Internal resistance parameters extracted from EIS are used as state indicators, not as standalone SoC or SoH measurements. Equivalent-circuit fitting separates the spectrum into parameters such as series resistance (R_s), charge-transfer resistance (R_{ct}) or polarization resistance (R_p), capacitance, and diffusion terms. (R_s) is primarily useful for tracking SoH and aging, while (R_{ct}) changes with electrochemical operating state and can support SoC estimation when calibrated against temperature, cell history, and voltage.

Core takeaway: EIS converts frequency-dependent impedance into physically meaningful parameters. A calibrated relationship between those parameters and reference measurements allows (R_{ct})-related changes to support SoC estimation and resistance growth—especially (R_s)—to support SoH tracking.

How EIS Produces Battery Resistance Parameters

Measuring the impedance spectrum

EIS applies a small-amplitude sinusoidal voltage or current perturbation over a range of frequencies and measures the resulting current response. The resulting impedance contains both resistive and reactive information, represented by magnitude and phase or by real and imaginary components.

Because different physical processes respond on different time scales, frequency selection helps separate them. High-frequency behavior is associated mainly with ohmic paths and connections, mid-frequency behavior with interfacial charge transfer, and low-frequency behavior with diffusion and capacitive processes.

Fitting an equivalent-circuit model

The measured spectrum is fitted to an equivalent circuit containing elements such as:

• Series resistance (R_s) or (R_\Omega): Electrolyte, current collectors, tabs, contacts, and other ohmic contributions.
• Charge-transfer resistance (R_{ct}): Kinetic resistance at the electrode–electrolyte interface.
• Polarization resistance (R_p): A broader resistance term that may include charge-transfer and other polarization effects, depending on the model.
• Double-layer capacitance: Interfacial capacitive behavior associated with electrode surfaces.
• Diffusion elements: Low-frequency behavior caused by ion transport within electrodes and related solid-state limitations.

The fitted values are useful only when the model is physically appropriate and the fit reproduces the measured spectrum adequately.

Using Resistance Parameters to Estimate SoC

Why (R_{ct}) changes with SoC

The charge-transfer process depends on electrode potential and the availability of electrochemically active species. As SoC changes, the activation barrier and reaction kinetics can change, causing (R_{ct}) or (R_p) to vary in a repeatable manner for a particular cell chemistry and operating condition.

This makes (R_{ct}) a potential SoC-sensitive feature. The relationship is usually not universally linear and may be strongly chemistry-dependent.

Building the SoC relationship

A practical system first measures EIS at known SoC values under controlled conditions. It then creates a calibration map such as:

[ \mathrm{SoC}=f(R_{ct}, R_s, T, V, \text{current history}) ]

The calibration may use lookup tables, regression models, equivalent-circuit observers, or filtering algorithms. During operation, a newly extracted resistance value is compared with the calibrated relationship to update the SoC estimate.

Why SoC cannot be inferred from resistance alone

The same resistance value can occur at different SoC values when temperature, aging, current history, or relaxation state differs. For this reason, reliable SoC estimation normally combines impedance features with terminal voltage, current integration, temperature, and a battery model.

In practice, EIS-derived (R_{ct}) is best treated as a corrective or diagnostic observation that improves a conventional SoC estimator rather than replacing all other measurements.

Using Resistance Parameters to Estimate SoH

Tracking series-resistance growth

(R_s) reflects bulk electrolyte conductivity, current-collector and contact resistance, and other rapid ohmic losses. As the cell ages, electrolyte changes, interfacial layers grow, contacts degrade, and transport paths can become less conductive.

The resulting increase in (R_s) is therefore a useful indicator of degradation and long-term loss of performance. It can be compared with the resistance of a new or reference cell:

[ \Delta R_s = R_{s,\mathrm{aged}}-R_{s,\mathrm{new}} ]

or normalized against a baseline:

[ R_{s,\mathrm{norm}}=\frac{R_{s,\mathrm{aged}}}{R_{s,\mathrm{new}}} ]

Tracking charge-transfer degradation

Aging can also increase (R_{ct}) through changes such as interfacial-film growth, loss of active surface area, and reduced reaction kinetics. Consequently, the evolution of (R_{ct}), polarization resistance, and diffusion-related parameters can provide additional information about the degradation mechanism.

These parameters help distinguish a cell with mainly ohmic degradation from one experiencing significant interfacial or transport limitations.

Relating resistance to capacity-based SoH

Capacity-based SoH is commonly expressed as:

[ \mathrm{SoH}_{\mathrm{capacity}}

\frac{Q_{\mathrm{available}}}{Q_{\mathrm{rated\ or\ initial}}} \times 100% ]

Resistance-based indicators do not directly measure capacity. Instead, they rely on a calibrated correlation between impedance growth and capacity fade established through controlled aging tests.

A robust system therefore combines EIS parameters with periodic capacity measurements, rather than assuming that one resistance value uniquely determines remaining capacity.

A Practical Estimation Workflow

Establish controlled reference data

Measure EIS at known SoC, temperature, and aging levels. The reference set should include both fresh and aged cells because the resistance–SoC relationship itself can change as the battery degrades.

Temperature control is particularly important because electrolyte conductivity and reaction kinetics vary substantially with temperature.

Extract and validate parameters

Fit each spectrum to the selected equivalent-circuit model and record (R_s), (R_{ct}) or (R_p), capacitance, and diffusion-related parameters. The fitting process should include residual checks so that an apparently plausible resistance value is not accepted from a poor model fit.

Correct for operating conditions

Before interpreting a resistance change as SoC or aging, compensate for temperature and measurement conditions. The system should also account for whether the cell is relaxed, charging, discharging, or recovering after a load.

Fuse EIS with conventional state estimation

A battery-management or laboratory analysis system can use EIS parameters as inputs to a state observer. Voltage, current, coulomb counting, temperature, and impedance-derived features can then be combined through model-based filtering or data-driven estimation.

This approach separates the roles of the measurements: current and voltage follow short-term state evolution, while impedance provides an additional view of electrochemical condition and degradation.

Understanding the Trade-offs

Resistance is sensitive but not uniquely interpretable

Resistance changes can indicate SoC, SoH, temperature, or temporary polarization. Without calibration and compensation, attributing every change to aging can produce substantial estimation errors.

Capacity-based SoH remains the direct benchmark

A full charge–discharge capacity test is slower and generally unsuitable for continuous online operation, but it directly measures available capacity. Resistance-based SoH is faster and potentially non-invasive, yet it is an indirect estimate that requires a validated correlation.

EIS measurements require suitable conditions

EIS assumes a sufficiently small perturbation around an operating point. Large disturbances, unstable SoC, active loads, or insufficient relaxation can distort the spectrum and make equivalent-circuit parameters difficult to interpret.

Model choice affects the result

Different equivalent circuits can fit similar spectra while assigning different physical meanings to their elements. (R_p) may include more than pure charge-transfer resistance, so the parameter definition must remain consistent across calibration and deployment.

Online implementation involves practical constraints

Broad-spectrum EIS can be time-consuming and may require specialized instrumentation. Faster systems can measure selected frequency points or use models and machine-learning methods, but reducing the measurement set increases dependence on calibration quality and cell-specific behavior.

Making the Right Choice for Your Goal

Use the parameter that matches the state you are trying to observe, while treating voltage, temperature, and operating history as essential context.

• If your primary focus is SoC estimation: Use calibrated (R_{ct}) or (R_p) features alongside voltage, current, temperature, and relaxation history; do not infer SoC from resistance alone.
• If your primary focus is SoH estimation: Track the growth of (R_s), (R_{ct}), and diffusion-related parameters against fresh-cell and aged-cell baselines.
• If your primary focus is capacity prediction: Correlate EIS parameters with periodic reference capacity tests and validate the relationship across temperature and aging conditions.
• If your primary focus is real-time monitoring: Use selected-frequency or rapid EIS measurements within a model-based estimator, with continuous fit-quality and temperature checks.

With proper calibration and condition compensation, EIS resistance parameters provide a powerful, non-destructive view of both battery operating state and degradation.

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