Siegen, Institute of Engineering Thermodynamics, Paul-Bonatz-Str. 9-11, 57076, Siegen, North-Rhine-Westphalia, GermanyRheinische Hochschule Köln University of Applied Sciences, Vogelsanger Str. 295, 50825, Cologne, North-Rhine-Westphalia, GermanyRecycling and second life of lithium-ion batteries are vital for lowering the growing resource demand of sectors like mobility or home energy storage. However, an often-overlooked issue is the sometimes-unknown cell chemistry of batteries entering the end-of-life. In this work, a machine learning based approach for the identification of lithium-ion battery cathode chemistries is presented. First, an initial measurement boundary determination is introduced. Using the Python Battery Mathematical Modelling (PyBaMM) framework, synthetical partial open circuit voltage (OCV) charge and discharge curves are generated with an electrochemical single particle model for three different cathode chemistries and the initial state of charge and state of health values as well as the initial capacities are varied. The dV/dQ characteristics are chosen as features and four machine learning algorithms are trained on different lengths of OCV curves. The trade-off between achievable accuracy and the number of OCV steps showed that an increasing accuracy correlates with a higher step number. While extremely small charge and discharge capacities per step did not yield sufficient testing accuracies, capacities starting from 0.2 Ah per step up to 0.6 Ah per step showed increasingly good results with an accuracy of up to 89.3 % for 0.5 Ah and 15 OCV steps. Additionally, the approach was validated by classifying experimental data. The results especially demonstrate the effectiveness of the approach to distinguish between lithium iron phosphate (LFP) and lithium nickel manganese cobalt (NMC) cells.http://www.sciencedirect.com/science/article/pii/S2666546824001344Lithium-ion batteriesSecond-lifeRecyclingCell chemistryIdentificationMachine learning |
| spellingShingle | Christopher Wett Jörg Lampe Dominik Görick Thomas Seeger Bugra Turan Identification of cell chemistries in lithium-ion batteries: Improving the assessment for recycling and second-life Energy and AI Lithium-ion batteries Second-life Recycling Cell chemistry Identification Machine learning |
| title | Identification of cell chemistries in lithium-ion batteries: Improving the assessment for recycling and second-life |
| title_full | Identification of cell chemistries in lithium-ion batteries: Improving the assessment for recycling and second-life |
| title_fullStr | Identification of cell chemistries in lithium-ion batteries: Improving the assessment for recycling and second-life |
| title_full_unstemmed | Identification of cell chemistries in lithium-ion batteries: Improving the assessment for recycling and second-life |
| title_short | Identification of cell chemistries in lithium-ion batteries: Improving the assessment for recycling and second-life |
| title_sort | identification of cell chemistries in lithium ion batteries improving the assessment for recycling and second life |
| topic | Lithium-ion batteries Second-life Recycling Cell chemistry Identification Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2666546824001344 |
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