PDF Publication Title:
Text from PDF Page: 113
Reaction rates. Finally, there are ten rate constants in the model, which are alto- gether unknown. An initial scan was performed testing dozens of combinations of rate constants for just one criterion: The solver needs to be able to calculate the ini- tial equilibrium. This technique yielded an initial guess for the rate constants in the range 1013–1019 mol · m−2 · s−1 (data not shown). One exception is the rate of disso- lution of sulfur, which is a non-electrochemical reaction that occurs at a much slower rate. Because of the model’s high sensitivity on this value, it was chosen to be fitted independently as follows: 1. Choose the other rate constants fast. 2. Starting from a fully charged cell, simulate a discharge with a slow rate and plot voltage vs. capacity (see Fig. 5.16). 3. Adjust the rate of dissolution until the slope matches that of the reference data. Even though it is somewhat off in absolute position, the best agreement for the slope is obtained for kfwd = 3.0 · 10−4 mol · m−2 · s−1. All other rate constants were ad- justed by studying the concentrations of polysulfides during cycling and increasing the rate constants for those reactions whose reactants accumulated excessively (data not shown). Those still random, but “normalized” rates, were then scaled by a common factor, which is fitted as follows: 1. Simulate a charge and discharge at a medium rate. 2. Adjust the factor until the voltage hysteresis, i.e. the difference between charge and discharge voltage matches experimental data. Even for the rather slow experiment presented in Fig. 5.17 (charge: C/20, discharge: C/50), the reaction overpotential is considerably large. For the slowest rate constant, the solver did not even come up with a valid solution for the entire experiment. Since the cell is charged at a faster rate, the impact of slow electrochemical rate constants affects the charge branch more strongly than the discharge branch. In summary, the best fit is obtained for a scaling factor of ten. At this point the model is fully calibrated, but not necessarily validated. In order to come up with the final values reported in Tab. 5.2, the manual fitting process was iterated three times, since the combined effect of all other fit parameters may signifi- cantly affect the best value of each single parameter. For the purpose of illustrating the process, the parameter variations shown here are selected from the second run. The first run still contains lots of aborted simulations and some which are really far off. During the third run, in contrast, the parameters are changed by tiny amounts only, making it hard to discern the effect they have on the simulation. 113PDF Image | Lithium-Sulfur Battery: Design, Characterization, and Physically-based Modeling
PDF Search Title:
Lithium-Sulfur Battery: Design, Characterization, and Physically-based ModelingOriginal File Name Searched:
Dissertation_David_N._Fronczek_The_Lithium_Sulfur_Battery.pdfDIY PDF Search: Google It | Yahoo | Bing
Sulfur Deposition on Carbon Nanofibers using Supercritical CO2 Sulfur Deposition on Carbon Nanofibers using Supercritical CO2. Gamma sulfur also known as mother of pearl sulfur and nacreous sulfur... More Info
CO2 Organic Rankine Cycle Experimenter Platform The supercritical CO2 phase change system is both a heat pump and organic rankine cycle which can be used for those purposes and as a supercritical extractor for advanced subcritical and supercritical extraction technology. Uses include producing nanoparticles, precious metal CO2 extraction, lithium battery recycling, and other applications... More Info
CONTACT TEL: 608-238-6001 Email: greg@infinityturbine.com (Standard Web Page)