--- license: cc-by-4.0 language: - en size_categories: - 1B **Severson, K. A., Attia, P. M., Jin, N., Perkins, N., Jiang, B., > Yang, Z., Chen, M. H., Aykol, M., Herring, P. K., Fraggedakis, D., > Bazant, M. Z., Harris, S. J., Chueh, W. C., Braatz, R. D. (2019).** > "Data-driven prediction of battery cycle life before capacity degradation." > *Nature Energy*, **4**(5), 383–391. > doi:[10.1038/s41560-019-0356-8](https://doi.org/10.1038/s41560-019-0356-8) ```bibtex @article{severson2019datadriven, author = {Severson, Kristen A. and Attia, Peter M. and Jin, Norman and Perkins, Nicholas and Jiang, Benben and Yang, Zi and Chen, Michael H. and Aykol, Muratahan and Herring, Patrick K. and Fraggedakis, Dimitrios and Bazant, Martin Z. and Harris, Stephen J. and Chueh, William C. and Braatz, Richard D.}, title = {Data-driven prediction of battery cycle life before capacity degradation}, journal = {Nature Energy}, volume = {4}, number = {5}, pages = {383--391}, year = {2019}, doi = {10.1038/s41560-019-0356-8}, url = {https://www.nature.com/articles/s41560-019-0356-8}, } ``` ## Cell specifications | Property | Value | |---|---| | Manufacturer | A123 Systems | | Model | APR18650M1A | | Form factor | 18650 cylindrical | | Cathode chemistry | LFP (lithium iron phosphate, LiFePO4) | | Anode chemistry | Graphite | | Nominal capacity | 1.1 Ah | | Nominal voltage | 3.3 V | | Charge cutoff (used) | 3.6 V | | Discharge cutoff (used) | 2.0 V | | Number of cells (this dataset) | 124 | | End-of-life threshold | 80 % capacity retention | ## Cycling protocol All cells were cycled inside a 30 °C controlled environmental chamber. Charging was performed under one-step or two-step fast-charging policies spanning charge rates from 1C to 6C (corresponding to 8 to 13.3 minutes to reach 80 % SOC), giving a total of 72 distinct fast-charging strategies across the cohort. Discharging was uniform : 4C constant-current to the discharge cutoff. A 1-minute rest was enforced after reaching 80 % SOC during charging, and a 1-second rest after each discharge. Internal resistance was probed once per cycle by 10 pulses of ±3.6C with a pulse width of 30 or 33 ms. This protocol is what makes Severson 2019 a strong stress-test for filter benchmarks : the cell-to-cell variation is dominated by *charging policy* rather than ambient conditions, isolating the protocol-driven aging mechanisms that filters are typically asked to compensate for. ## File inventory (best-effort) Severson 2019 is distributed as **three** `.mat` files (HDF5 v7.3 format) on the TRI data portal : | File | Date | Cells | Size (approx.) | |---|---|---|---| | `2017-05-12_batchdata_updated_struct_errorcorrect.mat` | 2017-05-12 | 46 | 2.82 GB | | `2017-06-30_batchdata_updated_struct_errorcorrect.mat` | 2017-06-30 | 48 | 1.80 GB | | `2018-04-12_batchdata_updated_struct_errorcorrect.mat` | 2018-04-12 | 46 | 3.01 GB | | **Total** | | **140 channels → 124 cells after exclusions** | **~7.6 GB** | A fourth file dated 2019-01-24 is sometimes seen in the same data.matr.io project ; that file belongs to **Attia et al. 2020** ("Closed-loop optimization of fast-charging protocols for batteries with machine learning") and is **not** part of Severson 2019. This Tier 1 mirror covers only the three Severson 2019 batches. The 16 channels that account for the gap between the 140 raw channels and the published cohort of 124 cells are documented in the upstream Braatz Group `Load Data.ipynb` notebook : five cells in batch 1 did not reach the 80 % capacity threshold (`b1c8`, `b1c10`, `b1c12`, `b1c13`, `b1c22`), five cells in batch 2 were re-assigned to batch 1 because they were continued from the first experimental run (`b2c7`, `b2c8`, `b2c9`, `b2c15`, `b2c16`), and six cells in batch 3 were excluded as noisy channels (`b3c37`, `b3c2`, `b3c23`, `b3c32`, `b3c42`, `b3c43`). The Tier 1 mirror still preserves these channels in the raw `.mat` files ; the Tier 2 canonical Parquet repository will apply the published exclusion mask. Sizes are rounded community estimates (see BatteryML and the BatteryBits "Comparison of Open Datasets for Lithium-ion Battery Testing" article). Exact bytes will be locked once the actual upload to HuggingFace completes and `manifests/severson_2019_lfp.yaml` is populated with SHA-256 digests. ## Why "raw mirror" tier BSEBench follows a **dual-tier** dataset strategy : - **Tier 1 (this repository)** — the original `.mat` files, preserved byte-for-byte, with SHA-256 digests recorded in our manifest and cross-checked against the original publication's distribution. Use this tier if you need to verify provenance, run independent harmonizations, or audit our adapter's correctness. - **Tier 2** — the BSEBench-canonical Parquet harmonization at [`bsebench-org/severson-2019`](https://huggingface.co/datasets/bsebench-org/severson-2019). Consistent column names, BPX-1.1 sign convention, unified schema across all benchmark datasets. Use this tier for filter benchmarking and most downstream work. ## Original source The original Severson 2019 dataset was distributed via [data.matr.io](https://data.matr.io/1/) (the Toyota Research Institute Experimental Data Platform), specifically project [`5c48dd2bc625d700019f3204`](https://data.matr.io/1/projects/5c48dd2bc625d700019f3204). This URL is recorded as **citation and provenance metadata** only. **The HuggingFace Hub mirror at this repository is the BSEBench single source of truth for fetching.** Adapters in `bsebench-datasets` never hit `data.matr.io` at runtime. This insulates the benchmark from upstream availability changes (URL shifts, registration requirements, bandwidth limits, eventual portal retirement) while preserving the citation chain back to the original publishers. ## License The Severson 2019 dataset is distributed under the [Creative Commons Attribution 4.0 International (CC-BY-4.0)](https://creativecommons.org/licenses/by/4.0/) license, consistent with the licensing policy of the `data.matr.io` platform's earlier (pre-2025) datasets per the [TRI Energy & Materials Datasets](https://data.matr.io/) catalog. Verbatim core grant from the [CC-BY-4.0 legal code](https://creativecommons.org/licenses/by/4.0/legalcode) : > "Subject to the terms and conditions of this Public License, the > Licensor hereby grants You a worldwide, royalty-free, non-sublicensable, > non-exclusive, irrevocable license to exercise the Licensed Rights in > the Licensed Material to: (1) reproduce and Share the Licensed Material, > in whole or in part; and (2) produce, reproduce, and Share Adapted > Material." The redistribution rights granted by this license are the legal basis on which BSEBench mirrors the dataset on the HuggingFace Hub. Attribution is given to the original authors via the BibTeX block above and via the manifest's `citation_bibtex` field. Derivative material (the Tier 2 Parquet harmonization at `bsebench-org/severson-2019`) is offered under the same CC-BY-4.0 license, with BSEBench attribution added on top of the original Severson 2019 attribution chain. Note : the *publication text* of the Nature Energy paper is governed by Springer-Nature's text-and-data-mining terms (CrossRef license type `tdm`, effective 2019-03-25), which is a separate licensing regime from the dataset hosted on data.matr.io. CC-BY-4.0 covers the experimental data only ; do not assume it covers the paper PDF. ## How to use ```python from huggingface_hub import snapshot_download local_dir = snapshot_download( "bsebench-org/severson-2019-raw", repo_type="dataset", ) # local_dir contains the three .mat files, SHA-256 verified # against bsebench-datasets/manifests/severson_2019_lfp.yaml ``` To then read a `.mat` file in Python (the files are HDF5 v7.3, not classic v5, so `scipy.io.loadmat` will not work — use `h5py`) : ```python import h5py from pathlib import Path p = Path(local_dir) / "2017-05-12_batchdata_updated_struct_errorcorrect.mat" with h5py.File(p, "r") as f: print(list(f.keys())) # ['#refs#', '#subsystem#', 'batch', 'batch_date'] batch = f["batch"] print(list(batch.keys())) # ['Vdlin', 'barcode', 'channel_id', # 'cycle_life', 'cycles', 'policy', # 'policy_readable', 'summary'] ``` For the BSEBench-harmonized Parquet version that exposes a clean benchmark-ready API, prefer [`bsebench-org/severson-2019`](https://huggingface.co/datasets/bsebench-org/severson-2019). ## Citation Cite the **original** Severson 2019 paper (BibTeX above). BSEBench's contribution is hosting and harmonization, not the data itself. If you also use BSEBench tooling for filter benchmarking, additionally cite : ```bibtex @misc{bsebench2026, author = {Akir, Oussama and {BSEBench Contributors}}, title = {{BSEBench}: an open-source benchmark for battery state-estimation filters}, year = {2026}, url = {https://bsebench.org}, } ``` ## Provenance manifest A machine-readable manifest validating this dataset's metadata against the [`bsebench-dataset-manifest/v1`](https://github.com/bsebench-org/bsebench-specs) Pydantic v2 schema lives at [`bsebench-org/bsebench-datasets/manifests/severson_2019_lfp.yaml`](https://github.com/bsebench-org/bsebench-datasets/tree/main/manifests). The manifest records, for every `.mat` file in this repository : - `source.canonical_url` — the data.matr.io project URL - `source.canonical_doi` — the Nature Energy DOI for citation - `source.publication_authors` and `source.publication_year` - per-file `path`, `sha256`, and `size_bytes` - the dataset-wide `license` (SPDX `CC-BY-4.0`) and `redistribution_allowed` flag (true) - the `citation_bibtex` block (verbatim copy of the BibTeX above) - `huggingface_tier1_repo` (= `bsebench-org/severson-2019-raw`) and `huggingface_tier2_repo` (= `bsebench-org/severson-2019`) The manifest is committed only after the SHA-256 digests are populated from the actual HuggingFace mirror — never with placeholder values. ## See also - [Tier 2 canonical Parquet sibling repository](https://huggingface.co/datasets/bsebench-org/severson-2019) - [Original publication (Nature Energy)](https://doi.org/10.1038/s41560-019-0356-8) - [Original data portal (TRI / data.matr.io)](https://data.matr.io/1/projects/5c48dd2bc625d700019f3204) - [Upstream Braatz Group GitHub starter code](https://github.com/rdbraatz/data-driven-prediction-of-battery-cycle-life-before-capacity-degradation) - [BSEBench organization on HuggingFace](https://huggingface.co/bsebench-org) - [BSEBench documentation site](https://bsebench.org)