BatteryLake：基于物理原理的异构电池老化数据智能管理与基准测试
BatteryLake: Agentic, Physics-Grounded Curation of Heterogeneous Battery Aging Data and Benchmarking
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中文总结 AI 辅助
针对公共电池老化数据集应用受限问题，BatteryLake通过大语言模型智能体、人在回路机制等，将原始数据转化为可用于基准测试的资产，还发布了含多种任务和协议的开放基准测试。
中文摘要 AI 辅助
公共电池老化数据集对先进健康管理至关重要，但格式不一致、模式不清晰且元数据分散，限制了其实际应用。当前管理多为手动且难重现，通用数据集成工具又缺乏电化学时间序列数据的领域特定语义。我们提出BatteryLake，一个受治理的数据湖仓，通过基于物理原理的智能管理框架将原始公共电池数据转化为可用于基准测试的资产。贡献包括：大语言模型智能体提取元数据并合成特定数据集的转换器；人在回路机制将验证作为选择性预测，并通过26条模式、统计和物理合理性规则筛选数据；发布来自25个以上机构的41个数据集的开放基准测试，具有标准化的健康状态和剩余使用寿命任务、三种分割协议和八个基线模型家族。该平台、基准测试和管理协议可在指定网址公开获取。
英文摘要
Public battery aging datasets are a critical asset for advanced health management, but their practical use is often limited by inconsistent formats, unclear schemas, and metadata scattered across repositories and publications. Current curation remains largely manual and hard to reproduce, while general-purpose data integration tools miss the domain-specific semantics of electrochemical time-series data. We present BatteryLake, a governed data lakehouse that turns raw public battery data into benchmark-ready assets through an agentic, physics-grounded curation framework, with three contributions. First, LLM agents extract metadata and synthesize dataset-specific converters, grounding every output in verbatim evidence and abstaining when none supports a value. Second, a human-in-the-loop mechanism frames verification as selective prediction and gates admitted data through 26 schema, statistical, and physical-plausibility rules. Third, we release an open benchmark of 41 datasets from over 25 institutions, with standardized SOH and RUL tasks, three split protocols, and eight baseline model families. The platform, benchmark, and curation protocol are publicly available at https://tianwen1209.github.io/batterylake/.
发表机构
- College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
机构由 AI 辅助整理，请以论文原文为准。
