1. Background Introduction

In new energy vehicles or energy storage stations, lithium-ion batteries are often used in modular or battery pack (Pack) configurations, where cells are connected in parallel and series. If any individual cells have performance defects or safety risks, it can lead to the failure of the entire module or battery pack, potentially resulting in a phenomenon known as the “barrel effect”—where the performance of the entire system is limited by its weakest component. Therefore, ensuring the consistency of the cells used in these configurations is critical. By consistency screening before the batteries are shipped or assembled into modules and packs, the effective utilization of batteries can be improved, and the cycle life and safety of new energy vehicles or energy storage stations can be significantly enhanced.

2. Current Situation

Before lithium-ion batteries are shipped, they are typically graded based on parameters such as open-circuit voltage (OCV), capacity, 1000Hz ACIR, and K-value. However, these parameters primarily fall within the domain of electronic resistance testing, with little to no evaluation of ionic resistance. Furthermore, there is often no additional consistency evaluation or classification before assembling the batteries into modules or packs, which increases the likelihood of “weak link” batteries appearing and subsequently affecting the lifespan and performance of the entire module or pack.

3. Using EIS Technology for Consistency Screening of Lithium-Ion Batteries

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]

Since Electrochemical Impedance Spectroscopy is a multi-frequency analysis, it not only includes the high-frequency electronic conductivity but also covers the mid-to-low-frequency ionic processes. Applying this technique in battery screening before shipping or assembly can enable more refined consistency screening, reducing the likelihood of weak link batteries. Electrochemical Impedance Spectroscopy (EIS) testing typically requires the use of expensive and precise equipment such as an “electrochemical workstation,” which limits its application to academic research and research institutes. Industrial applications of EIS are rare. Additionally, as battery capacity increases and internal resistance decreases, conventional electrochemical workstations struggle to meet the EIS testing needs of such lithium batteries, necessitating the use of large-scale current amplifiers, further increasing testing costs. To address this issue, IEST has innovatively launched an “industrial-grade” EIS rapid testing device—the Battery Consistency Screening Instrument (BCS6000), as shown in Figure 2(a). This device can be used with manual fixtures (Figure 2(b)) or automatic feeding equipment (Figure 2(c)) to quickly conduct EIS testing on high-capacity batteries used in power or energy storage applications. It employs neural network algorithms to assess the consistency of large batches of batteries and to group them precisely, ensuring a high level of consistency within each group. Figure 3 shows a comparison of the EIS results for ten 340Ah energy storage batteries. If only the high-frequency region of the Electrochemical Impedance Spectroscopy (EIS) is considered, the electronic conductivity of these ten batteries appears almost identical. However, when examining the mid-to-low-frequency region of the Electrochemical Impedance Spectroscopy (EIS) , significant differences in charge transfer and ion diffusion processes are observed for Cell #8 and Cell #10 compared to the other cells. Neural network algorithm analysis also provides a distribution curve of the dispersion for these ten batteries. By setting the appropriate specification line, batteries with poor consistency can be excluded, or they can be grouped by gradient according to dispersion, ensuring the consistency of the batteries within each group.

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

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

4. 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.