Data on battery health and performance: Analysing Samsung INR21700-50E cells with advanced feature engineering
This dataset provides a comprehensive collection of detailed measurements from 256 Samsung INR21700-50E cells, spanning 32 batches. It uniquely combines raw data and engineered features derived from charge-discharge cycles and Hybrid Pulse Power Characterization tests. The engineered features—such a...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-04-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340925000782 |
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| author | Sahar Qaadan Aiman Alshare Alexander Popp Myrel Tiemann Utz Spaeth Benedikt Schmuelling |
| author_facet | Sahar Qaadan Aiman Alshare Alexander Popp Myrel Tiemann Utz Spaeth Benedikt Schmuelling |
| author_sort | Sahar Qaadan |
| collection | DOAJ |
| description | This dataset provides a comprehensive collection of detailed measurements from 256 Samsung INR21700-50E cells, spanning 32 batches. It uniquely combines raw data and engineered features derived from charge-discharge cycles and Hybrid Pulse Power Characterization tests. The engineered features—such as State of Health, internal resistance, capacity fade, and energy efficiency—offer critical insights into battery health and aging processes. These features are indispensable for predictive modelling, lifecycle management, and battery performance optimization, addressing key challenges in battery technology. This dataset is particularly valuable for advanced machine learning applications, enabling accurate battery state-of-health estimation and predictive maintenance. The engineered features, including cumulative cycles and dynamic resistance, further enhance the dataset's capacity to model battery behavior under diverse conditions. With batch-specific organisation and CSV format, this dataset facilitates seamless integration into a wide range of analyses, making it a vital resource for researchers and engineers focusing on battery degradation, energy storage systems, and developing robust predictive models for real-world applications. |
| format | Article |
| id | doaj-art-b12b8f510af940e9bb1e3c76560f9b4f |
| institution | Kabale University |
| issn | 2352-3409 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj-art-b12b8f510af940e9bb1e3c76560f9b4f2025-02-06T05:11:59ZengElsevierData in Brief2352-34092025-04-0159111346Data on battery health and performance: Analysing Samsung INR21700-50E cells with advanced feature engineeringSahar Qaadan0Aiman Alshare1Alexander Popp2Myrel Tiemann3Utz Spaeth4Benedikt Schmuelling5Mechatronics Engineering, German Jordanian University, Amman, Jordan; Institute of Electric Mobility and Energy Storage Systems, University of Wuppertal, Wuppertal, Germany; Corresponding author at: Mechatronics Engineering, German Jordanian University, Amman, Jordan.Mechanical and Maintenance Engineering, German Jordanian University, Amman, JordanInstitute of Electric Mobility and Energy Storage Systems, University of Wuppertal, Wuppertal, GermanyInstitute of Electric Mobility and Energy Storage Systems, University of Wuppertal, Wuppertal, GermanyInstitute of Electric Mobility and Energy Storage Systems, University of Wuppertal, Wuppertal, GermanyInstitute of Electric Mobility and Energy Storage Systems, University of Wuppertal, Wuppertal, GermanyThis dataset provides a comprehensive collection of detailed measurements from 256 Samsung INR21700-50E cells, spanning 32 batches. It uniquely combines raw data and engineered features derived from charge-discharge cycles and Hybrid Pulse Power Characterization tests. The engineered features—such as State of Health, internal resistance, capacity fade, and energy efficiency—offer critical insights into battery health and aging processes. These features are indispensable for predictive modelling, lifecycle management, and battery performance optimization, addressing key challenges in battery technology. This dataset is particularly valuable for advanced machine learning applications, enabling accurate battery state-of-health estimation and predictive maintenance. The engineered features, including cumulative cycles and dynamic resistance, further enhance the dataset's capacity to model battery behavior under diverse conditions. With batch-specific organisation and CSV format, this dataset facilitates seamless integration into a wide range of analyses, making it a vital resource for researchers and engineers focusing on battery degradation, energy storage systems, and developing robust predictive models for real-world applications.http://www.sciencedirect.com/science/article/pii/S2352340925000782Battery datasetFeature engineeringState of healthPredictive modellingSamsung INR21700-50E |
| spellingShingle | Sahar Qaadan Aiman Alshare Alexander Popp Myrel Tiemann Utz Spaeth Benedikt Schmuelling Data on battery health and performance: Analysing Samsung INR21700-50E cells with advanced feature engineering Data in Brief Battery dataset Feature engineering State of health Predictive modelling Samsung INR21700-50E |
| title | Data on battery health and performance: Analysing Samsung INR21700-50E cells with advanced feature engineering |
| title_full | Data on battery health and performance: Analysing Samsung INR21700-50E cells with advanced feature engineering |
| title_fullStr | Data on battery health and performance: Analysing Samsung INR21700-50E cells with advanced feature engineering |
| title_full_unstemmed | Data on battery health and performance: Analysing Samsung INR21700-50E cells with advanced feature engineering |
| title_short | Data on battery health and performance: Analysing Samsung INR21700-50E cells with advanced feature engineering |
| title_sort | data on battery health and performance analysing samsung inr21700 50e cells with advanced feature engineering |
| topic | Battery dataset Feature engineering State of health Predictive modelling Samsung INR21700-50E |
| url | http://www.sciencedirect.com/science/article/pii/S2352340925000782 |
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