CarlAlbertCode/BatterySwapAI2026-MnesisLab

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.