Electrochemical impedance spectroscopy (EIS) is a critical tool for battery characterization, providing insights into kinetic and transport processes, interfacial phenomena, and degradation mechanisms. To ensure reliable and comparable results, adherence to international standards and best practices is essential. This article outlines key standards, methodologies, and challenges in EIS testing for batteries, focusing on cell preparation, parameter selection, and data interpretation.

Cell Preparation and Measurement Conditions
Proper cell preparation is foundational for reproducible EIS measurements. The cell must be in a stable state before testing, typically achieved by holding it at a defined state of charge (SOC) and temperature until voltage and current stabilize. ISO 16737-1 specifies guidelines for preconditioning lithium-ion cells, recommending equilibration times of at least two hours at the target SOC and temperature. ASTM E2529 emphasizes the importance of temperature control, as impedance spectra are highly sensitive to thermal variations. A deviation of ±1°C is generally acceptable for most battery testing.

Electrode connections must be low-resistance and consistent. Four-terminal Kelvin sensing is preferred to minimize contact resistance errors. The use of spring-loaded or clamped contacts, as described in IEC 62660-1, helps maintain uniform pressure and reduce variability. For pouch or prismatic cells, ensuring uniform current distribution across the electrodes is critical to avoid artifacts in the impedance response.

Parameter Selection and Measurement Protocols
The selection of frequency range, amplitude, and DC bias significantly impacts EIS results. A typical frequency range for battery testing spans from 10 mHz to 100 kHz, as recommended by DIN 50918. Lower frequencies probe slower processes like diffusion, while higher frequencies capture ohmic and charge-transfer resistances. The amplitude of the AC signal should be small enough to maintain linearity, usually between 5-20 mV, as per ASTM E1050. Excessive amplitude can induce nonlinear responses, distorting the impedance spectrum.

DC bias must align with the cell's operating voltage. Applying EIS at multiple SOCs (e.g., 10%, 50%, 90%) provides a comprehensive view of impedance behavior across different states. ISO 12405-3 suggests a stepwise SOC approach with equilibration periods between measurements. For dynamic conditions, such as during charging or discharging, specialized protocols like current-interrupt EIS may be employed, though these require careful synchronization to avoid transient artifacts.

Data Quality and Reproducibility
Ensuring reproducibility requires strict control of environmental and operational variables. Humidity can affect measurements, particularly for open-cell configurations, making dry room conditions (<1% RH) advisable for sensitive studies. Repeat measurements on the same cell should yield a coefficient of variation below 5% for key parameters like ohmic resistance, as noted in SAE J2289.

Statistical validation is increasingly used to assess measurement reliability. Techniques such as Kramers-Kronig transformation check the consistency of EIS data by verifying compliance with linearity, causality, and stability criteria. Outliers or non-physical responses (e.g., negative resistances) indicate measurement errors or unstable cell conditions.

Equivalent Circuit Modeling and Pitfalls
Equivalent circuit models (ECMs) are widely used to interpret EIS data, but overfitting is a common challenge. A typical ECM for batteries includes elements like ohmic resistance (RΩ), charge-transfer resistance (Rct), double-layer capacitance (Cdl), and Warburg diffusion (W). ASTM E2592 recommends starting with simple models (e.g., Randles circuit) and incrementally adding elements only if justified by physical mechanisms.

Overfitting occurs when overly complex models are applied to noisy data, resulting in non-unique solutions. To avoid this, the chi-squared (χ²) goodness-of-fit metric should be monitored, with values below 0.01 indicating a reasonable fit. Bayesian information criterion (BIC) or Akaike information criterion (AIC) can further guide model selection by penalizing unnecessary complexity.

Common errors in ECM analysis include neglecting distributed elements or assuming ideal capacitors. Real-world systems often exhibit constant phase elements (CPE) due to surface heterogeneity. Replacing ideal capacitors with CPEs, as described in IEC 62620, improves model accuracy. Additionally, ECMs should be validated against physical measurements (e.g., DC resistance tests) to ensure consistency.

Standardized Reporting and Interlaboratory Comparisons
To facilitate cross-study comparisons, reporting should include detailed metadata such as temperature, SOC, signal amplitude, and cell geometry. ISO 9001:2015 outlines general quality management principles applicable to EIS data reporting. Interlaboratory round-robin tests, like those conducted under the IEA Annex 23 framework, help identify systematic biases and improve harmonization.

Emerging trends include the use of machine learning for automated EIS analysis and model selection. However, these methods must be grounded in electrochemical principles to avoid black-box interpretations. Standardization bodies are beginning to address these advancements, with preliminary guidelines under development by ASTM Subcommittee D02.50.

Conclusion
Robust EIS testing in batteries demands meticulous attention to cell preparation, parameter selection, and data interpretation. International standards provide a framework for reproducibility, while best practices like Kramers-Kronig validation and cautious ECM fitting mitigate common pitfalls. As battery technologies evolve, ongoing collaboration between researchers and standardization organizations will be critical to refine EIS methodologies and ensure their relevance to next-generation systems.