Independent verification of battery SOH & RUL models.
I don't build your model — I audit it against held-out data and physical law, with every number regenerating from a script you keep.
The party that builds a battery SOH/RUL model usually also reports its accuracy. That is a structural conflict of interest — and it is why "98% accuracy" claims so often collapse on a cell the model has never seen. You have to trust a vendor's number, but you have no neutral way to check it.
This audit is the neutral check. I did not build your model, I am not selling you a competing one, and I have no incentive to flatter the result. The only product is an honest verdict on whether the claim survives contact with data it has never seen.
Was the model validated cell-wise, or did consecutive cycles from one cell leak across train and test? Cycle-wise splits inflate accuracy because the model recognizes the cell instead of predicting.
Was any scaler or normalization fit on the full dataset before the split, leaking test-cell statistics upstream into training?
Is the reported error the full-set error, or was a hard cell quietly dropped to match a benchmark?
Does the model respect physics — monotonic aging, no impossible capacity "recovery", internal resistance within real bounds — or does it quietly violate conservation?
Does a claimed "predictor" actually forecast degradation early enough to act on, or does it only track degradation that is already happening?
Does the model know how much it doesn't know — calibrated confidence intervals — or does it deliver a single bare number that hides its own error?
Every number I report regenerates from a single script I hand you. If I can't regenerate it, I don't claim it.
Survives held-out data and physical law; the number is the honest number.
The effect is leakage, inflation, or curation; the corrected number differs, and I show by how much and why.
The claim cannot be tested as written; I specify what would make it testable.
I hold my own work to the standard I sell. The public, DOI-archived portfolio is itself a worked example of every check above — applied to my own analyses first, catching my own errors before anyone else could:
Reproduced a 99.65% sample-reduction claim, confirmed it on quasi-static profile, and showed it does not transfer to dynamic load (24.84% SOC error).
Gradient-free local adaptation of frozen INT4 networks with O(1)-in-depth memory scaling and rigorous boundary failure analysis.
Li-ion battery prognostic predictability boundaries, impedance observability study and early predictions on NASA PCoE dataset.
BESS operators, second-life firms, battery startups, and BESS investors / independent engineers who must trust a vendor's SOH/RUL number but have no neutral party to check it. The EU Battery Regulation's digital battery passport (mandatory 2027) is creating a wave of SOH documentation that will need exactly this kind of independent check.