Electrochemical Characterization and Temperature Dependency of Mass-Transport Properties of LiPF₆in EC:DEC

Abstract

Mass transport in the electrolyte is one of the limiting processes when it comes to the power density and energy efficiency of lithium-ion batteries. Electrolyte characterizations are therefore of utmost importance. This study reports the ionic conductivity, diffusion coefficient, lithium-ion transport number, and thermodynamic enhancement factor, as well as density and viscosity, for the electrolyte LiPF 6 in EC:DEC (1:1, by weight) at 10 • C, 25 • C, and 40 • C and for concentrations between 0.5 M and 1.5 M. By combining mathematical modeling and three experiments: conductivity measurements, concentration cells, and galvanostatic polarizations, the mass transport phenomena were fully characterized. All parameters were found to vary strongly with both concentration and temperature proving that temperature dependent parameters are essential when studying thermal behavior of lithium-ion batteries. Moreover, conductivity increased with temperature and showed a local maximum at around 1 M within the concentration range at all temperatures. The other parameters either showed a continuous decrease (diffusion coefficient and lithiumion transport number) or increase (thermodynamic enhancement factor) with concentration at all temperatures. Limited liquid range leading to solvent crystallization at 10 • C leads to very poor performance, possibly due to the strong coordination between the lithium ion and the crystallizing species, EC. Overall, the studied electrolyte is found to perform poorly compared to previously studied systems.Lithium-ion batteries (LIBs) have proven their commercial viability by conquering most markets for rechargeable batteries, including mobile devices and electric vehicles. The high power performance of LIBs has been found to be limited by the transport of lithium ions in the electrolyte. 1-4 As these batteries are being implemented in increasingly demanding applications, such as hybrid electric vehicles, an accurate prediction of their performance is crucial, to be able to design better battery packs. The predictions are made possible through modeling, which requires characterization of the mass-transport properties of the materials used in a wide range of operational conditions, including variations in temperature.Characterizations of electrolytes are not uncommon. However, they are mostly limited to partial investigations, providing only some of the properties needed to describe the performance of the electrolyte or assuming that it behaves like a dilute system (or both). 5,6 Concentrated electrolyte theory, which includes ion-ion interactions by implementing friction coefficients for all components, 7 should be used when modeling and characterizing the electrolytes used in LIBs, as dilute theory will generate big deviations in concentration profile simulation, and in extension voltage prediction, especially at high currents.

Determination of Transport Parameters in Liquid Binary Electrolytes: Part II. Transference Number

“…While in all these reports, in which the transference number does not change more than 10% in the investigated concentration range, the salts and solvents differ, also strong concentration dependencies are reported for example for LiPF 6 electrolytes. Monotonically decreasing values for the lithium transference number are reported by Nyman et al 19 for LiPF 6 in EC:EMC (3:7 w:w, red crosses) and Lundgren et al 20 for LiPF 6 in EC:DEC (1:1 w:w, red diamonds). While comparison of the reports for LiPF 6 based electrolytes (compare red symbols in Figure 17) show the influence of the solvent on the transference number, direct comparison with the electrolyte investigated in this work is difficult due to the different salts, solvents, and measurement techniques.…”

“…Therefore, it is necessary to perform three different experiments to determine the transference number, which usually results in an accumulation of inaccuracies from the errors in each experimental procedure and due to the necessity to use arbitrary functional relationships (e.g., assuming a concentration independent transference number in some cases). Alternatively, the diffusion coefficient, the transference number, and the thermodynamic factor can also be determined by a numerical optimization approach as shown in the publications by Georén and Lindbergh, 18 Nyman et al, 19 and Lundgren et al 20 In this approach, usually, the same or similar experiments as for the galvanostatic polarization are performed.…”

Determination of Transport Parameters in Liquid Binary Lithium Ion Battery Electrolytes

“…A similar approach based on a more elaborate optimization framework was used by Nyman et al 19 and Lundgren et al 26 for LiPF 6 in EC:EMC (3:7 w:w) and in EC:DEC (1:1 w:w), respectively. In both publications, solvent effects and convective transport due to the motion of ions are included in the physical model used for the numerical optimization and, in addition, the effective ionic conductivity of the used glass microfiber filters was determined experimentally.…”

Experimental and Modeling Analysis of Graphite Electrodes with Various Thicknesses and Porosities for High-Energy-Density Li-Ion Batteries

“…Moreover, the lithium diffusion coefficient in the solid phase, the reaction-rate constant and the electrolyte properties are taken to be dependent on the lithium concentration, which is in harmony with experimental facts. [31][32][33][34][35] Indeed, the assumption of parameter invariance with concentration falls short for high-loading electrodes where large concentration gradients form even at moderate C-rate in the liquid phase across the cell and in the solid phase across the electrodes. However, composition-dependent properties may complicate the model convergence, hence calling for an increase of the number of nodes across the cell sandwich and/or across the radial dimension of the active particles, along with a decrease of the time step, which is at the expense of simulation time.…”