Multi-Manufacturer EV Battery Time-Series Dataset for Health and Faults

A large-scale time-series dataset tracks the performance of lithium-ion batteries from multiple manufacturers. It contains measurements for state of health (SOH), state of charge (SOC), and fault conditions. The dataset is sourced from Kaggle, but the author, organization, and specific update date are unknown.

Use Cases

• Predict State of Health (SOH) from time-series voltage and current measurements.
• Classify fault types using time-series data on temperature and internal resistance.
• Estimate State of Charge (SOC) by analyzing time-series discharge/charge cycle patterns.
• Train anomaly detection models to identify early signs of battery degradation from sensor time-series.

Strengths

• Large-scale collection of battery data.
• Includes data from multiple battery manufacturers.
• Contains time-series data for key metrics like SOH and SOC.

Limitations

• The exact number of rows, columns, and sample size is unknown, limiting reproducibility.
• The dataset's temporal coverage is unspecified, making it difficult to assess relevance for current battery chemistries.
• The synthetic nature of the data, as indicated by a platform tag, may not fully represent real-world operational conditions.

Provenance

Source

Kaggle

Time Range

5 years (implied by title)