Entering Electrochemistry | Using EIS Technology for Consistency Screening of Lithium-ion Battery

1. Introduction — Why Consistency Screening Matters

In electric vehicles and energy storage systems, lithium-ion batteries are connected in series and parallel to form modules and packs. The performance and safety of the entire pack can be compromised by a small number of underperforming or defective cells—a phenomenon often described as the “barrel effect,” where the shortest stave determines the barrel’s capacity. Consequently, identifying and screening cells for consistency before pack assembly is crucial for maximizing system lifespan, ensuring safety, and improving secondary utilization rates.

2. Limitations of Conventional Screening Methods

Current industry practices for cell grading before shipment often rely on parameters like open-circuit voltage (OCV), capacity, 1 kHz AC impedance (ACIR), and K-value. While useful, these metrics primarily assess electronic resistance, offering little insight into ionic resistance and the kinetics of internal electrochemical reactions. Furthermore, cells are rarely re-evaluated for consistency immediately before module or pack assembly. These gaps increase the probability of “weak link” cells negatively impacting overall pack performance and longevity.

3. EIS as a Comprehensive Tool for Consistency Screening

Electrochemical Impedance Spectroscopy (EIS) involves applying a small amplitude current or voltage excitation signal to a lithium-ion battery and measuring the corresponding response signal. This technique helps researchers understand the dynamic characteristics of electrochemical reactions within the battery, such as double-layer capacitance, charge transfer processes, and ion diffusion behavior^【1-3】. Figure 1 shows a classic Electrochemical Impedance Spectroscopy (EIS) plot (Nyquist plot) for a lithium-ion battery, where different semicircles represent electrochemical processes with different time constants. The equivalent circuit in the lower right corner of Figure 1 can be used to fit the EIS plot, allowing the extraction of parameters such as capacitance and impedance for each electrochemical process.

Figure 1. Classic EIS Nyquist Plot of a Lithium-Ion Battery^【1】

Unlike standard tests, EIS testing battery properties across multiple frequencies provides a holistic view, capturing both high-frequency electronic conduction and mid-to-low-frequency ionic processes. This makes EIS battery analysis exceptionally well-suited for high-precision consistency screening, helping to identify subtle variations that other methods miss.

4. Practical challenges to industrial EIS testing

Traditional EIS equipment—academic electrochemical workstations—deliver excellent fidelity but are costly, relatively slow, and designed for small-scale research. In addition, modern large-capacity cells have very low absolute internal resistance and large stored charge; performing broadband EIS on these cells sometimes requires current-range amplifiers or specialized front ends that further increase cost and complexity. Consequently, industrial adoption of EIS for high-throughput consistency screening has been limited.

5. An industrial approach: High-throughput EIS Testing for Cell Pack

To bridge this gap, IEST has developed the BCS6000 Battery Consistency Screening System (Figure 2a). This industrial-grade instrument is designed for rapid EIS testing battery performance on large-format cells. It can be integrated with manual fixtures (Figure 2b) or automated feeding equipment (Figure 2c) for high-throughput screening, enabling rapid EIS testing at production line speeds. Crucially, BCS6000 couples high-range impedance acquisition with machine-learning (neural-network) analysis to transform EIS spectra into actionable consistency metrics and group assignments.

• Hardware: aAccommodates large cells, interfaces with manual or automated fixtures, and records Nyquist and Bode spectra across the required frequency range.

• Software: Fits spectra to an equivalent circuit model (e.g., Rs − (Rct||Cdl) − Warburg) and extracts parameters that represent distinct electrochemical processes. It also feeds spectral features into a trained neural network to compute a single-cell “dispersion” or consistency index for grouping.

Figure 2. (a) Physical Image of the IEST Battery Consistency Screening Instrument (BCS6000); (b) Manual Fixture; (c) Automatic Battery Feeding Equipment

6. Case Study: Screening 340 Ah Energy Storage Cells

The effectiveness of this approach is demonstrated by screening ten 340 Ah energy storage cells. While the high-frequency region of the EIS spectra for all ten cells appeared nearly identical (Figure 3), the mid- and low-frequency regions revealed significant differences in the charge transfer and ion diffusion processes for Cell #8 and Cell #10.

By applying a neural network algorithm to the full EIS dataset, a discreteness distribution curve was generated. This data-driven approach allows engineers to set pass/fail thresholds or create finely-traded consistency groups, effectively identifying and segregating outliers to ensure highly uniform cell populations in a pack.

Figure 3. EIS Comparison of Ten 340Ah Energy Storage Batteries and the Distribution Curve of Dispersion Analyzed by Neural Network Algorithm

7. Conclusion

Implementing EIS-based consistency screening at the point of shipment or pre-assembly provides a deeper, more nuanced assessment of cell quality than traditional methods. The BCS6000 system enables this capability in an industrial setting, facilitating the creation of more reliable, longer-lasting, and safer battery modules and packs by minimizing the risk posed by “weak link” cells.

8. References

[1] W.X. Hu, Y.F. Peng, Y.M. Wei and Y. Yang, Application of Electrochemical Impedance Spectroscopy to Degradation and Aging Research of Lithium-Ion Batteries. J. Phys. Chem. C 127 (2023) 4465-4495.

[2] T. Osaka, D. Mukoyama and H. Nara, Review-Development of Diagnostic Process for Commercially Available Batteries, Especially Lithium Ion Battery, by Electrochemical Impedance Spectroscopy. J. Electrochem. Soc. 162 (2015) 2529-2537.

[3] A. Battistel and F.L. Mantia, On the Physical Definition of Dynamic Impedance: How to Design an Optimal Strategy for Data Extraction. Electrochim. Acta 304 (2019) 513-520.