Multi-Modality Cross-Validated State-Of-Charge

Mechanism & Primitive Description

The multi-modality cross-validated state-of-charge primitive treats each available SOC modality as a noisy estimator of a single underlying scalar, the true charge state of the working cell, and applies a Bayesian combination rule to fuse the modality-specific estimates into a posterior estimate. Three modalities are explicitly disclosed: density-based SOC (derived from contemporaneous mass and volume measurement, as described in the combined-density-SOC primitive), electrochemical impedance spectroscopy (derived from the cell's small-signal frequency response across a calibrated frequency band), and coulomb counting (derived from time-integrated current across the cell's terminals).

Each modality probes a physically distinct pathway. The density modality responds to bulk mass and volume changes that accompany lithium intercalation, gas evolution, structural compaction, and electrostatic strain. The impedance modality responds to interfacial charge-transfer kinetics, double-layer capacitance, and bulk ionic conductivity, all of which depend on charge state. The coulomb-counting modality responds to the integral of charge transferred and is the most direct measure under ideal sensing but accumulates integration error and is blind to self-discharge and parasitic reactions.

Because the three pathways are physically independent, their dominant uncertainty sources are uncorrelated. Density-modality uncertainty is dominated by sensor-fixture compliance and ambient temperature; impedance-modality uncertainty is dominated by contact resistance, frequency noise, and temperature; coulomb-counting uncertainty is dominated by current-sensor offset, drift, and missed-cycle accounting. The Bayesian combination weights each modality by the inverse of its instantaneous variance and produces a posterior whose variance is approximately the harmonic mean of the input variances. For three modalities of comparable native precision, the posterior variance is roughly one-third of the single-modality variance and the posterior standard deviation is roughly one-half, the disclosed quantitative target. The combination is performed in real time on each measurement event and the resulting posterior is signed into the credentialed admissibility profile as a multi-modality SOC attestation.

Operating Parameters & Engineering Envelope

The primitive operates across a sampling cadence range bounded below by the slowest modality (impedance, which requires a finite frequency sweep) and above by the fastest application requirement. Typical cadences range from 0.1 Hz (one sample per ten seconds, suitable for grid-services dispatch) to one sample per cycle (suitable for commissioning verification). The density modality is sampled continuously; the impedance modality is sampled at user-configured intervals using either a galvanostatic excitation injected during nominal operation or a dedicated rest-period sweep; the coulomb-counting modality is sampled at the current-sensor's native rate (typically 1 kHz or higher) and downsampled by integration to match the fusion cadence.

Each modality reports both a point estimate and an uncertainty (variance or covariance). The density modality's uncertainty is computed from the propagated mass-channel and volume-channel uncertainties; the impedance modality's uncertainty is computed from the frequency-domain noise floor and the parameter-extraction Jacobian; the coulomb-counting modality's uncertainty grows linearly with elapsed time since last reset and is reset to a calibration-floor value at each end-of-discharge or end-of-charge event. The fusion algorithm requires that all three uncertainty estimates be honest, under-reporting of uncertainty by any single modality biases the posterior, and the disclosed primitive incorporates a residual-consistency check that flags modality disagreement exceeding statistical expectation.

Engineering parameters include the impedance frequency band (typically 0.1 Hz to 10 kHz), the impedance-model order (single-RC through full Randles), the coulomb-counter integration window, and the residual-consistency threshold (typically three standard deviations of the predicted disagreement distribution). All parameters are settable per cell and recorded in the cell's profile.

Alternative Embodiments

In a first embodiment, two modalities (density and coulomb counting) are fused, yielding a posterior with one over square-root of two reduction. In a second embodiment, four or more modalities are fused, adding terminal-voltage-curve lookup and surface-temperature-derived state, for further uncertainty reduction at the cost of additional sensing complexity.

In a third embodiment, the Bayesian combination is replaced by a Kalman filter with state propagation between samples, exploiting temporal correlation in the underlying state to reject sample-level noise. In a fourth embodiment, a particle-filter combination is used to handle non-Gaussian uncertainty distributions, which arise when one or more modalities operate near a saturation limit. In a fifth embodiment, the fusion is performed locally at the cell's monitoring subsystem; in an alternative, the per-modality estimates are streamed to a remote fusion service that signs the posterior with a service credential. In a sixth embodiment, the residual-consistency check is used not only as a diagnostic but as a trigger for adaptive remeasurement, with the disagreeing modality re-sampled at higher cadence until consistency is restored or a fault is declared.

Composition with Adjacent Primitives

The multi-modality primitive composes upward with the combined-density-SOC primitive, which supplies the density modality through co-located mass-and-volume sensing. It composes with the credentialed-admissibility-profile primitive, which receives the signed posterior as a measurement event bound to cell identity. The signed-event composition supports decision-grade applications in which a downstream consumer (a grid dispatcher, a compliance auditor, an end-of-life-phase orchestrator) requires not merely a state estimate but a verifiable attestation of its provenance and uncertainty.

The primitive composes with the asymptotic-equilibrium-saturation primitive: the multi-modality posterior provides the high-confidence capacity reading against which the equilibrium-detection logic evaluates per-cycle slope. It composes with the capacity-rise-inversion primitive: the multi-modality posterior tracks the rising capacity trajectory with sufficient resolution to discriminate genuine rise from measurement artifact during break-in.

Prior-Art Distinctions

Prior-art battery-management systems typically rely on a single SOC modality or apply a primitive blending of two modalities (a coulomb-count corrected at endpoints by a voltage-curve lookup, for example). Such blending is not Bayesian: it does not propagate uncertainties, it does not produce a posterior variance, and it does not flag modality disagreement. Prior-art impedance-based SOC is offered as a diagnostic adjunct but is rarely fused with coulomb counting under a principled statistical framework, and density-based SOC is essentially absent from prior art.

The disclosed primitive is distinct in three respects. First, it incorporates a density modality whose physical pathway is independent of both impedance and charge integration, providing a third axis of cross-validation. Second, it applies a Bayesian combination that propagates and reports posterior uncertainty, supporting decision-grade rather than diagnostic-grade outputs. Third, it composes with the credentialed-admissibility-profile to produce signed measurement events whose downstream consumers can verify provenance. No prior-art system known to the inventor combines all three of these features.

Disclosure Scope

The disclosure encompasses any state-of-charge determination apparatus or method that fuses two or more physically independent SOC modalities, where at least one modality is density-based, through a statistical combination rule that propagates per-modality uncertainty to a posterior uncertainty. Scope extends to all combination rules in the Bayesian family, including but not limited to direct Bayesian update, Kalman filtering, particle filtering, and minimum-variance linear combination, and to all per-modality uncertainty models (Gaussian, mixture, empirical) that admit principled propagation.

Scope extends to the residual-consistency check as a diagnostic and as an adaptive-remeasurement trigger. Scope extends to all combinations of modality count and modality identity, provided a density-derived modality is among them. Scope extends to local, remote, and hybrid fusion topologies. Scope extends to the composition with the credentialed-admissibility-profile primitive: any system in which the multi-modality posterior is cryptographically bound to a cell-identity record and presented as a verifiable measurement event falls within the disclosed primitive. Scope extends to all downstream applications, grid-services dispatch, end-of-life-phase transition decisions, warranty and compliance reporting, that consume the resulting signed posterior.