From fleet trajectories to single-cell promises: hierarchically weighted conformal prediction for battery SOH monitoring
Organizations: Independent Researcher, Beijing, China
Abstract
Interval predictions from conformal prediction are usually read as "90% coverage", but on nested battery data that number promises the fleet average, not the individual cell. We measure the gap on 124 public LFP cells under a preregistered three-tier reporting protocol (fleet, batch, cell): pooled coverage looks healthy (0.875-0.906 across splits) while the worst cell is covered 0.1-2% of the time, with exact binomial tests against the nominal level. We then repair part of the gap with a hierarchically weighted conformal engine whose weights consume only side information frozen at prediction time - signature features, batch labels, cycle indices - and never touch residuals. The preregistered repair criterion passes: 10th-percentile cell coverage moves 0.750 to 0.830 (53% of the gap), the worst cell 0.642 to 0.694, with zero deferrals and a flat sensitivity spectrum. What the engine does not repair is localized rather than left open: under protocol drift the frozen weights harm monotonically on the SOH scale (the absolute scale is reported in full), a structural dead end we report instead of tuning away; and repair capacity is bounded by calibration-pool battery diversity, measured on both datasets. On a cross-chemistry external set the calibrated intervals collapse (0.286), while the within-domain skeleton replicates (pooled 0.933, worst cell 0.686). The deliverable is a reporting protocol - three tiers of promise anchored to measured regimes, worst-cell statistics, deferral share - that makes per-cell interval claims auditable before deployment.
Figures & tables
| scale | panel | pooled | min batch | worst cell | frac | perm. | |
|---|---|---|---|---|---|---|---|
| SOH | A (random, 5-seed median) | 0.906 | 0.787 | 0.642 | 4.2e-55 | 28% | — |
| SOH | C (official split) | 0.875 | 0.790 | 0.014 (b2c1) | 35% | — | |
| SOH | B (2017 2018 drift) | 0.799 | — | 0.001 (b3c29) | — | — | <0.001 |
| abs | A | 0.897 | 0.790 | — | 6.1e-58 | 28% | — |
| abs | C | 0.888 | 0.778 | 0.020 (b2c1) | 27% | — | |
| abs | B | 0.810 | — | 0.001 (b3c29) | — | — | <0.001 |
| arm | pooled | q10 | min batch | worst | frac | deferral |
|---|---|---|---|---|---|---|
| baseline | 0.906 | 0.750 | 0.787 | 0.642 | 28% | 0% |
| engine | 0.909 | 0.830 | 0.884 | 0.694 | 16% | 0% |
| arm | pooled | q10 | min batch | worst | width | fallback |
|---|---|---|---|---|---|---|
| baseline | 0.906 | 0.750 | 0.787 | 0.642 | 0.0019 | — |
| engine (ours) | 0.909 | 0.830 | 0.884 | 0.694 | 0.0021 | 0% |
| Mondrian (within-batch) | 0.899 | 0.825 | 0.869 | 0.631 | 0.0022 | 0% (A) |
| GP posterior | 0.965 | 0.913 | 0.932 | 0.786 | 0.0011 | none possible |
| BHM-lite | 0.953 | 0.863 | 0.910 | 0.831 | 0.0039 (2 ) | 0% (A) |
| arm | pooled | worst | reading |
|---|---|---|---|
| cross-domain plain (Severson pool Oxford) | 0.286 | 0.137 (oxc2) | 4.5e-36; 100% of cells below 0.85 |
| cross-domain engine | 0.376 | 0.216 | +0.09; not saved |
| within-domain plain (LOO 8) | 0.933 | 0.686 (oxc2) | oxc2 binomial 2.3e-05; Bonferroni 1.9e-04 |
| within-domain engine | 0.933 | 0.686 | identical to plain; ESS_batt ceiling 3.73 (cap flag) |
| # | prediction (summary) | outcome |
|---|---|---|
| P1 | Mondrian: batch tier repaired, cell tier untouched | , sharpened: q10 repaired cosmetically while worst unmoved |
| P2 | GP: overconfident, worst-cell disaster | reshaped: conservative via non-converged hyperparameters; conclusion (no trustworthy ) holds with rewritten mechanism |
| P3 | BHM between base and Mondrian | half : conservative coverage at 2 width; drift “win” is a 100% fallback artifact |
| P4 | sim-only reverses the median | reversed: no reversal under battery-level pools; cause relocated to pool narrowness |
| P5 | bal-only base, tail kept | ; plus unexpected +0.037 pooled on the drift panel |
| P6 | trajectory family hurts coverage most | reshaped to the width axis: 29 width, worst-cell collapse, q10 improves |
Conformal Prediction
Conformal prediction wraps predictive models with calibrated sets or intervals that provide statistical coverage guarantees.
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