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bsebench-org/xjtu-pinn4soh-2024-raw

XJTU battery dataset for PINN4SOH BSEBench status: raw_mirror_pending_validation This repository is a raw mirror of the Zenodo dataset record: Source: Zenodo Source URL: https://doi.org/10.5281/zenodo.10963339 DOI: 10.5281/zenodo.10963339 Zenodo record: https://zenodo.org/records/10963339 License: Creative Commons Attribution 4.0 International (CC-BY-4.0) Institution: Xi'an Jiaotong University Download date: 2026-05-08 Source Description The Zenodo record… See the full description on the dataset page: https://huggingface.co/datasets/bsebench-org/xjtu-pinn4soh-2024-raw.

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XJTU battery dataset for PINN4SOH

BSEBench status: raw_mirror_pending_validation

This repository is a raw mirror of the Zenodo dataset record:

  • —Source: Zenodo
  • —Source URL: https://doi.org/10.5281/zenodo.10963339
  • —DOI: 10.5281/zenodo.10963339
  • —Zenodo record: https://zenodo.org/records/10963339
  • —License: Creative Commons Attribution 4.0 International (CC-BY-4.0)
  • —Institution: Xi'an Jiaotong University
  • —Download date: 2026-05-08

Source Description

The Zenodo record describes a battery dataset associated with the publication "Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis." The source states that it contains data from 55 LISHEN NCM 18650 lithium-ion cells under six charge/discharge strategies, sampled at 1 Hz.

Files

  • —Battery Dataset.zip: Original source archive downloaded from the official Zenodo API file URL. The archive is preserved without unpacking or transformation.
  • —BSEBENCH_SOURCE.json: Machine-readable provenance and verification metadata.
  • —SHA256SUMS.txt: SHA-256 checksums for files uploaded to this repository, excluding SHA256SUMS.txt itself.

Provenance And Verification

Before upload, the DOI, source record, and license were verified against the live Zenodo API record and DataCite metadata. The downloaded archive matched the Zenodo-declared size of 2438769934 bytes and Zenodo MD5 checksum a635cef9678e0de21f9d5c1f78a4342c.

No benchmark conclusions, performance claims, or leaderboard claims are made in this raw mirror.

Citation

Wang, Fujin. (2024). Project - Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis. Zenodo. https://doi.org/10.5281/zenodo.10963339