Battery Feature Lab

Battery Feature Lab (BFL) is a Python framework for the analysis of battery time-series measurements and electrochemical impedance spectroscopy (EIS) data. It represents measurement conditions (Operation), observed and model-derived responses (Response), and changes across observations that meet explicit comparability criteria (Evolution).

The framework produces machine-readable analysis records with traceable source references, processing provenance, method configurations, quality diagnostics, and interpretation limits. Time-series and EIS measurements are processed through distinct analysis interfaces and output schemas. For EIS, the framework retains measured spectra, evaluates linear Kramers–Kronig consistency, and estimates equivalent-circuit parameters with explicit convergence diagnostics. Parameter uncertainty, stability, and identifiability remain unassessed in the current release; numerical fit quality alone does not establish a physical interpretation.

Outputs are provided as JSON and Parquet for downstream analytical workflows and evidence-informed data reuse.

How BFL is organised

BFL analyses measurements along three complementary, capability-gated dimensions. This diagram shows conceptual and data dependencies, not the compiler's execution order or a requirement that every dimension produce an applicable result. Metadata provides context, while evidence and validation make each result traceable and define its interpretation limits.

The editable Mermaid source is retained alongside the static SVG.

Raw files are standardized by Battery Data Standard (BDS). Existing formal Battery Data Format (BDF) artifacts may instead use the optional batterydf adapter. Both routes produce the same internal quantity names and retain their provider-native report; neither route silently falls back to the other after a provider error.

Quick start

Battery Feature Lab 0.6.0 supports Python 3.11 and 3.12.

python -m pip install "battery-feature-lab==0.6.0"

For EIS analysis, install the optional providers:

python -m pip install "battery-feature-lab[eis]==0.6.0"

Analyze a raw cycler export:

import bfl

result = bfl.analyze(
    "cell.xlsx",
    output_dir="bfl_outputs/cell",
    input_adapter="bds",
    nominal_capacity_ah=None,
    temperature_column=None,
)

for path in result.files:
    print(path)

Equivalent CLI:

bfl analyze cell.xlsx --output-dir bfl_outputs/cell --input-adapter bds

For native formal-BDF input, install "battery-feature-lab[bdf]==0.6.0" and use input_adapter="bdf". The optional OpenAI notebook client is available through "battery-feature-lab[ai]==0.6.0".

Output contract

Every successful run writes exactly six artifacts. The provider report filename depends on the selected input adapter.

normalized_data.bdf.parquet
bds_conversion_report.json       # BDS input
# or bdf_validation_report.json  # formal-BDF input
analysis_metadata.json
analysis_results.json
analysis_evidence.json
analysis_validation.json

Start with analysis_results.json. It is a compact index over Operation, Response, and Evolution. Resolve a record_id in analysis_evidence.json only when source intervals, full series, method parameters, references, or detailed quality evidence are needed.

Serialized provenance uses portable artifact filenames plus SHA-256 digests. Host-local absolute paths are never written to the public JSON contract; configuration.output_dir is represented as .. The AnalysisResult Python object still returns the actual runtime paths.

import json
from pathlib import Path

root = Path("bfl_outputs/cell")
summary = json.loads((root / "analysis_results.json").read_text(encoding="utf-8"))
evidence = json.loads((root / "analysis_evidence.json").read_text(encoding="utf-8"))

record_id = summary["dimensions"]["operation"][0]["record_id"]
record = next(item for item in evidence["records"] if item["record_id"] == record_id)

Progressive capability

BFL does not require every dataset to contain every battery channel.

Missing optional channels produce explicit not_computable metrics and not_invoked provider calls. BFL never creates a voltage channel, assumes a sampling rate, or promotes a filename token into cell metadata.

Scientific boundaries

• Operation labels are limited to constant_current_like, constant_voltage_like, pulse_like, dynamic_current, and unmatched. BFL does not infer a named protocol.
• Current-step delta-V/delta-I is an apparent terminal response, not intrinsic resistance or SOH.
• Relaxation checkpoints are terminal-voltage recovery, not equilibrium OCV.
• A capacity-aligned coordinate is not SOC unless an explicit reference establishes it.
• Capacity evolution requires source or joined cycle identity, structural completeness, and a comparable operation signature. A short test does not support ageing or lifetime claims.
• Metadata fields remain unknown when neither the preprocessing provider nor the caller supplies them.

Documentation

EIS

Install battery-feature-lab[eis]==0.6.0 and use bfl.analyze_eis() or bfl analyze-eis. The independent EIS path combines BDS-backed intake, explicit spectrum grouping, measurement conditions, Lin-KK consistency checks, eisfit 0.1.1 circuit fitting and condition-gated comparisons. It retains source rows, conversion operations, provider failures and interpretation limits in a modality-specific six-artifact contract.

See EIS analysis and evidence for the input declarations, comparison policy, dependency compatibility and output schemas.

• Documentation home
• Core architecture
• API reference

Two worked examples are indexed in examples/README.md:

• examples/BFL_Catenaro_Onori_example.ipynb analyses a time-series workbook. Follow the download and checksum instructions; derived JSON is under examples/outputs/Catenaro_Onori_2021/.
• examples/BFL_Zhang_2020_EIS_example.ipynb screens 261 impedance spectra from one ageing coin cell, then fits and compares six of them. Follow the download and checksum instructions; derived JSON is under examples/outputs/Zhang_2020/.

Source data comes from the cited CC BY 4.0 datasets and is intentionally not tracked here, nor are normalized time-series or spectrum arrays.

Development

git clone https://github.com/shiyunliu-battery/Battery-Feature-Lab.git
cd Battery-Feature-Lab
uv sync --python 3.12 --extra dev --extra ai --extra eis
uv run ruff check .
uv run pytest -q

Real-data validation instructions are kept in tests/data/real/README.md.

Citation

If Battery Feature Lab supports your work, please cite the software:

@software{liu_2026_battery_feature_lab,
  author  = {Liu, Shiyun},
  title   = {Battery Feature Lab},
  year    = {2026},
  version = {0.6.0},
  url     = {https://github.com/shiyunliu-battery/Battery-Feature-Lab},
  license = {MPL-2.0}
}

GitHub can also generate citation formats from CITATION.cff. The example datasets remain separately licensed under CC BY 4.0 and must retain their original citation and attribution: Catenaro–Onori and Zhang et al..