Experiments to Diagnose SoH of Samsung INR21700-50E Li-ion Cells

77 Samsung INR21700-50E lithium-ion cells were experimentally evaluated to develop state-of-health diagnostic methods. The dataset results from the REBELION project, part of a Horizon Europe program, and was last updated in October 2025. It contains data from three novel diagnostic methods—electrochemical noise analysis, current steps, and high-frequency current steps—contrasted with conventional techniques.

Use Cases

• Compare novel diagnostic methods (ENA, CS, HF-CS) against conventional methods (ICA, OCV, EIS) based on the experimental protocol described.
• Train machine learning models to predict State of Health (SoH) based on electrochemical response features.
• Benchmark battery aging models using data from cells cycled to SoH levels between 100% and 80%.
• Develop fast diagnostic protocols for battery repurposing based on the project's stated requirements for time and equipment.

Strengths

• Focuses on 77 specific Samsung INR21700-50E cells, providing a controlled experimental sample.
• Explicitly contrasts three novel diagnostic methods with several conventional techniques, enabling comparative analysis.
• Cells were pre-aged to create a defined distribution of State of Health between 100% and 80%.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is noted as obsolete, superseded by a newer repository with additional data.

Provenance

Source

Rubio González, Miguel Angel via e-cienciaDatos Harvested Dataverse.

Collection Method

Experimental results from laboratory testing of 77 lithium-ion cells.

Time Range

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Freshness

Last updated 2025-10-14 21:43:51; freshness should be verified

Geography

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