Entering Electrochemistry | Using Electrochemical Impedance Spectroscopy(EIS) to Diagnose the “Causes” of Failure of Lithium Batteries

1. Background

In investigative reports on energy storage station explosions or the charred remnants of new energy vehicle fires, “battery failure” is consistently identified as a core culprit. As a complex electrochemical system, lithium-ion battery failure often originates from multiscale, multiphysics evolution: structural collapse of electrode materials, lithium dendrite growth due to electrolyte decomposition, abnormal thickening of the SEI (Solid Electrolyte Interphase) film, and other microscopic changes. These ultimately manifest macroscopically as capacity fade, internal resistance surge, or thermal runaway.

Traditional failure analysis relies on disassembly testing and capacity calibration but faces two critical limitations: (1) Destructive testing prevents sample reuse, and (2) Static parameters fail to reflect dynamic degradation processes. In contrast, Electrochemical Impedance Spectroscopy (EIS), with its non-destructive testing and multi-frequency resolution capabilities, is emerging as a “diagnostic lens” for failure analysis.

2. EIS: A Multilayered “CT Scan” for Battery Interiors

By applying low-amplitude current/voltage excitation signals across a wide frequency range (MHz to mHz), EIS resolves electrochemical processes with distinct time constants, akin to a “layered CT scan”:

• High-frequency region (10,000–100 Hz): Captures contact impedance at current collector/electrode interfaces, reflecting mechanical defects such as tab welding or electrode compaction issues.
• Mid-frequency region (1,000–10 Hz): Reveals charge transfer impedance, diagnosing reaction kinetics degradation in active materials.
• Low-frequency region (10–0.01 Hz): Tracks Warburg impedance, exposing lithium-ion diffusion blockage within the electrode bulk.

Figure 1. Electrochemical processes with different time constants inside a battery

Through equivalent circuit modeling, abstract semicircle curves in Nyquist plots are quantified into parameters like Rsei (SEI film resistance) and Rct (charge transfer resistance), enabling failure localization. Techniques such as Distribution of Relaxation Times (DRT) further enable rapid visualization. For example, a shortened 45° low-frequency slope in a cycled power battery (see figure) correlated with DRT analysis revealed a doubled lithium-ion diffusion impedance. Post-disassembly, this was traced to graphite layer collapse caused by electrolyte corrosion.

Figure 2. Distribution of Relaxation Times, DRT Analysis

3. From Lab to Industry: Overcoming Barriers in Failure Warning

Despite extensive academic research (over 10,000 papers), Electrochemical Impedance Spectroscopy(EIS) industrial adoption has long been hindered by:

• Equipment limitations: Traditional potentiostats struggle with high-current testing for large-capacity batteries (>100 Ah) and incur high costs
• Data analysis threshold: Impedance spectra with multiple time constants require professional equivalent circuit modeling capabilities, which is difficult for production line engineers to quickly interpret.

The industrial-grade EIS test equipment (BIT6000 series) launched by IEST has achieved technological breakthroughs through three major innovations:

• High current technology: Increase the current test range to meet the EIS test of large-capacity batteries (>100Ah);
• Composite excitation technology: Use multi-frequency superposition technology to compress the original test time by more than half;
• Intelligent data processing technology: Combined with big data engine and machine learning algorithm, it can realize rapid data analysis, locate failure causes, automatic classification, etc., which is more suitable for production line engineers

4. New paradigm of failure analysis: extended application of EIS technology matrix

With the advancement of science and technology, EIS technology is also deeply integrated with multi-dimensional detection methods, gradually moving towards the “EIS+” model.

• EIS+in-situ XRD: This combined technology can simultaneously observe the correlation between the phase change impedance and crystal structure evolution of NMC positive electrode materials;
• EIS+ultrasonic scanning: locate the hot spot of lithium deposition inside lithium-ion batteries through spatial mapping of acoustic impedance and electrical impedance;
• EIS+AI technology: The AI ​​model trained based on a million-level impedance spectrum database can predict the remaining life (SOH) of the battery with an error of <3%

These technology combinations are building a “digital twin” system for battery failure analysis, allowing researchers to simulate the failure evolution path under different stress conditions in virtual space, and provide a theoretical foundation for the next generation of battery design that “failure prevention is more important than post-analysis”.

5. Summary

From precision instruments in the laboratory to “industrialized” intelligent testing equipment, EIS technology is completing the transformation from a scientific research tool to an industrial tool. When we analyze the physical meaning behind each semicircle on the Nyquist diagram, we are actually deciphering the “fingerprint code” of lithium battery failure. This deep decoding of the battery’s “vital signs” is not only related to the company’s quality control, but also a key technical barrier to protect the safety bottom line of the new energy industry!