We sealed our predictions before running the experiments — across six public battery datasets. Full scorecard, including two unedited falsifications.
TL;DR
- One data-level diagnostic workflow, applied to six battery fleets from five public sources (NASA PCoE; CALCE/UMD — the CS2 and CX2 series; MIT/Severson; Oxford; HUST). Every prediction was written down and hashed before the corresponding experiment ran — one of them with a publicly timestamped, Bitcoin-seeded protocol anyone can verify.
- Diagnosis mode: on the HUST fleet (77 cells, never touched before), we predicted an error-reduction band of [−45%, −20%] before modeling. Measured: −34.4%. Hit.
- Design mode: using only the manufacturer's spec sheet (max continuous charge current, 4A) plus Joule's law, we located the empirically best fast-charging policy in the MIT/Severson fleet within 2.2% of the measured optimum — before looking at any lifetime data.
- We also publish two clean falsifications: sealed predictions that missed. They stay in the record.
- We are not publishing the algorithm. We are publishing protocols, data sources, and verdicts.
- Why we pre-register everything
Battery RUL papers are full of numbers that were polished after the fact. We wanted protection against our own hindsight bias, so every experiment in this campaign follows the same rule: the prediction — with explicit numeric bands — is written to a dated, hashed document before the experiment runs. Afterward we score against the sealed document, not against our memory. Two of our sealed predictions failed. Both failures are below, unchanged.
- Scorecard: diagnosis mode (six fleets)
The task: predict remaining useful life (RUL) under strict fleet-level protocols (leave-one-battery-out or leave-one-batch-out, nested train-only CV for all choices, official splits where they exist). Two task modes are used, and we label them explicitly: (a) lifetime from early data — MIT only, features from cycles ≤ 100 predict the full cycle life; (b) online RUL — all other fleets, at each cycle the model uses only data up to that cycle (no future information). Baseline ("naive") uses cycle count + current capacity only; the treatment adds an early-life feature set selected by the diagnostic. Leverage = (naive − treated) / naive, computed against the deployed treated configuration in every row.
Fleet Protocol Naive RMSE Treated RMSE (deployed) Leverage
NASA Li-ion (B0005/6/18) nested LOBO (b) 11.34 7.03 −38.0%
MIT/Severson (124 cells) official split (a) 248.8 (primary) / 481.4 (secondary) 128.7 (primary) / 189.9 (secondary) −48.3% / −60.6%
CALCE CS2 (4 cells) LOBO (b) 10.82 10.82 (suite declined features) 0% — called in advance
Oxford Degradation (8 cells) nested LOBO (b) 76.5 56.9 (deployed); 51.4 best subset −25.6% (subset: −32.8%)
HUST (77 cells, 10 batches) leave-one-batch-out (b) 58.7 38.5 −34.4%
CALCE CX2 (7 prismatic cells) nested LOBO (b) 81.2 63.4 −21.9% (see §3b)
Oxford note: the deployed treated model (56.9 RMSE, −25.6%) is the number the sealed band prediction was scored against in §5 — the post-hoc best subset (51.4, −32.8%) is reported for completeness but is not the deployed figure.
The CALCE row deserves explanation: before modeling, our diagnostic said this fleet has "nothing to repair," so the suite declined to add features and deployed the baseline. It was right — adding features made things worse (23.68 vs 10.82). Knowing when not to act is half the value of a diagnostic.
2b. Benchmark position — same-protocol comparisons only
We compare numbers only where the protocol matches, and we cite the source for every literature figure. RMSE in cycles; primary / secondary test sets as defined in each source.
MIT/Severson official split (41 train / 43 primary / 40 secondary), EOL = capacity < 0.88 Ah, features from cycles ≤ 100 only:
Model Primary Secondary
Severson et al. — discharge model (Table 1) 91 173
Severson et al. — variance model (Table 1) 138 196
Severson et al. — full model (Table 1) 118 214
BatteryML (Microsoft, ICLR 2024) — MATR-1/2 dummy baseline 398 510
Ours (diagnostic-selected features, ridge) 128.7 189.9
Position: we beat the variance and full models on the secondary set and the variance model on the primary set; the discharge-curve model (91/173) remains ahead of us. Our dummy-baseline check (392.8 / 491.8) lands within 1.3% (primary) and 3.6% (secondary) of BatteryML's published reproduction (398 / 510), which independently anchors that our split and labels match the literature pipeline.
NASA Li-ion: no comparison claimed. Most published NASA numbers train and test on cycles of the same battery; ours is leave-one-battery-out across different cells. Those protocols measure different things, so we do not put the numbers side by side.
HUST / Oxford / CALCE: run under our own strict fleet-level protocols (below); we are not aware of published results under identical protocols, so we report absolute numbers only.
We deliberately make no "SOTA" or "record" claims anywhere in this post. Every number above comes from our own runs; literature figures are quoted with their sources.
- The part we're most proud of: predicting the leverage itself
Across the first five fleets we noticed the leverage is not random — it tracks how heterogeneous the fleet is. We formalized this with two dispersion indices computed from early-life data only [we are keeping the exact definitions unpublished], and a band rule that maps index values to expected leverage. Then we tested it on a fleet we had never touched:
> Sealed prediction (before any HUST modeling): "Both indices fall in the mid band. Expected leverage: [−45%, −20%], central estimate ≈ −30%."
Measured: −34.4%.
Every fleet scored so far lands in its assigned band under the final dual-track diagnostic — one of them (HUST) fully pre-registered end-to-end, and one (Oxford) assigned correctly only after its original single-track call failed and forced the revision documented in §5. The sixth fleet gets its own section (§3b) with a fully public, cryptographically seeded protocol. For practitioners this is the useful takeaway: given a new fleet, early-life data alone can tell you in advance whether richer features are worth paying for.
3b. The cryptographically verifiable instance (sixth fleet, CX2)
For the sixth fleet (CALCE CX2 — a different prismatic cell and test rig from CS2), we ran the full public protocol, and this time the two heterogeneity indices disagreed — one landed in the mid band, the other near-flat. Our rule ("either-high governs") was thus facing its first hard case. Sequence, all timestamped:
2026-07-19 20:43 UTC — sealed prediction (band [−45%, −20%], falsification clause, full protocol) hashed and committed publicly: github.com/angus81226-glitch/sealed-predictions
After the commit — the experiment's random seed (741333625) was drawn from the hash of Bitcoin block 958777, the first block mined after the commitment. A proof-of-work block hash is unknowable until the moment it is solved — not even the miner knows it in advance — so the results could not have existed before the commitment did.
Experiment ran. Measured leverage: −21.9% — inside the sealed band. The sealed document is revealed in the same repo; anyone can re-hash it and compare against the 20:43 commitment.
One correction, disclosed in full in the repo (commitments/2026-07-20-cx2-verdict-v3-erratum.txt): the block's miner-declared header timestamp (20:33Z) predates our commit. Header stamps are not real time — in this very window, block 958776 is stamped older than its own parent — which is exactly why the protocol rests on the hash's unpredictability, not the header clock. We have since hardened the rule: future commits will name the chain tip (height + hash) explicitly, and the seed block's header time must be ≥ the commit time.
Six fleets, six correct band assignments under the final diagnostic — two of them pre-registered, one of them cryptographically verifiable by anyone. (Counting rigor: Oxford's original call failed, §5; its assignment under the revised dual-track diagnostic is correct. We count band assignments by the final diagnostic, and we count the failure openly.)
- Design mode: finding the best charging policy from the spec sheet
The MIT/Severson fleet cycled 124 cells under 72 distinct charging-policy strings in the authors' metadata — 64 standard two-step policies plus 8 'new-structure' variants from the follow-up batch — with measured lifetimes from 148 to 2237 cycles. We asked: can a balance-point calculation — attack energy vs. the cell's rated defense, using only public quantities (policy parameters, the manufacturer's 4A max continuous charge spec, Joule heating) — locate the optimum without lifetime data?
Results (all sealed in advance except where noted):
- The empirically best policy (3.6C to 80%) sits at balance ratio 0.978 — 2.2% from the predicted balance point of 1.0. The ratio is computed on Joule-weighted attack energy, i.e. R = (3.6/3.64)² = 0.978, not on raw current (which would give 3.6/3.64 = 0.989); the squared form follows from I²R heating and is stated here explicitly to avoid ambiguity.
- Death line: every policy above ratio 1.5 died before 1200 cycles. Correct.
- Ranking in the over-stressed region only worked after we added where-in-the-charge the stress lands (high rates late in the charge are far more damaging than the same energy early). Sealed prediction: correlation strengthens past |ρ| = 0.4. Measured: ρ = −0.60 (from −0.16). Hit.
- A second sealed refinement (rate-switch transient penalty) made things worse (ρ collapsed to −0.15). Falsified, removed, kept in the record.
4b. Full test protocols and exact parameters (reproduction-grade)
Everything below is exactly what we ran. The only items withheld are the diagnostic's selection logic; all engineering parameters are listed.
NASA Li-ion (B0005/6/18)
- EOL: discharge capacity < 80% of rated (B0005 = cycle 125)...