| --- |
| license: mit |
| library_name: joblib |
| tags: |
| - batteryswapai-2026 |
| - predictive-maintenance |
| - survival-analysis |
| - scheduling |
| --- |
| |
| # BatterySwapAI 2026 — MnesisLab |
|
|
| Causal battery-swap planning: hierarchical Wiener first-passage reranking over a |
| degradation model, with a cost-aware capacity and routing policy. |
|
|
| ## Artifact |
|
|
| `submission_artifacts/weekly99_planner.joblib`, loaded by `script.py`. |
|
|
| | setting | value | |
| |---|---| |
| | FPT rank residual weight | 0.15 | |
| | capacity lookback | 42 days | |
| | emergency operational scale | 0.5 | |
| | capacity weekly limit fraction | 0.99 | |
|
|
| ## Contract |
|
|
| - Each scenario uses only readings with `end_time <= scenario.start_time`. |
| - EOL is reconstructed as the evaluator defines it: strict `10 < T < 30`, daily median, |
| days with fewer than five readings masked, seven-calendar-day rolling median with |
| `min_periods=3`, first smoothed voltage `<= 2.40 V`. This matches all 82 observed train |
| EOL devices; censored devices remain censored. |
| - Runtime: `batteryswap_public==0.3.4`, CPU only, no network, every live battery emitted |
| once with a valid plan date, 19,890 rows on the train split. |
|
|
| MIT licensed (`LICENSE`). Third-party notices in `THIRD_PARTY_LICENSES.md`. |
|
|
