Lithium Battery Fault Diagnosis Experimental Parameters and Results

Experimental data supporting a study on lithium battery fault diagnosis. The dataset contains results from testing a model combining an optimized Empirical Mode Decomposition algorithm with a 2D Convolutional Neural Network. It was authored by Xiaofei Yin and last updated on March 17, 2026.

Use Cases

• Benchmarking fault diagnosis model performance based on reported accuracy and runtime metrics.
• Analyzing the relationship between feature count and model accuracy based on the described experimental results.
• Studying the application of Empirical Mode Decomposition for feature extraction from voltage signals.
• Training or validating convolutional neural networks for time-series classification of battery faults.

Strengths

• Model performance metrics are provided, including a 98.7% feature information consistency and a 99.2% recognition accuracy under specific conditions.
• Runtime performance is documented, with the highest reported time below 6 milliseconds across 7 data groups.
• Data is shared under a permissive CC-BY-4.0 license.

Limitations

• The dataset is very small at 5.5 KB, indicating limited scope.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

figshare, authored by Xiaofei Yin.

Collection Method

Data generated from experiments constructing a lithium battery fault diagnosis model.

Time Range

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Freshness

Last updated 2026-03-17 17:25:25; freshness should be verified.

Geography

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