Optimization of Diesel Oxidation Catalyst (DOC) on Passenger Cars to Improve Emission Robustness 2015-01-1013
Emission compliance at the production level has been a challenge for vehicle manufacturers. Diesel oxidation catalyst (DOC) plays a very important role in controlling the emissions for the diesel vehicles. Vehicle manufacturers tend to ‘over design’ the diesel oxidation catalyst to ‘absorb’ the production variations which seems an easier and faster solution. However this approach increases the DOC cost phenomenally which impacts the overall vehicle cost.
The main objective of this paper is to address the high variation in CO tail pipe emissions which were observed on a diesel passenger car during development. This variation was posing a challenge in consistently meeting the internal product requirement/specification. This paper outlines a step by step process by following a DFSS (Design for Six Sigma) methodology in selecting an optimized DOC design in terms of tailpipe emission, engine performance and system cost for a given level of engine-out CO emission variation in a cheaper and faster way by combining concepts of virtual emission simulation, Taguchi design of experiments and multi parameter optimization.
In order to quantify the emission variation, engine-out & tail pipe emissions were measured on various development vehicles with the same level of hardware and calibration. Further, a parameter diagram (P-diagram) which identified all the control factors, input signal, and noise and error states was developed. The optimal values of control parameters were arrived by rationalizing the DOC design against DOC system cost and other performance criteria using multi parameter optimization methods.
The optimized DOC design had a 14% reduction in CO emissions and a ∼14% improvement in robustness based on signal to noise ratio when compared with the baseline design that was being used during development. Monte Carlo simulations predicted an 87% reduction in out-of-specification emissions for an additional 23% increase in cost when compared to baseline.