We present a deterministic residual-structure signal for lithium-ion battery health monitoringand evaluate it on the NASA PCoE battery aging dataset (cell B0005). The signal indicates atransition region at cycle 38, compared to cycle 79 for a standard 85% capacity threshold andcycle 101 for end-of-life (80%), corresponding to a 41-cycle lead relative to threshold crossing.The signal remains elevated over multiple cycles, indicating sustained structural change ratherthan a transient fluctuation. Results are reproducible using the accompanying dsfb-batterycrate and notebook.This work illustrates, on a real NASA PCoE battery trajectory under a fixed and reproducibleconfiguration, that the DSFB (Drift-Slew Fusion Bootstrap) layer produces a persistent structuraltransition at cycle 38, while a representative fixed-threshold baseline triggers at cycle 79,corresponding to an observed lead of approximately 41 cycles, with outputs provided as anexplicit, audit-ready diagnostic trace rather than a scalar alarm.The method operates as a read-only augmentation layer over existing battery managementsystem (BMS) estimators and interprets residual quantities as structured diagnostic signals.The paper presents the empirical behavior of the method on this dataset without claiminggeneralization across chemistries or operating regimes. The DSFB framework executes entirelyat the edge using fixed logic, requiring no cloud connectivity and no model retraining afterdeployment.We develop a domain-instantiated DSFB structural semiotics endoduction framework forlithium-ion battery health monitoring. The central claim is not that battery management systems(BMSs) should be replaced, but that the residual quantities they already compute or can cheaplyexpose—capacity deviation, voltage innovation, internal-resistance change, thermal couplingmismatch, and their temporal derivatives—can be formalized as structured signs rather thantreated only as nuisance error. In this view, capacity fade becomes a residual trajectory, driftbecomes a syntax of persistent degradation direction, slew becomes a diagnostic marker of regimeacceleration, and admissibility envelopes become a grammar distinguishing healthy aging fromboundary approach and structural violation. This interpretation is evaluated on the NASAPCoE battery aging dataset (cell B0005), where a residual-structure signal indicates a transitionregion at cycle 38, compared to cycle 79 for a standard 85% capacity threshold and cycle 101 forend-of-life (80%).The resulting framework positions DSFB as an interpretive augmentation layer over incumbentbattery estimation pipelines such as equivalent-circuit-model observers, Coulomb-counting stacks,impedance-informed estimators, and data-driven state-of-health predictors. We formulate battery-domain reason codes, a transferable heuristics bank for degradation motifs, and finite-timedetectability results showing that early warning is governed by structural separation relative toadmissible aging envelopes rather than by state-of-health magnitude alone. This is especiallyimportant for knee onset, lithium-plating-like acceleration, and pack heterogeneity, wherepointwise thresholding tends to arrive late. In the evaluated case, this corresponds to a 41-cyclelead relative to threshold crossing, with the signal remaining persistently elevated rather thanappearing as a transient fluctuation.We further develop a staged validation pathway centered on the NASA Prognostics Center ofExcellence battery aging dataset, chosen because it provides cycle-to-failure trajectories withcapacity and impedance information under controlled conditions and supports executable andreproducible proof-of-concept evaluation via a provided implementation and notebook. The paperis written as a licensing-grade methodological and systems paper: it argues that the strongestcommercial position for DSFB is as a certifiable, low-compute, explainable augmentation layerthat reads what existing BMS architectures already know, surfaces typed early-warning signalsearlier than threshold-based indicators, and produces audit-ready degradation narratives suitablefor aerospace, defense, grid storage, industrial mobility, and high-consequence electric platforms.
Riaan De Beer (Mon,) studied this question.