We don't build your model — we check whether its numbers survive contact with data they haven't seen.
VolMax Studio Lab is an independent verification practice for battery and energy machine-learning models: leakage detection, metric-integrity audits, physical-consistency checks. Power electronics + domain-grounded ML, with every finding reproducible from source.
It is why a model reporting 98% accuracy in the lab can collapse on a cell it has never seen — the result was leakage, an inflated metric, or a curated benchmark, not prediction.
Buyers, investors, and insurers can't tell a sound model from a flattering one by reading the vendor's own numbers.
VolMax is the neutral check. We don't sell a competing model, so we have no incentive to flatter a number. Independence is the product.
Independent audits designed to catch interpretation artifacts.
Independent integrity audit of an SOH/RUL model or a vendor's accuracy claim. We check split integrity (cell-level vs cycle-level), preprocessing leakage, metric honesty (full-set error, no dropped cells), and physical consistency. Deliverable: a reproducible report where every number regenerates from one script.
We reproduce a claimed result on a clean, group-aware split and report the gap between the claimed and the verified number — and trace its cause. The honest number is the one that survives a new cell.
Verification of measured-signal pipelines (power quality, vibration, MCSA, PV) against physics and the sensor's real resolution — built on a test-covered signal-processing core.
A reproducible audit report; a plain verdict (where the claim holds, where it breaks, the corrected number); the regeneration script.
An accuracy number. We are not selling a model, so we make no "95%" promise — that is the vendor claim we exist to test. A verifier who guarantees a flattering result has the same conflict of interest as the vendor. We do not build the model under test, and we list no client outcomes we don't have — the proof is the public work below.
Every finding is produced by one procedure: reduce to first principles → hunt the interpretation artifacts (leakage, inflation, curation) → compare to state of the art → deliver a reproducible verdict. The caveat is the mechanism — each correction comes from a constraint that narrows an overclaim. We apply it to our own work first.
Read the full method →The standard we hold your model to, we applied to our own first. These are public, reproducible, test-covered repositories — the credential that replaces a CV.
NASA PCoE + Severson/Attia, DOI-archived. Honest findings led by their limits: where early prognosis breaks, where impedance is observable vs predictive, capacity-regeneration isolated from true fade. Includes the worked example where the audit caught its own pipeline overclaiming three times. Every number regenerates from reproduce.py.
github.com/VolMax-Studio/Battery_Health_PortfolioTest-covered signal library (RMS, THD, DWT, Hilbert). The measurement layer the audits stand on.
Hierarchical DGA fault diagnosis, with tested boundaries.
PV fault detection on real NREL data + injected benchmarks, honestly framed.
The "PUE Loophole" audit: how PSU conversion losses mask real facility savings.
(+ Power Quality, NILM, MCSA, CWRU, Grid Frequency, VPP — domain breadth.)
View all on GitHub →20+ years in power electronics and field electrical work, now applying that hardware intuition to independent verification of energy ML. The combination is the point: a pure data scientist doesn't know why a converter loses efficiency at high frequency; a pure hardware engineer doesn't audit a model's train/test split.
VolMax stands at the intersection — between the measurement and the claim.
Physics doesn't lie. Sensors don't lie. Everything between is interpretation — and that's where I check.
Alongside the verification practice, VolMax runs hardware and edge-ML R&D — embedded signal processing on STM32/ESP32, and a hardware safety-interlock concept (analog-to-logic override, sub-2.5µs actuator-interrupt latency, Serbian IPO priority filed). These inform the measurement side of the audits and are in active development.
VolMax Studio Lab d.o.o.
Independent energy-ML verification · Serbia · EU/remote engagements
Email: volmax.core@gmail.com
GitHub: github.com/VolMax-Studio
For audit enquiries, include the model type and dataset if possible.