EV Battery Failure Prediction Data Across 5 Chemistries and 20 Brands

200,000 synthetic records of electric vehicle battery data generated using physics-informed methods. The dataset covers five different battery chemistries and includes data from 20 vehicle brands. It was sourced from Kaggle, but details on the author, license, and specific update date are not provided.

Use Cases

• Predicting battery failure modes based on synthetic physical parameters.
• Comparing degradation patterns across the five mentioned battery chemistries.
• Benchmarking anomaly detection algorithms using data from 20 different vehicle brands.
• Training physics-informed machine learning models for battery health estimation.

Strengths

• Contains 200,000 synthetic data points for model training.
• Covers five distinct battery chemistries for comparative analysis.
• Includes data from 20 different vehicle brands, suggesting diversity in source.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Data is synthetic, which may not perfectly capture real-world failure distributions.

Provenance

Source

Kaggle

Collection Method

Physics-informed synthetic data generation.