Determination of the binary diffusion coefficient of a battery electrolyte

Importance of the Topic

Accurate modeling of lithium-ion battery performance relies on precise transport parameters for electrolyte solutions. The binary diffusion coefficient governs how ions migrate through the liquid phase, which directly affects charge–discharge rates, capacity retention and cell safety. Determining this coefficient alongside separator characteristics improves simulation fidelity and supports optimized battery design.

Objectives and Study Overview

This study presents a galvanostatic pulse polarization (GPP) approach to quantify the binary diffusion coefficient of a commercial 1 mol/L LiPF6 electrolyte in EC:DMC (1:1 v/v) at 20 °C. In parallel, the method yields separator transport metrics: MacMullin number and tortuosity. The procedure adapts protocols from Ehrl and Landesfeind et al., ensuring high accuracy for battery modeling inputs.

Methodology and Instrumentation

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Cell design: TSC Battery Advanced measuring cell with metallic lithium working and counter electrodes (active area = 1.13 cm2) separated by a porous polyethylene film (500 µm thickness, 30 % porosity) pre-soaked in electrolyte for 48 h.

Electrolyte: 1 mol/L LiPF6 in EC:DMC (1:1 v/v) from Sigma-Aldrich used without further purification.

Temperature control: Microcell HC Peltier unit integrated in NOVA software, maintained at 20 ± 0.1 °C.

Electrochemical workstation: Metrohm Autolab PGSTAT204 with FRA32M for EIS and CV measurements; NOVA software for data acquisition.

Measurement sequence:

1. Temperature equilibration (1800 s).
2. EIS from 100 kHz to 1 Hz (1 mV RMS, 20 points/decade).
3. GPP with ±150 µA pulses (900 s each).
4. OCP monitoring for 10800 s after each pulse (1 s intervals).

Main Results and Discussion

Impedance data fitted to an equivalent circuit (Rbulk in series with R||CPE) yielded Rbulk = 63.3 Ω for ion transport through the separator. With a cell constant of 0.0442 cm⁻¹, the separator conductivity was derived and compared to the free electrolyte conductivity (9.9 mS/cm). This ratio produced a MacMullin number of 14.1. Using the known porosity (30 %), the tortuosity was calculated as 4.2, slightly below literature values. Analysis of OCP decay after current interruptions gave a slope of 0.0023 s⁻¹, leading to a binary diffusion coefficient of 2.5 × 10⁻⁶ cm²/s at 20 °C, in close agreement with previous reports.

Benefits and Practical Applications

• Provides accurate input for battery performance simulations and design optimization.
• Enables characterization of separator quality in manufacturing and QA/QC processes.
• Supports comparative studies of new electrolyte formulations and advanced separator materials.

Future Trends and Opportunities

Emerging research will extend this methodology to high-voltage and solid‐state electrolytes, explore temperature and concentration dependencies, and integrate real-time monitoring within cell assemblies. Combining diffusion data with digital twin frameworks and machine learning can further accelerate electrolyte and separator development.

Conclusion

The GPP method implemented with the TSC Battery Advanced cell and Microcell HC setup offers a robust route to determine both the binary diffusion coefficient of lithium-ion electrolytes and key separator transport properties. These metrics enhance the predictive power of battery simulations and support advanced material screening.

Reference

• [1] A. Ehrl et al., Journal of The Electrochemical Society, 164(4): A826–A836 (2017).
• [2] J. Landesfeind, H.A. Gasteiger, Journal of The Electrochemical Society, 166(14): A3079–A3097 (2019).
• [3] T. Hou, C.W. Monroe, Electrochimica Acta, 332: 135085 (2020).
• [4] F. Wohde, M. Balabajew, B. Roling, Journal of The Electrochemical Society, 163(5): A714–A721 (2016).
• [5] Metrohm Application Note AN-BAT-006, Determination of the MacMullin Number.