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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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-04-01
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| Series: | Data in Brief |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340925000782 |
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| Summary: | 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. |
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| ISSN: | 2352-3409 |