Entering Electrochemistry | GITT Analyzes The Diffusion Kinetics Of Lithium Batteries

1. Preface

A lithium-ion battery operates as a “rocking-chair” system, where lithium ions shuttle between the cathode and anode through the electrolyte (Figure 1). During charging, lithium ions migrate from the cathode to the anode, while electrons flow through the external circuit. In discharging, the opposite occurs, generating usable electric power. Since lithium-ion transport directly impacts charging/discharging efficiency, cycle life, and temperature performance, accurate GITT test analysis of ion diffusion is essential for battery optimization.

Figure 1. Schematic diagram of the working principle of lithium-ion battery

2. What is the GITT Test in Battery Research?

The Galvanostatic Intermittent Titration Technique (GITT) is a widely used electrochemical measurement method to evaluate the lithium-ion diffusion coefficient in battery electrode materials. As a core GITT battery characterization tool, it provides insights into how lithium ions move through electrodes by analyzing the relationship between voltage and time during intermittent current pulses.^{[1] [2]} 

4. Basic Overview of the GITT Test

The overall GITT process consists of a series of “pulse-constant current-relaxation” processes (Figure 2). A set of “pulse-constant current-relaxation” process is to charge/discharge the battery by applying a constant current for a certain period of time, and then disconnecting the current and recording the voltage change of the whole process, the key of the test is the constant current and the accurate voltage. In the relaxation phase after disconnecting the current, it is necessary to let the lithium ions diffuse fully inside the active material, and the diffusion coefficient is further calculated by the relationship between voltage and time. In order to satisfy the assumption of the GITT method that “the diffusion process mainly occurs in the surface layer of the solid-phase material”, it is necessary to make certain limitations on the test conditions:

Key conditions for GITT analysis:

• The pulse time t must be short enough to satisfy the condition \( \tau \ll L^2/D \), where L is electrode thickness and D is diffusion coefficient.

• The relaxation time must be long enough to allow lithium ions to reach equilibrium within the active material.

Through this approach, the GITT battery method effectively captures the voltage response linked to ion transport kinetics.

Figure 2. (a) GITT test curve and (b) localized zoomed-in schematic

5. Core Formula in GITT Analysis

The GITT test data allows for further calculation of the corresponding diffusion coefficients with the following equations:

Through GITT test data, the lithium-ion diffusion coefficient can be calculated using the following core formula:

Validity Condition: \( \tau \ll L^2/D \), where \( \tau \) is the current pulse duration, \( L \) is the electrode thickness, and \( D \) is the diffusion coefficient to be determined. This condition is essential to ensure the test conforms to the “semi-infinite diffusion” model.

• D = lithium-ion diffusion coefficient

• mB = active material mass

• Vm = molar volume of electrode

• MB = relative molecular mass

• S = electrode surface area

•  \( \tau \) = relaxation time

• \( \Delta E_t \) = voltage change during current pulse

• \( \Delta E_s \) = voltage change during relaxation phase

By substituting \( \Delta E_s \) , \( \Delta E_t \) , and material constants into the equation, precise values for D are obtained.

In general, the voltage changes obtained during the test include not only the surface diffusion values, but also the voltage changes that reflect the SOC changes. However, the challenge in GITT analysis lies in ensuring accuracy: shorter pulse times improve theoretical precision but reduce the magnitude of \( \Delta E_s \) , requiring highly accurate instruments. For example, the IEST analyzer achieves 0.01% measurement accuracy across 8 testing channels, enabling reliable GITT battery characterization.

6. Application Cases

6.1 Lithium-ion Diffusion at Different SOC States

The authors investigated the changes of Li-ion diffusion coefficient during the charging and discharging process of LiNi_0.8Co_0.1Mn_0.1O₂₍NCM811) by means of GITT test^[3]. The value of lithium ion diffusion coefficient D_Li+ varies significantly in different SOC states. The values of D_Li+ were 10^-8~10^-9cm² s^-1 during charging and 10^-7~10^-11cm² s^-1 during discharging. At the beginning of charging, D_Li+ increased with the release of lithium ions, reached a maximum value at a lithium content of~0.5, and then gradually decreased. The diffusion coefficient decreases rapidly when the lithium content is lower than 0.2. In addition, during the discharge process, D_Li+ was extremely high at the beginning; after that, the value decreased slightly and remained at a high level with the insertion of lithium ions. When the Li-ion embedding content reaches 0.8, the D_Li+ drops sharply by three orders of magnitude. This very low Li-ion embedding kinetics explains the capacity loss in the first cycle.

Figure 3. First turn GITT curve and Li ion diffusion coefficient of NCM811

6.2 Effect of material modification on ion diffusion coefficient

The authors introduced high entropy elements (Cr, Mn, Fe, Zn, Al) into the NASICON structure Na₃V₂(PO₄)₃ (NVP) to obtain NNa₃V_1.8(CrMnFeZnAl)_0.2(PO₄)₃(HE-NVP-0.2) in order to realize the material’s crystal structure tuning and diffusion ability [4]. As shown in Figures 4a and 4b, the GITT battery study results show that the HE-NVP-0.2 electrode exhibits better Na ion diffusion kinetics after the introduction of high entropy elements. After NVP and HE-NVP-0.2 were assembled into half-cells and the rate performance was tested, it was found that the rate performance of HE-NVP-0.2 was significantly better than that of the NVP sample (Figure 4c).

Figure 4. (a) GITT curves and corresponding Na ion diffusion coefficients for NVP and (b) HE-NVP-0.2

6.3 Why Choose GITT over EIS or CV?

While CV provides a snapshot of redox peaks, GITT analysis provides a continuous map of diffusion kinetics across the entire SOC range. Unlike EIS, which is highly dependent on equivalent circuit modeling, GITT offers a more direct measurement of ion transport within the solid-phase lattice.

7. Implementing GITT: The Role of High-Precision Instrumentation

Translating the theoretical framework of GITT analysis into reliable, publication-grade data hinges on the precision and stability of the testing equipment. The core challenge in performing a GITT test lies in accurately measuring the subtle voltage changes—\( \Delta E_s \) (steady-state voltage change after relaxation) and ΔEt (voltage change during the current pulse)—which are fundamental to the GITT diffusion coefficient calculation.

The accuracy of these measurements is critically dependent on two factors: the exceptional stability of the galvanostatic (constant current) source and the microvolt-level resolution of voltage measurement. Any drift or noise can significantly distort these small signals, especially when using short pulse times (\( \tau \)) to satisfy the fundamental assumption \( \tau \ll L^2/D \) for semi-infinite linear diffusion.

To ensure valid data, the instrumentation must reliably capture microvolt-level changes in  \( \Delta E_s \) (steady-state voltage change) and  ΔEt (voltage change during the pulse). The IEST High-Precision Electrochemical Analyzer addresses these core pain points through:

• Exceptional Measurement Precision: With a measurement accuracy of 0.01% FS, the device ensures that even minute voltage fluctuations are recorded with high signal-to-noise ratios.

• Aviation-Grade Stability: Reliable multi-channel control allows for consistent current application during short pulses, which is critical for satisfying the theoretical condition \( \tau \ll L^2/D \)

• High-Resolution Data Capture: By capturing high-fidelity data even at low magnitudes of \( \Delta E_s \) , the IEST analyzer directly supports more reliable and reproducible GITT analysis across varying SOC states.

Figure 5. IEST electrochemical analyzer 

8. GITT Best Practices & Common Pitfalls to Avoid

A successful GITT test requires careful attention to experimental detail. Beyond understanding what GITT is theoretically, avoiding these common pitfalls is key to obtaining meaningful diffusion kinetics data.

8.1 Pulse Duration (\( \tau \)) Selection: A Critical Trade-off

Choosing the correct current pulse time is paramount. If  \( \tau \)  is too long, it violates the “semi-infinite diffusion” assumption, causing the calculated diffusion coefficient to be underestimated. If \( \tau \) is too short, the voltage response (\( \Delta E_s \) ) becomes exceedingly small and prone to measurement noise. A practical approach is to estimate the diffusion coefficient (Dest) from literature or a quick experiment, then ensure your chosen \( \tau \) satisfies the condition \( \tau \ll L^2/D \).

8.2 Defining the Relaxation Endpoint Scientifically

A common mistake is using a fixed, arbitrary relaxation time. Instead, a voltage stabilization threshold should define the endpoint. For instance, the relaxation phase can be considered complete when the open-circuit voltage changes by less than 0.1 mV over a 5-minute period. This ensures lithium-ion concentration reaches equilibrium throughout the electrode, a prerequisite for an accurate \( \Delta E_s \) value.

8.3 Validating Data with the \( \sqrt{\tau} \) Plot

A powerful internal check for data quality involves plotting the steady-state voltage change \( \Delta E_s \) for each step against the square root of the pulse time \( \sqrt{\tau} \). For a valid test under Fickian diffusion, this plot of \( \Delta E_s \) versus \( \sqrt{\tau} \) should yield a straight line passing through the origin. Significant deviation from linearity suggests that the test conditions may not meet the model’s assumptions, calling for a re-examination of parameters like pulse duration or electrode homogeneity.

8.4 The Impact of Electrode Parameter Accuracy

The calculated absolute value of the diffusion coefficient (D) is directly proportional to the square of the electrode thickness (L) and is sensitive to the active material mass (mB) and coating density. Inaccurate measurements of these geometric and mass parameters are not just minor errors; they are systematically magnified in the final result. Meticulous characterization of the electrode itself is therefore as important as the electrochemical measurement for reliable GITT diffusion coefficient calculation.

9. Summary

The diffusion behavior of lithium ions within the active material reflects the microscopic kinetic performance of the battery and greatly affects the overall performance of the battery. Segmented studies of electrochemical reactions at different charging and discharging depths can effectively find the key factors affecting the polarization of the battery at each stage, and GITT test can effectively determine the diffusion coefficient of lithium ions, D, and thus study the kinetic process of the battery.

10. References

[1] Nickol A, Schied T, Heubner C, et al. GITT analysis of lithium insertion cathodes for determining the lithium diffusion coefficient at low temperature: challenges and pitfalls[J]. Journal of The Electrochemical Society, 2020, 167(9): 090546.

[2] Tang K , Yu X , Sun J ,et al. Kinetic analysis on LiFePO4 thin films by CV, GITT, and EIS[J].Electrochimica Acta, 2011, 56(13):4869-4875.

[3] Hong C, Leng Q, Zhu J, et al. Revealing the correlation between structural evolution and Li+ diffusion kinetics of nickel-rich cathode materials in Li-ion batteries[J]. Journal of materials chemistry A, 2020, 8(17): 8540-8547.[4] Zhou Y, Xu G, Lin J, et al. A Multicationic-Substituted Configurational Entropy-Enabled NASICON Cathode for High-Power Sodium-Ion Batteries[J]. Nano Energy, 2024: 109812.

11. FAQ About GITT