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BatteryLake: Agentic, Physics-Grounded Curation of Heterogeneous Battery Aging Data and Benchmarking

Jul 14, 2026

BatteryLake is a governed data lakehouse that uses LLM agents and human-in-the-loop verification to curate public battery aging datasets into benchmark-ready assets. It standardizes 41 datasets from over 25 institutions using schema, statistical, and physical-plausibility rules, and releases an open benchmark for SOH and RUL tasks.

Why it matters: This work addresses the challenge of inconsistent battery aging data by automating curation with domain-specific semantics, enabling reproducible research and benchmarking in battery health management.

Full story at: arXiv AI/ML ↗

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