Gas Solubility and Vapor Pressure of Electrolytes Based on Alkylcarbonates Containing Litfsi, Lifap, and LiPF6 Lithium Salt: Measurement and Prediction

Most liquid electrolytes used in commercial lithium-ion batteries are composed by alkylcarbonate mixture containing lithium salt. The decomposition of these solvents by oxidation or reduction during cycling of the cell, induce generation of gases (CO2, CH4, C2H4, CO …) increasing of pressure in the sealed cell, which causes a safety problem [1]. The prior understanding of parameters, such as structure and nature of salt, temperature pressure, concentration, salting effects and solvation parameters, which influence gas solubility and vapor pressure of electrolytes is required to formulate safer and suitable electrolytes especially at high temperature.

We present in this work the CO2, CH4, C2H4, CO solubility in different pure alkyl-carbonate solvents (PC, DMC, EMC, DEC) and their binary or ternary mixtures as well as the effect of temperature and lithium salt LiX (X = LiPF6, LiTFSI or LiFAP) structure and concentration on these properties. Furthermore, in order to understand parameters that influence the choice of the structure of the solvents and their ability to dissolve gas through the addition of a salt, we firstly analyzed experimentally the transport properties (Self diffusion coefficient (D), fluidity (h-1), and conductivity (s) and lithium transport number (tLi) using the Stock-Einstein, and extended Jones-Dole equations [2]. Furthermore, measured data for the of CO2, C2H4, CH4 and CO solubility in pure alkylcarbonates and their mixtures containing LiPF6; LiFAP; LiTFSI salt, are reported as a function of temperature and concentration in salt. Based on experimental solubility data, the Henry’s law constant of gases in these solvents and electrolytes was then deduced and compared with values predicted by using COSMO-RS methodology within COSMOthermX software. From these results, the molar thermodynamic functions of dissolution such as the standard Gibbs energy, the enthalpy, and the entropy, as well as the mixing enthalpy of the solvents and electrolytes with the gases in its hypothetical liquid state were calculated and discussed [3]. Finally, the analysis of the CO2 solubility variations with the salt addition was then evaluated by determining specific ion parameters Hi by using the Setchenov coefficients in solution. This study showed that the gas solubility is entropy driven and can been influenced by the shape, charge density, and size of the anions in lithium salt.

References

[1] S.A. Freunberger, Y. Chen, Z. Peng, J.M. Griffin, L.J. Hardwick, F. Bardé, P. Novák, P.G. Bruce, Journal of the American Chemical Society 133 (2011) 8040-8047.

[2] P. Porion, Y.R. Dougassa, C. Tessier, L. El Ouatani, J. Jacquemin, M. Anouti, Electrochimica Acta 114 (2013) 95-104.

[3] Y.R. Dougassa, C. Tessier, L. El Ouatani, M. Anouti, J. Jacquemin, The Journal of Chemical Thermodynamics 61 (2013) 32-44.

Data-Driven Dynamic Prediction of Interrelated Heat and Electric Outputs of Microturbines

Microturbines are among the most successfully commercialized distributed energy resources, especially when they are used for combined heat and power generation. However, the interrelated thermal and electrical system dynamic behaviors have not been fully investigated. This is technically challenging due to the complex thermo-fluid-mechanical energy conversion processes which introduce multiple time-scale dynamics and strong nonlinearity into the analysis. To tackle this problem, this paper proposes a simplified model which can predict the coupled thermal and electric output dynamics of microturbines. Considering the time-scale difference of various dynamic processes occuring within microturbines, the electromechanical subsystem is treated as a fast quasi-linear process while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A three-stage subspace identification method is utilized to capture the dominant dynamics and predict the electric power output. For the thermo-mechanical process, a radial basis function model trained by the particle swarm optimization method is employed to handle the strong nonlinear characteristics. Experimental tests on a Capstone C30 microturbine show that the proposed modeling method can well capture the system dynamics and produce a good prediction of the coupled thermal and electric outputs in various operating modes.