Machine learning for battery quality classification and lifetime prediction using formation data
Accurate classification of battery quality and prediction of battery lifetime before leaving the factory would bring economic and safety benefits. Here, we propose a data-driven approach with machine learning to classify the battery quality and predict the battery lifetime before usage only using fo...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Energy and AI |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666546824001174 |
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| author | Jiayu Zou Yingbo Gao Moritz H. Frieges Martin F. Börner Achim Kampker Weihan Li |
| author_facet | Jiayu Zou Yingbo Gao Moritz H. Frieges Martin F. Börner Achim Kampker Weihan Li |
| author_sort | Jiayu Zou |
| collection | DOAJ |
| description | Accurate classification of battery quality and prediction of battery lifetime before leaving the factory would bring economic and safety benefits. Here, we propose a data-driven approach with machine learning to classify the battery quality and predict the battery lifetime before usage only using formation data. We extract three classes of features from the raw formation data, considering the statistical aspects, differential analysis, and electrochemical characteristics. The correlation between over 100 extracted features and the battery lifetime is analysed based on the ageing mechanisms. Machine learning models are developed to classify battery quality and predict battery lifetime by features with a high correlation with battery ageing. The validation results show that the quality classification model achieved accuracies of 89.74% and 89.47% for the batteries aged at 25°C and 45°C, respectively. Moreover, the lifetime prediction model is able to predict the battery end-of-life with mean percentage errors of 6.50% and 5.45% for the batteries aged at 25°C and 45°C, respectively. This work highlights the potential of battery formation data from production lines in quality classification and lifetime prediction. |
| format | Article |
| id | doaj-art-ee70a1a2032b4f708af2f82f3a6e0489 |
| institution | Kabale University |
| issn | 2666-5468 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy and AI |
| spelling | doaj-art-ee70a1a2032b4f708af2f82f3a6e04892024-12-18T08:53:08ZengElsevierEnergy and AI2666-54682024-12-0118100451Machine learning for battery quality classification and lifetime prediction using formation dataJiayu Zou0Yingbo Gao1Moritz H. Frieges2Martin F. Börner3Achim Kampker4Weihan Li5Chair for Electrochemical Energy Conversion and Storage Systems, Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University, Campus-Boulevard 89, 52074 Aachen, Germany; Center for Ageing, Reliability and Lifetime Prediction of Electrochemical and Power Electronic Systems (CARL), RWTH Aachen University, Campus-Boulevard 89, Aachen, Germany; Juelich Aachen Research Alliance, JARA-Energy, GermanyDepartment of Computer Science, RWTH Aachen University, 52056 Aachen, GermanyChair of Production Engineering of E-Mobility Components (PEM), RWTH Aachen University, Bohr 12, 52072 Aachen, GermanyChair for Electrochemical Energy Conversion and Storage Systems, Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University, Campus-Boulevard 89, 52074 Aachen, Germany; Center for Ageing, Reliability and Lifetime Prediction of Electrochemical and Power Electronic Systems (CARL), RWTH Aachen University, Campus-Boulevard 89, Aachen, Germany; Juelich Aachen Research Alliance, JARA-Energy, GermanyChair of Production Engineering of E-Mobility Components (PEM), RWTH Aachen University, Bohr 12, 52072 Aachen, GermanyChair for Electrochemical Energy Conversion and Storage Systems, Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University, Campus-Boulevard 89, 52074 Aachen, Germany; Center for Ageing, Reliability and Lifetime Prediction of Electrochemical and Power Electronic Systems (CARL), RWTH Aachen University, Campus-Boulevard 89, Aachen, Germany; Juelich Aachen Research Alliance, JARA-Energy, Germany; Corresponding author.Accurate classification of battery quality and prediction of battery lifetime before leaving the factory would bring economic and safety benefits. Here, we propose a data-driven approach with machine learning to classify the battery quality and predict the battery lifetime before usage only using formation data. We extract three classes of features from the raw formation data, considering the statistical aspects, differential analysis, and electrochemical characteristics. The correlation between over 100 extracted features and the battery lifetime is analysed based on the ageing mechanisms. Machine learning models are developed to classify battery quality and predict battery lifetime by features with a high correlation with battery ageing. The validation results show that the quality classification model achieved accuracies of 89.74% and 89.47% for the batteries aged at 25°C and 45°C, respectively. Moreover, the lifetime prediction model is able to predict the battery end-of-life with mean percentage errors of 6.50% and 5.45% for the batteries aged at 25°C and 45°C, respectively. This work highlights the potential of battery formation data from production lines in quality classification and lifetime prediction.http://www.sciencedirect.com/science/article/pii/S2666546824001174BatteryFormationQuality classificationLife predictionMachine learning |
| spellingShingle | Jiayu Zou Yingbo Gao Moritz H. Frieges Martin F. Börner Achim Kampker Weihan Li Machine learning for battery quality classification and lifetime prediction using formation data Energy and AI Battery Formation Quality classification Life prediction Machine learning |
| title | Machine learning for battery quality classification and lifetime prediction using formation data |
| title_full | Machine learning for battery quality classification and lifetime prediction using formation data |
| title_fullStr | Machine learning for battery quality classification and lifetime prediction using formation data |
| title_full_unstemmed | Machine learning for battery quality classification and lifetime prediction using formation data |
| title_short | Machine learning for battery quality classification and lifetime prediction using formation data |
| title_sort | machine learning for battery quality classification and lifetime prediction using formation data |
| topic | Battery Formation Quality classification Life prediction Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2666546824001174 |
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