OSC modelling of 3-way automotive catalysts to understand the effect of latent OSC on dynamic OSC performance

2022-01-0693

03/29/2022

Event
WCX SAE World Congress Experience
Authors Abstract
Content
A Three-way automotive catalyst's ability to store oxygen is still a crucial performance metric for modern day catalyst applications. With more stringent emissions legalisation, the oxygen storage capacity (OSC) within the catalyst can assist with converting different exhaust gases such as CO, THC and NOx under transient operating conditions. OSC is currently the only onboard catalyst performance metric recorded during a vehicle's useful life. Catalyst performance is correlated to this OSC measurement. OSC in 3-way automotive catalysts can be split into two main OSC types. "Latent OSC" deep within the layers of the washcoat and "dynamic" OSC on the surface layers of the catalyst washcoat. Dynamic OSC is more commonly applied in the evaluation of the activity of the catalyst during practical operation. This paper uses a kinetic OSC model with a layered washcoat approach to analyse OSC test data and show how latent OSC can affect the dynamic OSC performance of a 3-way catalyst. Model predictions highlight how both latent and dynamic OSC of a catalyst varies throughout OSC testing affecting the conversion performance of the catalyst. The application of the model can be applied to on-road applications. Model predictions are compared in detail to test data to study the interaction between dynamic and latent OSC and how this oxygen exchange is affected by catalyst ageing. With a mesh approach, the model can show how OSC is affected axially throughout the brick. This approach will help create an understanding to gain optimal dynamic OSC performance and improve the conversion efficiency of the catalyst.
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Citation
Mc Grane, L., Douglas, R., Elliott, M., Irwin, K. et al., "OSC modelling of 3-way automotive catalysts to understand the effect of latent OSC on dynamic OSC performance," SAE Technical Paper 2022-01-0693, 2022, .
Additional Details
Publisher
Published
Mar 29, 2022
Product Code
2022-01-0693
Content Type
Technical Paper
Language
English