Submission for the BatterySwapAI 2026 challenge.

Roughly 414 vibration sensors are in service in any planning window. About nine of them will reach end-of-life during the next six weeks. The task is to find those nine and send a technician — no more, no fewer.

The planner ranks every battery by the average of two percentile ranks derived from its smoothed voltage curve, calibrates that rank into a probability of dying inside the window, and takes as many as the calibrated probabilities say will die. The visits are then chained into as few trips as the day allows.

There is no trained model. The stored artefact is 1193 bytes: two ranking column names, a step calibration held as two arrays, and a schedule. That is a deliberate, measured choice — gradient boosting scored +359 ± 70 and logistic regression +292 ± 76 against this baseline, paired over 48 scenarios and validated out-of-fold on buildings. With 24 buildings of genuinely independent data, every fitted degree of freedom costs more variance than it captures.

Three decisions carry the result:

Which batteries. Not a voltage threshold, and not a fixed count. Thresholds let the count swing from 19 to 107 per scenario; a fixed count cannot fit a scenario with two dying batteries and one with nineteen. Instead the count follows the expected number of deaths:

The sum of calibrated probabilities estimates how many will actually die (correlation 0.73 with the truth). The multiplier is above one because a miss costs several times a wasted swap. Against a fixed twelve this is −100.1 ± 36.5, t = −2.74, winning 30 of 48 scenarios. The bounds matter more than the multiplier — the sweep is flat around the optimum (1.25 → 1925, 1.5 → 1889, 1.75 → 1929) and the bounds cap the damage if a split's base rate differs from this one's.

All 210 pairs of the 21 features were searched — v_min_30 + v_med_7 is the best. Percentile ranks rather than raw volts, because a ranking carries no scale, which is what lets a calibration fitted here transfer to a split with different buildings.

When. As early as the schedule allows. Replacing a battery one day late costs twenty times as much as one day early (late_replacement_penalty_daily = 10.0 against early = 0.5), so within the window it always pays to be early. Placing swaps near a predicted end-of-life instead was measured across the full range and loses monotonically — see What did not work.

How the visits are laid out. Nearest-neighbour route, chained into trips of up to 44 hours rather than one building per day. Travel is 2 x the sum of distances to the buildings visited, so ordering alone cannot change it; only chaining can, by removing the return to base between buildings. Measured on the public split:

This is the one place where the local measurement and the public split disagree in direction, twice. Chaining costs 118 on the training split (t = 3.17) and won 67 and then 73 on the public one. The daily-limit penalty rose locally and fell on public both times. The public numbers decide.

Every parameter was chosen by grid search against the competition's own evaluator over all 48 training scenarios, not by judgement:

smooth_series() defaults to a 7-day rolling window and a 10–30 °C band. Those are tuned to reject temperature noise in general, not to catch a steep drop early — and a lithium cell holds an almost flat voltage for two years before falling in weeks.

Sharpening it measurably helps on the training split:

It was still reverted. On the public split the same change scored worse: 1851 against 1735. The temperature band had been tuned against the training buildings, and the splits are split by building — the documentation notes that placement influences ambient temperature. The defaults generalised better, so the defaults are what ships.

The diagnosis behind it is worth recording. The filter discards 15 % of readings overall, but between 33.9 % and 96.8 % retained depending on the building. And it discards more from the devices that matter: units that reached end-of-life retain 77.9 % of their readings against 86.0 % for those that did not.

Requires Python 3.10+, Docker, and the competition dataset in dataset/.

Rebuild the stored planner from scratch — this regenerates best.pickle:

Expected output: total_cost 1911.02 on the training split. The pickle it writes is byte-identical to the one in e205611, the best submission.

Other modes:

The planner contains no randomness — selection is a deterministic ranking and scheduling is deterministic given the buildings. Rebuilding best.pickle on the same dataset reproduces the same file and the same score.

The unused classifier code in train_due_classifier() uses random_state=42 throughout. It is kept as documentation of an approach that was measured and rejected; it is not part of the submission path.

sjekk.py runs the whole chain and reports five checks:

Check 4 matters more than it looks. .gitattributes routes every *.pickle to LFS, and an LFS pointer is 128 bytes of text that pickle.load() cannot read. The last line of .gitattributes exempts the planner, exactly as the upstream template does for its own pickle.

Mean total_cost over all 48 training scenarios, and the public leaderboard score of the corresponding submission:

The best public score is 1525.77, eighth of twenty-seven — the configuration this repository reproduces. The two splits are divided by building, and they have disagreed about the direction of a change four times — see the note under How the visits are laid out and the SMOOTH_WINDOW comment in batteryswap_example/features.py. The public column is the one that decides.

Where the remaining gap sits, against the leaders on the public split:

Logistics is now ahead of the eighth-placed entry on three of four components. What is left is selection: late_swap against a leader at 591.67.

1.5 s per scenario against a budget of roughly 15 s, CPU only. Well inside the 30-minute wall-clock limit for 96 plans.

Seventeen approaches were measured and rejected over four days. Each is documented with its numbers in the commit that tested it; the summary is here so the next person does not repeat them.

Selection. Gradient boosting reached out-of-fold average precision 0.272–0.327 and logistic regression 0.252–0.287, against 0.339 for a single raw feature. With 24 buildings of genuinely independent data, every fitted degree of freedom costs more variance than it captures. Curve extrapolation — fitting (V − 2.4) / |slope| per device with temperature as a covariate — reached 44.2 % recall against 55.0 %; on a curve this flat, a battery in the knee already has a low level, so rate and level point at the same devices. Device temperature as a ranking feature scored 3.1 %: it is a cross-sectional property, not a timing signal.

Survival analysis, the one the organisers signposted. lifelines>=0.30.0 sits in the competition's own requirements.txt, and the data is exactly the shape it is built for: 379 of 461 devices never reach end-of-life inside the training window, so the labels are censored. A classifier sees only the binary outcome; a survival model also sees how long each survivor lasted, and that a device dying on day 60 is closer than one dying on day 300.

Cox proportional hazards, scored as S(42 days | features) and fed through the same calibration and count machinery, out-of-fold on buildings:

It loses on 46 of 48 scenarios. That completes a pattern worth stating plainly, because it is the central result of this entry:

logistic regression   +292 ± 76
gradient boosting     +359 ± 70
Cox proportional      +707 ± 71

Every fitted weighting loses to two raw percentile ranks, and the loss grows with the number of degrees of freedom. Three model families, one conclusion, monotone in parameters. With 24 buildings of genuinely independent data — devices in the same room share temperature, installation date and battery batch — there is not enough signal to fit anything. The ranking works precisely because it fits nothing.

Routing. Seven strategies, none better than alphabetical order: distance ordering, far/near alternation, urgency ordering, nearest-neighbour tours, packing several buildings per day, travel-aware selection, and clustering selection.

The common cause is worth stating once. Decomposing the 50.5 hours of travel per scenario on the training split:

The whole travel budget is 50 hours and a single miss costs 330. Every attempt to reach those 12 hours traded away selection accuracy, and that trade can never pay. Late swaps and travel are one problem, not two: each miss costs 330 in penalty plus 4.7 hours of emergency driving. Only better selection cuts both.

Travel is the visible symptom; selection is the disease. The public leaderboard publishes cost components per team, and ours stand out in one column. Travel: 75.3 against 36.6 for the leader and 45.5 for fourth place — the worst in the top fifteen, with the next-worst at 59.0.

Across the fourteen teams above 2200, travel almost perfectly determines the two largest penalties, and is uncorrelated with misses:

travel <-> overtime      +0.981
travel <-> daily_limit   +0.937
travel <-> late_swap     -0.009

Matching fourth place's travel would carry overtime and the daily limit down with it — about 196 points, or 1735 to roughly 1539.

Travel is 2 x the sum of distances to the buildings visited, so order cannot change it. Only three things can: fewer buildings, nearer buildings, or chaining several into one trip. All three were measured on the calibrated planner:

The last row is the important one. Clustering reaches 38.2 — essentially the leader's 36.6 — and it costs 740 in late swaps, because recall collapses from 57 % to 31 %. Every mechanism that cuts travel pays for it in misses, and the exchange rate is never favourable.

Which settles what the leaderboard first appeared to say. The leader has both low travel (36.6) and low late swaps (591.7 against our 807.1). That combination is not reachable by any scheduling trick, because we measured all three and each one trades one for the other. It is reachable only with a sharper selection signal: when the ranking is right, twelve swaps do not have to be scattered across eleven buildings on the chance that one of them is dying.

Our travel is not a logistics failure. It is what a 57 % recall looks like from the outside.

Two changes measured better on train and worse on public: the sharper smoothing above, and a weekly working-hour budget that cut weekly_limit by 71 as intended but cost 171 in delay. Both were reverted. The training split is a necessary but not sufficient signal — the splits differ by building, and so does what generalises.

The dataset belongs to Soundsensing (all rights reserved) and is not included here.

Dockerfile sets BATTERYSWAP_SPLITS=train, so a local Docker run scores the training split. The competition environment overrides this to public,private; the two numbers are not comparable.

MIT — see LICENSE. All dependencies come from the competition's own requirements.txt and are unmodified.