--- license: cc-by-4.0 language: - en pretty_name: celljar tags: - battery - lithium-ion - energy-storage - timeseries - electrochemistry - bms - hppc - cycling size_categories: - 10K Scope: celljar harmonizes MEASUREMENTS. It converts units, normalizes the schema, and preserves provenance. It does NOT fit R_DC, dV/dQ, OCV, or ECM parameters from V/I/T - that is downstream work: fit it with your own code, or an open-source tool (PyBOP, equivalent-circuit-model, and others). Values a source publishes itself ARE carried, tagged via `*_method`. ## Quick start The full bundle lives on [HuggingFace](https://huggingface.co/datasets/mihnathul/celljar). Query it directly - no clone needed: ```python import duckdb df = duckdb.sql(""" SELECT * FROM 'https://huggingface.co/datasets/mihnathul/celljar/resolve/main/timeseries.parquet' WHERE test_id = 'ORNL_LEAF_2013_HPPC_25C' """).df() ``` pandas, Polars, and the `datasets` library read the same URLs - see [Query in place](#query-in-place---no-download-needed) below, including filtered reads that fetch only matching row groups instead of the whole file. ## Datasets | Dataset | Cell model | Chemistry | Test types | Cells | License | DOI | |---|---|---|---|---|---|---| | ORNL Leaf 2013 | AESC | `mixed` | `hppc` | 1 | [MIT](https://opensource.org/licenses/MIT) | [10.5281/zenodo.2580327](https://doi.org/10.5281/zenodo.2580327) | | HNEI 18650PF | Panasonic NCR18650PF | `NCA` | `hppc`, `qocv`, `drive_cycle`, `C1Discharge` | 1 | [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) | [10.17632/wykht8y7tg.1](https://doi.org/10.17632/wykht8y7tg.1) | | MATR (Severson 2019) | A123 APR18650M1A | `LFP` | `cycle_aging` | 135 | [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) | [10.1038/s41560-019-0356-8](https://doi.org/10.1038/s41560-019-0356-8) | | CLO (Attia 2020) | A123 APR18650M1A | `LFP` | `cycle_aging` | 45 | [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) | [10.1038/s41586-020-1994-5](https://doi.org/10.1038/s41586-020-1994-5) | | BILLS eVTOL (Bills 2023) | Sony US18650VTC6 | `NMC` | `drive_cycle` | 22 | [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) | [10.1184/R1/14226830](https://doi.org/10.1184/R1/14226830) | | NASA PCoE | undisclosed | `LCO` | `cycle_aging` | 34 | [CC0-1.0](https://creativecommons.org/publicdomain/zero/1.0/) | [dataset](https://www.nasa.gov/intelligent-systems-division/discovery-and-systems-health/pcoe/pcoe-data-set-repository/) | | Naumann 2018/2020 | Sony US26650FTC1 | `LFP` | `cycle_aging`, `calendar_aging` | 34 | [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) | [10.17632/kxh42bfgtj.1](https://doi.org/10.17632/kxh42bfgtj.1) | | Kollmeyer 30T aging (Duque 2025) | Samsung INR21700-30T | `NMC` | `hppc`, `cycle_aging`, `C0p5Discharge`, `C1Charge`, `C1Discharge`, `C20Discharge`, `C2Discharge` | 6 | [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) | [10.5683/SP3/UYPYDJ](https://doi.org/10.5683/SP3/UYPYDJ) | | Kollmeyer 30T BoL | Samsung INR21700-30T | `NMC` | `hppc`, `qocv`, `drive_cycle`, `C0p5Discharge`, `C1DischargeCharge`, `C2Discharge` | 1 | [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) | [10.17632/9xyvy2njj3.2](https://doi.org/10.17632/9xyvy2njj3.2) | | Kollmeyer HG2 BoL | LG INR18650HG2 | `NMC` | `hppc`, `drive_cycle`, `C0p5Discharge`, `C1DischargeCharge`, `C20DischargeCharge`, `C2Discharge` | 1 | [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) | [10.17632/cp3473x7xv.3](https://doi.org/10.17632/cp3473x7xv.3) | Only ORNL Leaf's raw data ships in the repo. For the other datasets you fetch the raw files yourself from the original source (each `data/raw//SOURCE_DATA_PROVENANCE.md` has the link and steps) and regenerate - or skip raw entirely and use the already-harmonized bundle on HuggingFace. ## Schema Four entities, joined by `cell_id` / `test_id` (and `checkup_id` where present): ``` cell_metadata cells/*.json one JSON per cell: chemistry, capacity, form factor test_metadata tests/*.json one JSON per test: protocol, SOH, provenance, license, checkup_id timeseries timeseries.parquet V / I / T per sample + signed coulomb count (∫I dt); join on test_id cycle_summary cycle_summary.parquet source-published per-cycle aggregates (capacity, R_DC, ...) ``` Conventions: SI units, relative timestamps, missing data is explicit `null`. Current is normalized to one canonical sign across every source: positive = charge (into the cell), negative = discharge. Provenance is first-class. Every test row carries `source_doi` / `source_citation` / `source_license`, and every value celljar could have computed instead of measured carries a `*_method` tag so you know where it came from: | Field | Tag | Values | |---|---|---| | `soh_pct` | `soh_method` | `capacity_vs_first_checkpoint`, `bol_assumption`, null | | `soc_range_min/max` | `soc_method` | `protocol_asserted`, `source_published`, null | | `resistance_dc_ohm` | `resistance_method` | `source_published`, null | celljar never derives SOC or R_DC from the timeseries - it persists the measured `coulomb_count_observed_min/max_Ah` instead. Full field list, types, and enums in the [JSON Schemas](https://github.com/mihnathul/celljar/tree/main/schemas); the runtime Pandera mirror is [`harmonize_schema.py`](https://github.com/mihnathul/celljar/blob/main/celljar/harmonize/harmonize_schema.py). ## Download the bundle ```bash pip install huggingface_hub huggingface-cli download mihnathul/celljar --repo-type dataset --local-dir ./celljar-bundle ``` Add `--revision ` to pin a release for reproducibility (tags are listed on the dataset's Versions tab). In Python: ```python from huggingface_hub import snapshot_download local = snapshot_download(repo_id="mihnathul/celljar", repo_type="dataset") # add revision="" to pin ``` ## Query in place - no download needed ### DuckDB - full SQL across all entities over HTTPS ```sql INSTALL httpfs; LOAD httpfs; SELECT c.chemistry, c.nominal_capacity_Ah, t.test_id, t.test_type, t.soh_pct, COUNT(*) AS n_samples FROM read_json('https://huggingface.co/datasets/mihnathul/celljar/resolve/main/cells/*.json') c JOIN read_json('https://huggingface.co/datasets/mihnathul/celljar/resolve/main/tests/*.json') t ON c.cell_id = t.cell_id JOIN 'https://huggingface.co/datasets/mihnathul/celljar/resolve/main/timeseries.parquet' ts ON t.test_id = ts.test_id GROUP BY 1,2,3,4,5 ORDER BY t.test_id; ``` ### pandas / Polars - predicate-pushdown read of one test ```python import pandas as pd df = pd.read_parquet( "https://huggingface.co/datasets/mihnathul/celljar/resolve/main/timeseries.parquet", filters=[("test_id", "==", "ORNL_LEAF_2013_HPPC_25C")], ) ``` ### `datasets` library - streaming ```python from datasets import load_dataset ds = load_dataset( "parquet", data_files="https://huggingface.co/datasets/mihnathul/celljar/resolve/main/timeseries.parquet", split="train", streaming=True, ) for row in ds.take(5): print(row) ``` ## Related tools celljar sits alongside, not in place of, the other tools in this space: - [Battery Data Commons](https://batterycommons.github.io/) - registry indexing 300+ public battery datasets. Great for discovery; celljar adds a harmonized data layer over a subset of them. - [Iontech](https://github.com/shiyunliu-battery/Iontech) (Shiyun Liu) - curated index of open-source battery monitoring and modeling datasets (RWTH home-storage, NREL failure databank, Stanford second-life, etc.) with paper links. - [BatteryLife](https://github.com/Ruifeng-Tan/BatteryLife) / [BatteryML](https://github.com/microsoft/BatteryML) - cycling-to-failure ML benchmark (KDD 2025). Optimized for lifetime-prediction ML; celljar keeps the full V/I/T timeseries that physics-based parameterization (ECM/SPM/DFN) needs. ## License & citation The science belongs to the original authors; celljar just puts their data in one schema. Cite their papers when you use the data - every `test_metadata` row carries its own `source_doi`, `source_citation`, and `source_license` so attribution is one query away. - celljar code: MIT ([LICENSE](https://github.com/mihnathul/celljar/blob/main/LICENSE)) - Harmonized bundle (schema, packaging): CC-BY-4.0 - Upstream raw data: each publisher's original license (see the Datasets table above) ```bibtex @misc{celljar, author = {Mihna Neerulpan}, title = {celljar: Public Battery Test Dataset Harmonization with a Canonical Schema}, year = {2026}, howpublished = {\url{https://github.com/mihnathul/celljar}}, } ``` ## Links - Code: - Issues / new-source requests: - Canonical JSON Schemas: ## Acknowledgments celljar exists because of the labs and authors who designed, ran, and openly published these experiments. Thank you to: Phillip Kollmeyer (HNEI, Samsung 30T, LG HG2) - G. Wiggins, S. Allu, H. Wang (ORNL) - K. Severson, P. Attia et al. (MATR, CLO; Stanford / MIT / TRI) - A. Bills et al. (BILLS; CMU) - B. Saha, K. Goebel (NASA PCoE) - M. Naumann et al. (TUM) - J. Duque, M. Naguib (Samsung 30T aging; McMaster)