VivaKitEV Li-Ion Battery State-of-Health & Remaining Useful Life Prognostics
Machine learning Battery Management System (BMS) prognostics engine powered by the official NASA Ames Prognostics Center of Excellence (PCoE) 18650 Li-ion battery run-to-failure dataset. Engineers multi-cycle autoregressive capacity lags, rolling statistics, and degradation rates across real experimental cells (B0005, B0006, B0007, B0018). Employs combined Random Forest and Gradient Boosting ensembles to predict continuous State of Health (SOH %), forecast Remaining Useful Life (RUL in cycles) to the 1.4 Ah End-of-Life threshold, and classify cell health states with zero cross-cell data leakage.
Secure checkout by Dodo Payments (UPI, cards). Your personalised download link is e-mailed within 24 hours. One purchase covers one project team of up to 4 students. See terms and refunds.
Our honest results
| State-of-Health (SOH) R² Score | 0.9767 (Held-out unseen test battery B0018, 132 cycles) |
| SOH Mean Absolute Error (MAE) | 0.74% (Across full lifecycle to failure) |
| Remaining Useful Life (RUL) R² Score | 0.8835 (Held-out unseen test battery B0018) |
| RUL Mean Absolute Error | 7.58 cycles (Cross-battery prognostic horizon) |
Evaluated strictly on held-out unseen cell B0018 after training exclusively on cells B0005, B0006, and B0007. Includes 4-cell empirical degradation curve visualizer, Gradio 6 console with BMS telemetry presets, automated pytest suite, and 15 viva voce questions on SEI growth, lithium plating, and battery aging electrochemistry.
What's included
- Readable source codeSmall, commented modules you can explain line by line in your viva.
- Demo appA local web app to show your examiners - runs offline on your laptop.
- Trained model + reference resultsSo you can compare your own runs fairly.
- Learning guide with viva Q&ABackground, design choices explained, limitations and extension ideas.
- One-click setup & testsWindows setup script and tests that prove it works on your PC.
- Dataset download scriptFetches the public data from its original source, with the citation.
Dataset credit
NASA Ames Prognostics Center of Excellence (PCoE) Li-ion Battery Aging Dataset (B. Saha & K. Goebel, 2007, NASA Open Data).
Academic integrity
We sell code and teaching, not finished projects. You must understand, extend and write up the work yourself, acknowledge the kit and cite the dataset. No ready-made reports.