Optimization of Metallic Substrate by 1D Simulations to Fulfill High Power Cold Start Conditions

<div class="section abstract"><div class="htmlview paragraph">The future of the internal combustion engine (ICE) is closely tied to its ability to achieve life cycle emissions comparable to those of pure battery electric vehicles (BEVs). To reach this goal, it is essential not only to utilize carbon-free fuels but also to enhance the hybridization of the powertrain to reduce fuel consumption. Additionally, it is crucial to minimize pollutant emissions to near-zero levels, necessitating the development of highly sophisticated exhaust aftertreatment systems.</div><div class="htmlview paragraph">For Plug-In Hybrid Electric Vehicles (PHEVs), one particular use case is the High-Power Cold Start (HPCS). This scenario occurs when the transition from pure electric drive to ICE-assisted drive happens during a high load request, such as accelerating on a freeway ramp. This use case has been evaluated by CARB and in numerous other studies. However, in this paper, the authors aim to investigate which metallic substrate technology performs best during an HPCS. This condition differs significantly from a normal cold start: the exhaust gas flow and the available energy are much higher. The catalyst must heat up quickly (as in a conventional cold start), but the required volume above the light-off temperature must be much larger to convert a higher quantity of pollutants.</div><div class="htmlview paragraph">A 1D tool will be used to quickly identify and select the best substrate technology to meet specific light-off time requirements, assuming ideal gas distribution at the inlet and simplified chemistry. The test cycle will then be simulated with a 1D code that accounts for detailed chemistry in the catalyst, examining the effects of washcoat loading and PGM composition. Additionally, the impact of water condensation/evaporation and hydrocarbon adsorption on the overall abatement efficiency of the catalyst will be evaluated using specific submodels. Measured data of raw engine emissions and gas temperature will be used as boundary conditions to model the driving cycle, and the numerical results will be compared with measured cumulative tailpipe emissions to validate the numerical model. Based on this validation, strategies for improving efficiency will be considered, focusing on the geometry of the catalyst while maintaining the same measured emissions from the experimental campaign as boundary conditions..</div></div>