Battery Cycle-Life Analyzer

Fit transparent empirical capacity-fade models, compare diagnostics, and project battery end of life inside an explicit extrapolation limit.

View the source and documentation | Open the Colab notebook

What it provides

• Linear, power-law, and logarithmic capacity-fade fits using SciPy.
• RMSE and R^2 diagnostics for every fitted model.
• A configurable EOL threshold relative to fitted initial capacity.
• A bounded projection horizon that returns no estimate when EOL is outside three times the observed cycle range.
• Reproducible synthetic LFP and NMC demonstrations.
• CSV/TSV cycle-capacity import with normalization and validation.
• Publication-ready Matplotlib figures and a tested Python API.

Quick start

git clone https://github.com/mohammadrezwankhan/battery-cycle-life-analyzer.git
cd battery-cycle-life-analyzer
python -m pip install .
python -m bcla --model all

Model families

For normalized capacity Q(n) at cycle n:

• Linear: Q(n) = Q0 - k n
• Power law: Q(n) = Q0 - alpha n^beta
• Logarithmic: Q(n) = Q0 - a ln(1 + b n)

The package reports fit diagnostics before selecting the lowest-RMSE model. An in-window fit is not treated as proof of a trustworthy long-range forecast, so unsupported threshold crossings return no estimate.

Scope and limitations

This is an empirical research and educational baseline, not an electrochemical, pack-safety, or production BMS model. The bundled data are synthetic. Engineering conclusions require representative laboratory data and explicit consideration of chemistry, temperature, protocol, and uncertainty.

Project links

• README and equations
• Contributing guide
• MIT license