Lithium Battery Failure Analysis: A 7-Dimensional Characterization Guide for Root Cause Identification

Abstract

1. The Challenge: Why Lithium Battery Failure Analysis Is Hard

Lithium-ion battery ageing involves coupled failure modes spanning materials, interfacial electrochemistry, electrode structure, and manufacturing processes. A single performance anomaly — capacity drop, impedance spike, or swelling — is rarely traceable to one isolated cause. The same symptom can originate from lithium plating, SEI thickening, particle cracking, or coating delamination, making root cause identification a persistent industry bottleneck.

Extensive review literature has consolidated battery aging into three core degradation modes: loss of cyclable lithium inventory (LLI), loss of active material (LAM) at either electrode, and internal resistance growth. These modes manifest macroscopically as capacity fade, DCIR rise, gas evolution, lithium plating, and — in extreme cases — thermal runaway. However, because these mechanisms are coupled and often occur simultaneously, assigning a single root cause requires a structured, multi-dimensional analytical framework.

 Figure 1. Schematic of lithium-ion battery ageing mechanisms, showing the interlinked pathways of active lithium loss (LLI), active material loss (LAM), and impedance growth that drive capacity fade.

2. A Systematic Failure Analysis Workflow

Effective failure analysis follows three core principles:

• Non-destructive first, then destructive: Visual inspection, CT scanning, ultrasound, and in-situ electrochemical testing screen for gross defects before disassembly, avoiding secondary damage.
• Reference baseline comparison: A healthy cell from the same production batch is tested in parallel to distinguish systematic defects from random outliers.
• Localize before explaining: Narrow the fault zone progressively, validate the root cause through replication experiments, and derive actionable optimization measures.

IEST Instrument‘s seven-dimensional characterization portfolio provides the full toolkit to execute this workflow — covering cell-level mechanical behavior, interfacial electrochemical response, electrode-level electronic/ionic transport, and particle-level mechanical integrity.

Lithium Battery Failure Analysis Flowchart:

3. In-Situ Swelling Analysis: Quantifying Cell Deformation and Aging Correlation

Using an in-situ swelling analyzer(SWE Series), a 2 Ah NCM/Si pouch cell at 100% SOH shows stable swelling amplitude and excellent springback during cycling. By contrast, the same cell aged to 80% SOH exhibits significantly larger irreversible deformation and delayed discharge rebound. Over long-term cycling, this cumulative irreversible deformation progressively damages the electrode structure, tears the SEI film, and accelerates active material loss and lithium inventory depletion. These data directly support cell structure optimization and cycle-life improvement.

 Figure 2. Differences in Swelling Behavior of Aged Cells.

4. EIS Impedance Decomposition: Pinpointing Interfacial Failure Modes

EIS impedance decomposition separates the cell’s impedance spectrum into four distinct components:

• R_s (ohmic resistance): Abnormal values indicate tab welding defects, busbar contact issues, or assembly faults.
• R_SEI (SEI resistance): Elevation points to poor formation protocols, electrode folding/creasing, or non-uniform SEI growth.
• R_ct (charge-transfer resistance): Increase signals sluggish electrochemical kinetics — the hallmark of lithium plating, low-temperature degradation, or electrolyte dry-out.
• R_diff (diffusion impedance): Abnormalities reflect process defects such as excessive compaction density or insufficient electrolyte wetting.

This “one dataset, one fault class” capability enables rapid diagnosis of whether the root cause is materials, formation, or mechanical assembly.

Figure 3. EIS impedance decomposition and fault correlation — each impedance component (\(R_s\), \(R_{SEI}\), \(R_{ct}\), \(R_{diff}\)) links to a distinct failure mechanism, enabling targeted root cause identification.

5. Electrode Resistance & Separator Ionic Conductivity: Isolating Material-Intrinsic Degradation

Electrode resistance measurements on fresh vs aged electrodes reveal a clear pattern: fresh dry electrodes show the lowest resistance and best uniformity. After aging, both cathode and anode resistances increase significantly — with the anode degrading far more severely than the cathode — and the coefficient of variation (COV) rises markedly, directly degrading cycling stability.

In parallel, separator ionic conductivity testing quantifies ion-transport efficiency across different separator types, identifying pore blockage, poor wetting, and material defects that contribute to poor rate capability and internal resistance fluctuations.

Figure 4. Resistance difference between cathode and anode electrode sheets.

Figure 5. Ionic conductivity comparison of different separators.

6. Electrode Cohesion Testing: Diagnosing Coating Delamination

Electrode cohesion testing quantifies the internal strength of the electrode coating under different binder formulations, coating thicknesses, calendering conditions, and aging states. The method clearly distinguishes structural differences between cathode and anode electrodes, enabling diagnosis of coating delamination, particle shedding, and binder failure caused by improper binder selection, non-uniform coating thickness, or defective calendering. This data directly supports optimization of coating, calendering, and formulation processes.

Figure 6. Cohesion data comparison between cathode and anode electrode sheets.

7. Single-Particle Mechanical Testing: Probing Active-Material Aging

Single-particle crushing tests on high‑nickel NCM cathodes reveal a direct correlation between cycle number and particle strength: after 400 cycles, aged particles exhibit significantly lower crushing strength and fracture more easily. This explains the sudden capacity drop and continuous impedance rise observed in long‑cycle cells. The technique provides the core data needed for cathode material modification and system optimization.

Figure 7. Correlation between cell SOH and single-particle crushing strength.

8. DCR Full-Component Decomposition: Quantifying Resistance Contribution by Component

DCR decomposition independently quantifies the resistance contribution of six components: cathode, anode, separator, aluminum current collector, copper current collector, and tab welding points. Data show that the cathode contributes 41.22% of total cell resistance — the dominant factor — followed by the anode at 35.82%. This component-level breakdown pinpoints whether high internal resistance originates from intrinsic material properties, production assembly, or welding defects, solving the industry pain point of “knowing that resistance is high, but not knowing where.”

Figure 8. DCR component contribution breakdown.

9. Reverse Coin Cell Validation: Cross-Validating the Root Cause

To eliminate coincidental errors from single measurements, IEST’s reverse Automatic Coin Cell Assembly System(CAAS Series) punches electrodes from the failed cell and reassembles them with standard lithium foil, separator, and electrolyte. CT imaging verifies electrode flatness and assembly quality. By replicating the failed cell’s material system and structure, the method reproduces the failure and, together with healthy-cell reference testing, rules out assembly errors, environmental interference, and equipment faults — confirming whether the root cause is material-, process-, or structure-related.

Figure 9. IEST Coin Cell Automatic Assembly System(CAAS Series).

10. Summary

From the perspective of lithium-ion battery ageing mechanisms, irreversible performance degradation in cells is primarily driven by the coupling of three core degradation modes:

• Loss of Lithium Inventory (LLI),
• Loss of Active Material (LAM) at the cathode/anode electrodes
• Increase in internal resistance across the entire system.

These three degradation mechanisms do not occur independently, particle fracture accelerates SEI growth, which consumes active lithium, while lithium loss alters the operating potential of the electrode and accelerates material structural deterioration, creating a mutually reinforcing vicious cycle.

IEST Instryment comprehensive seven-dimensional characterization solution for lithium-ion batteries achieves multi-scale, full-coverage testing—ranging from macroscopic cell deformation and impedance separation to electrode interfaces and single-particle micromechanics. It effectively differentiates root causes such as lithium loss, active material failure, and process/assembly defects, solving the industry pain points of difficult failure traceability and lack of direction in rectification. This provides actionable technical support for battery R&D iteration, mass-production quality improvement, and safety risk prevention and control.

11. References

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[3] Li D, Danilov D, Xie J, et al. Degradation Mechanisms of C₆/LiFePO₄ Batteries: Experimental Analyses of Calendar Aging[J]. Electrochimica Acta,2016,196:106‑116.