Large Language Model–Based Fault Diagnosis for Lithium-ion Batteries in Cloud-Edge Systems 核心 · 已核验

doi:10.5281/zenodo.18335411

This dataset was used in the paper Large Language Model–Based Fault Diagnosis for Lithium-ion Batteries in Cloud-Edge Systems. It comprises laboratory experimental data and real-world energy storage system (ESS)data.he laboratory data record battery current, voltage, and (in some cases) temperature during fault experiments, as well as the fault severity and fault type. The dataset includes both raw measurements and data obtained through augmentation. For fault cases, the samples retained are those closest to the time of fault occurrence, whereas samples farther from the fault time are labeled as normal.he real-world ESS data consist of current, voltage, and temperature measurements collected during the operation of an actual ESS power station. For commercial reasons, we do not disclose the exact fault locations or labels; however, the data are confirmed to include faults such as self-discharge and inconsistency. Note that, in the real-world ESS data, a positive current indicates battery discharge.

落地页

https://zenodo.org/doi/10.5281/zenodo.18335411

许可证

CC-BY-4.0 (判读置信:inferred)

国内可访问性

国内直连:可达 (2026-07-11 检测) 代理通道:可达 (2026-07-11 检测)
检测口径:lychee 双通道单轮探测;「直连超时」表示检测窗口内未完成,系慢或不稳定证据,不构成封锁证据。

发布年份

2026

发布方

Zenodo

设备类型

battery

PHM 任务

fault_diagnosis