Algorithm to Calibrate Catalytic Converter Simulation Light-Off Curve FCA US LLC

Format:

Book

Conference/Event

Author/Creator:

Wilson, John Parley, author.

Contributor:

DelVescovo, Dan

Conference Name:

WCX SAE World Congress Experience (2024-04-16 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2024

Summary:

Spark ignition engines utilize catalytic converters to reform harmful exhaust gas emissions such as carbon monoxide, unburned hydrocarbons, and oxides of nitrogen into less harmful products. Aftertreatment devices require the use of expensive catalytic metals such as platinum, palladium, and rhodium. Meanwhile, tightening automotive emissions regulations globally necessitate the development of high-performance exhaust gas catalysts. So, automotive manufactures must balance maximizing catalyst performance while minimizing production costs. There are thousands of different recipes for catalytic converters, with each having a different effect on the various catalytic chemical reactions which impact the resultant tailpipe gas composition. In the development of catalytic converters, simulation models are often used to reduce the need for physical parts and testing, thus saving significant time and money. However, calibration of these models can be challenging and requires significant time and effort. Catalytic converter models require the specification of input conditions (id est temperature, flowrate, and species concentrations). Then they calculate the predicted exhaust gas composition by simulating the chemical reactions occurring within the catalyst. These simulations can then be calibrated and validated against experimental measurements. The chemical reaction rates in the model utilize an Arrhenius expression which includes two tunable variables, the pre-exponential factor (A), which is a measure of collision frequency, and the activation energy (E), which is a threshold to overcome for molecules to react. Calibration of these values often requires many iterations, checking the results and adjusting to eventually identify the best values. In this work, an optimization algorithm was developed to automatically tune these parameters to best simulate catalyst light-off data. This algorithm is presented. It has the potential to significantly reduce time in calibrating catalyst models

Notes:

Vendor supplied data

Publisher Number:

2024-01-2630

Access Restriction:

Restricted for use by site license