Modeling and Experiments for Lean NOx Trap Desulfation by High Frequency A/F Modulation

Paper #:
  • 2000-01-1201

Published:
  • 2000-03-06
Citation:
Golovin, A. and Asik, J., "Modeling and Experiments for Lean NOx Trap Desulfation by High Frequency A/F Modulation," SAE Technical Paper 2000-01-1201, 2000, https://doi.org/10.4271/2000-01-1201.
Pages:
15
Abstract:
When an engine is operated at a lean air-fuel (A/F) ratio, a lean NOx trap (LNT) (or lean NOx catalyst) is needed to increase the NOx conversion efficiency. The presence of sulfur in the fuel degrades the LNT NOx storage capacity and significantly reduces its NOx conversion efficiency. The sulfur degradation is reversible and requires maintaining a temperature of 650 °C at a rich A/F ratio condition (λ < 1.00) for a period of approximately 5 minutes to purge the trap. The desulfation method used here produces an exotherm in an LNT by sequential A/F modulation of the cylinders, resulting in exhaust flows of rich and lean mixtures and creating an exotherm in the LNT (Reference (18).SIMULINK/MATLAB software is used for modeling the temperature and the oxidant-reductant concentrations. The TWC and the LNT are divided into cells. The function of each cell is described using mass and energy balance equations for the gas and solid phases. The equations in the model are simplified and the coefficients are adjusted to match steady state experimental data. The oxygen storage effect in the TWC and LNT is taken into account.The mathematical model is compared to steady state and dynamic experimental data. The influence of the parameters of the model on the dynamics of constituent concentrations and temperatures is analyzed. The longitudinal temperature distribution and the dynamics of temperature behavior for a step change in the oxidant and reductant predicted by the model compare well with experimental results. The only reductant considered for the exotherm process is CO. The model predicts a “double frequency” effect in the temperature at the entrance of the LNT, which is observed in the experimental data. The model assists in understanding the interactions, kinetics, and dynamics of this process.
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