Accurately predicting the lifespan of lithium-ion batteries is paramount for ensuring the operational safety and reliability of the myriad devices and systems that depend on them, from electric vehicles to grid-scale energy storage. Within the framework of data-driven or hybrid prognostic models, the selection of effective indirect health indicators (HIs) is a critical step. These are parameters, more readily measurable than direct capacity, that correlate strongly with the battery’s state of health (SOH) and remaining useful life (RUL). Commonly used indirect parameters include features extracted from voltage and current curves during operation, as well as impedance values obtained from electrochemical impedance spectroscopy (EIS). While parameters like constant-current charge time or incremental capacity curve features have shown empirical correlation with capacity fade, their selection often lacks a firm foundation in the fundamental degradation mechanisms of the lithium-ion battery. Impedance parameters, which reflect the dynamic electrochemical processes within the cell, are theoretically promising candidates. However, the intrinsic relationship between specific impedance components—such as charge transfer resistance and solid electrolyte interphase (SEI) film resistance—and the underlying mechanical-electrochemical coupling degradation processes remains insufficiently explored. This study aims to bridge this gap by designing and executing an integrated experimental protocol to elucidate the connection between measurable impedance parameters and the core degradation mechanics, thereby providing a mechanistic rationale for their use in life prediction.
The performance degradation of a lithium-ion battery is inherently a multi-physics process. During repeated charge-discharge cycles, lithium ions intercalate into and de-intercalate from the active material particles in the electrodes. This cyclic insertion/extraction induces significant mechanical stress due to volume changes, leading to fatigue damage in the electrode material. This damage manifests as particle cracking, fracture, and loss of electrical contact. Concurrently, complex electrochemical side reactions occur, most notably the continuous growth and reformation of the SEI layer on the anode surface. These mechanical and electrochemical processes are not independent; they are strongly coupled. Mechanical damage alters the electrode’s microstructure, affecting ionic and electronic transport paths, which in turn influences electrochemical kinetics and exacerbates side reactions. Therefore, an ideal indirect parameter for life prediction should encapsulate information from both the mechanical degradation and the electrochemical state of the lithium-ion battery.
This investigation centers on the hypothesis that key electrochemical impedance parameters are sensitive to the progressive mechanical damage within the electrode. To test this, we designed a synchronized experimental approach that combines long-term cycling, periodic electrochemical impedance spectroscopy (EIS), and in-situ acoustic emission (AE) monitoring. The EIS provides quantitative metrics on the electrochemical interface conditions, while the AE technique offers a direct, real-time measure of mechanical damage events, such as crack initiation and propagation within the electrode. By correlating the evolution of impedance parameters with the accumulation of acoustic emission activity, we can establish a direct, quantitative link between the electrochemical “symptoms” and the mechanical “root cause” of degradation in the lithium-ion battery.
Experimental Methodology and Integrated Setup
The core of this study is an integrated experimental protocol designed to simultaneously track electrical performance, electrochemical impedance, and mechanical degradation signatures throughout the life of a lithium-ion battery. CR2032-type coin cells were fabricated in an argon-filled glovebox to ensure strict control over moisture and oxygen levels. Two different anode materials were investigated to assess the generality of the findings: a conventional graphite electrode and a silicon-carbon composite electrode, the latter being of high interest due to its higher capacity but more pronounced volume expansion issues. The electrolyte formulation was carefully chosen to be compatible with both electrode types. The detailed assembly and testing conditions are summarized below.
| Test Category | Primary Objective | Key Parameters Measured | Testing Conditions / Protocol | Variables Tested |
|---|---|---|---|---|
| Cell Fabrication | Prepare consistent test samples | N/A | Assembly in Ar glovebox (H2O, O2 < 0.1 ppm). Working Electrode: Graphite or Si-C. Counter Electrode: Li metal. Specific electrolyte formulation. | Anode Material: Graphite, Silicon-Carbon. |
| Galvanostatic Cycling | Induce aging and measure capacity fade | Cycle number, Discharge Capacity, Voltage profiles | Voltage window: 0.01 V – 2.0 V. Sequence: Rest, CC Discharge, Rest, CC Charge, CV Charge until current decays. | C-rate: C/2, 1C; Material: Graphite, Si-C. |
| Electrochemical Impedance Spectroscopy (EIS) | Probe electrochemical interface evolution | Impedance spectrum, R0, RSEI, RCT | Performed every 5 cycles. Frequency: 0.01 Hz to 500 kHz. Amplitude: 5 mV. Fitted with equivalent circuit model. | C-rate: C/2, 1C; Material: Graphite, Si-C. |
| In-situ Acoustic Emission (AE) | Monitor mechanical damage events in real-time | AE hit count, signal amplitude, energy | Continuous monitoring during cycling. Sensor coupled to cell. Acquisition: 0-150 kHz band, threshold: 27 dB to isolate burst-type signals. | C-rate: C/2, 1C; Material: Graphite, Si-C. |
The galvanostatic cycling protocol followed a precise sequence to ensure stable conditions for each measurement: initial rest, constant-current (CC) discharge to the lower cutoff voltage, rest, CC charge to the upper cutoff voltage, and finally a constant-voltage (CV) hold until the current dropped to a defined cutoff, ensuring a full state of charge. This sequence was repeated for hundreds of cycles. Crucially, every fifth cycle, the cycling was paused at a defined state of charge (typically 50% SOC), and an EIS measurement was conducted. Simultaneously, throughout the entire cycling test, an acoustic emission sensor was acoustically coupled to the exterior of the cell fixture to capture the stress waves generated by internal mechanical events.
Theoretical Background: Linking Impedance, Damage, and Capacity
To interpret the experimental results, it is essential to understand the theoretical connection between the measured impedance parameters and the physical processes inside a degrading lithium-ion battery. A typical Nyquist plot from EIS of a lithium-ion battery exhibits several features, each corresponding to a specific electrochemical process. An equivalent circuit model, often used for analysis, is shown below alongside the conceptual degradation links.
The key impedance parameters of interest are:
$$ R_{\Omega} $$: The ohmic resistance, representing ionic conduction in the electrolyte and electronic conduction in cables and contacts.
$$ R_{SEI} $$: The resistance associated with Li+ ion migration through the passivating Solid Electrolyte Interphase (SEI) layer on the anode surface.
$$ R_{CT} $$: The charge transfer resistance, representing the kinetic barrier for the electrochemical reaction (Li+ + e– $\rightleftharpoons$ Li) at the electrode/electrolyte interface.
$$ Z_{W} $$: The Warburg impedance, related to solid-state diffusion of lithium within the active material particles.
During cycling, mechanical damage accumulates in the electrode due to cyclic stress. This damage has a direct impact on $$ R_{CT} $$ and $$ R_{SEI} $$:
- Effect on $$ R_{CT} $$: Cracking and fracture of active particles create new, electrochemically active surfaces and, more importantly, can break the conductive network. This increases the tortuosity of electronic pathways and may isolate some active material, effectively reducing the active surface area available for charge transfer. According to fundamental electrode kinetics, the charge transfer resistance is inversely proportional to the electroactive surface area. Therefore, mechanical damage directly causes an increase in $$ R_{CT} $$.
$$ R_{CT} \propto \frac{1}{A_{eff}} $$
where $$ A_{eff} $$ is the effective electroactive surface area, which decreases with mechanical degradation. - Effect on $$ R_{SEI} $$: The SEI layer is brittle. When the underlying electrode particle cracks or undergoes significant volume change, the existing SEI layer fractures. This exposes fresh electrode material to the electrolyte, triggering further electrolyte reduction and the formation of new SEI to cover the cracked surfaces. This process leads to a continuous, often non-linear, growth in the thickness and complexity of the SEI layer, increasing the resistance for Li+ transport through it, thereby increasing $$ R_{SEI} $$.
Both the increase in $$ R_{CT} $$ (making reaction kinetics slower) and the increase in $$ R_{SEI} $$ (increasing ionic transport resistance) contribute directly to capacity fade. Higher resistances lead to increased polarization, causing the cell to hit voltage cutoffs prematurely during discharge, thus reducing the accessible capacity. Furthermore, the consumption of cyclable lithium ions in forming new SEI and the loss of electrically isolated active material due to cracking are direct causes of irreversible capacity loss. This establishes the fundamental, qualitative degradation chain: Cyclic loading $\rightarrow$ Mechanical Damage $\rightarrow$ Increased $$ R_{CT} $$ & $$ R_{SEI} $$ $\rightarrow$ Capacity Fade.
Experimental Results: Evolution of Capacity, Impedance, and Acoustic Emission
The integrated experiment yielded a comprehensive dataset tracking the state of health (SOH), impedance parameters, and acoustic emission activity as functions of cycle number. The SOH is defined as the ratio of the current discharge capacity to the initial maximum capacity:
$$ SOH(n) = \frac{Q_{disch}(n)}{Q_{max}} \times 100\% $$
where `n` is the cycle number and $$ Q_{max} $$ is the maximum observed discharge capacity, typically from the initial cycles.
For a silicon-carbon composite anode cycled at a C/2 rate, the evolution trends were clearly captured. The SOH showed a characteristic profile: an initial stable period followed by a non-linear, accelerating fade. Concurrently, the electrochemical impedance parameters $$ R_{CT} $$ and $$ R_{SEI} $$, obtained from fitting the EIS spectra, exhibited a mirror-like non-linear increase. The ohmic resistance $$ R_{\Omega} $$ showed a much more gradual and linear increase. The synchronized acoustic emission monitoring provided a direct measure of mechanical activity. The cumulative AE hit count, representing the total number of detected damage events (primarily from cracking), also followed a distinct non-linear growth trend, visually similar to the trajectories of $$ R_{CT} $$ and $$ R_{SEI} $$.
To quantitatively assess the relationship between the electrochemical impedance parameters and the mechanical damage proxy (AE data), the evolution curves for $$ R_{CT} $$, $$ R_{SEI} $$, and cumulative AE hits were normalized to a [0,1] scale for comparison across different units. The normalization was performed as:
$$ X_{norm}(n) = \frac{X(n) – X_{min}}{X_{max} – X_{min}} $$
where $$ X(n) $$ is the parameter value (e.g., $$ R_{CT} $$ or AE hits) at cycle `n`, and $$ X_{max} $$ and $$ X_{min} $$ are its maximum and minimum values over the entire test period, respectively.
The visual alignment of the normalized curves suggested a strong correlation. To quantify this, the Pearson correlation coefficient `r` was calculated between the normalized impedance parameters and the normalized cumulative AE hits. For the silicon-carbon cell at C/2, the correlation coefficients were remarkably high:
$$ r(R_{CT}, AE_{hits}) \approx 0.962 $$
$$ r(R_{SEI}, AE_{hits}) \approx 0.939 $$
These high values (close to 1) provide strong quantitative evidence that the increases in $$ R_{CT} $$ and $$ R_{SEI} $$ are tightly linked to the progression of mechanical damage within the lithium-ion battery.
Validation Across Different Conditions and Materials
To demonstrate the robustness and general applicability of the observed relationship, the experimental matrix was expanded to include different stress conditions (charge/discharge C-rates) and different electrode materials. Tests were conducted on graphite and silicon-carbon anodes at both C/2 and 1C rates. Multiple cells were tested for each condition to ensure reproducibility.
The results consistently showed the same pattern: regardless of the material or C-rate, the normalized cumulative AE hits and the normalized $$ R_{CT} $$ and $$ R_{SEI} $$ evolved along strikingly similar non-linear trajectories, while the SOH evolved in the opposite direction. As expected, cells cycled at the higher 1C rate generally exhibited faster capacity fade and a more rapid increase in both impedance and AE activity compared to those cycled at C/2, due to the higher mechanical and electrochemical stresses.
The Pearson correlation analysis was applied to all tested cells. The results are compelling. For every individual cell, across all four test conditions (Graphite-C/2, Graphite-1C, Si-C-C/2, Si-C-1C), the correlation coefficients between the impedance parameters and the AE cumulative hits were consistently above 0.90, with many exceeding 0.95. A summary of the findings is presented below:
| Cell Group | Anode Material | C-rate | Avg. $$ r(R_{CT}, AE_{hits}) $$ | Avg. $$ r(R_{SEI}, AE_{hits}) $$ | Observation |
|---|---|---|---|---|---|
| Group 1 | Graphite | C/2 | 0.967 | 0.958 | Very strong correlation in both standard graphite systems. |
| Group 2 | Graphite | 1C | 0.981 | 0.974 | Even stronger correlation under higher-rate stress. |
| Group 3 | Silicon-Carbon | C/2 | 0.961 | 0.939 | Strong correlation persists in high-volume-change material. |
| Group 4 | Silicon-Carbon | 1C | 0.984 | 0.976 | Strongest correlations observed, aligning with severe damage. |
The slightly higher correlation coefficients observed at the 1C rate, especially for the silicon-carbon lithium-ion battery, are particularly telling. They indicate that under conditions that promote more severe and rapid mechanical degradation (higher currents, larger volume changes), the connection between the measurable impedance rise and the underlying mechanical damage becomes even more pronounced and deterministic.
Discussion: Impedance as a Dual-Purpose Health Indicator
The experimental evidence solidifies the role of specific impedance parameters, namely the charge transfer resistance $$ R_{CT} $$ and the SEI film resistance $$ R_{SEI} $$, as more than mere electrochemical metrics. They emerge as dual-purpose health indicators that encapsulate critical information about both the electrochemical interface condition and the mechanical integrity of the electrode structure within the lithium-ion battery.
The high-fidelity correlation with acoustic emission data, a direct signature of mechanical events, confirms that the evolution of $$ R_{CT} $$ and $$ R_{SEI} $$ is fundamentally governed by the progression of electrode damage. This provides the much-needed mechanistic justification for their selection as indirect parameters in data-driven prognostic models. When a model uses $$ R_{CT} $$ or $$ R_{SEI} $$ as an input feature to predict SOH or RUL, it is implicitly leveraging a signal that is causally connected to a primary aging mechanism (mechanical fatigue and interface degradation), rather than relying on a purely empirical statistical correlation.
Furthermore, this understanding explains the commonly observed non-linear evolution of both capacity and impedance in aging lithium-ion batteries. The mechanical damage processes—crack initiation, propagation, and network disconnection—are themselves non-linear and often exhibit an accelerating trend. Since the impedance parameters are so sensitive to this damage, they naturally inherit this non-linear trajectory. This makes them excellent candidates for predicting the often difficult-to-forecast “knee-point” or rapid fade phase in a battery’s life, a significant challenge in prognostic algorithms.
It is important to note that while $$ R_{\Omega} $$ also increases with aging, its correlation with mechanical damage is weaker, as its evolution is more tied to factors like electrolyte depletion or contact loss that may follow different kinetics. Thus, $$ R_{CT} $$ and $$ R_{SEI} $$ are identified as the most salient impedance-based features for life prediction rooted in the core mechanical-electrochemical coupling failure mode.
Conclusion
This experimental investigation successfully delineates the intrinsic connection between measurable electrochemical impedance parameters and the mechanical degradation processes within a lithium-ion battery. Through a synchronized protocol of long-term cycling, electrochemical impedance spectroscopy, and in-situ acoustic emission monitoring, we have demonstrated that the charge transfer resistance $$ R_{CT} $$ and the SEI film resistance $$ R_{SEI} $$ evolve in strong quantitative correlation with the accumulation of mechanical damage events. The Pearson correlation coefficients consistently exceeded 0.9 across various cycling rates and for both graphite and silicon-carbon electrode materials, validating the robustness of this relationship.
The findings establish a clear, mechanistic degradation pathway: cyclic operation induces mechanical damage in electrode materials; this damage directly increases the resistances associated with charge transfer and ion transport through the SEI; these increased resistances, combined with the active material loss from damage, collectively drive the non-linear fade in the capacity of the lithium-ion battery. Consequently, $$ R_{CT} $$ and $$ R_{SEI} $$ are validated as highly effective indirect health indicators. They serve as dual-purpose parameters that inherently reflect the coupled mechanical-electrochemical state of the cell, providing a scientifically grounded basis for their integration into advanced data-driven and hybrid models for accurate state-of-health estimation and remaining-useful-life prediction of lithium-ion batteries. Future work will focus on translating these fundamental correlations into practical prognostic algorithms and exploring their applicability under more complex, real-world loading profiles.