Multivariate Regression and Generalized Linear Model Optimization in Diesel Transient Performance Calibration 2013-01-2604
With stringent emission regulations, aftertreatment systems with a Diesel Particulate Filter (DPF) and a Selective Catalytic Reduction (SCR) are required for diesel engines to meet PM and NOx emissions. The adoption of aftertreatment increases the back pressure on a typical diesel engine and makes engine calibration a complicated process, requiring thousands of steady state testing points to optimize engine performance. When configuring an engine to meet Tier IV final emission regulations in the USA or corresponding Stage IV emission regulations in Europe, this high back pressure dramatically impacts transient performance. The peak NOx, smoke and exhaust temperature during a diesel engine transient cycle, such as the Non-Road Transient Cycle (NRTC) defined by the US Environmental Protection Agency (EPA), will in turn affect the performance of the aftertreatment system and the tailpipe emissions level.
As calibration is complex and costly, simulation and trend analysis are widely used in the industry. Most of the analysis is focused on steady state calibration. This paper describes an optimization methodology focused on diesel engine transient performance. The multivariate regression and Generalized Linear Model (GLM) approaches are compared and discussed. Non-linear effects are addressed by the link function in the Generalized Linear Model (GLZ). The methodology is verified to provide optimal engine response during transient operation while still meeting emission regulations.
The application of the methodology to the Cat ® C18 Tier 4 engine is presented to demonstrate the optimized performance combined with low emissions in transient operations.