This paper presents a novel method to estimate an exhaust pressure at 357 different steady-state engine operating conditions using a diesel particulate filter (DPF) mass flow model to precisely control the air quantity for a light-duty diesel engine operated with dual-loop exhaust gas recirculation (EGR) and variable geometry turbocharger (VGT) systems. This model was implemented on a 32 bit real-time embedded system and evaluated through a processor-in-the-loop-simulation (PILS) at two transient engine operating conditions. And the proposed model was validated in a vehicle. By applying Darcy's law, the DPF mass flow model was developed and shows a root mean square error (RMSE) of 3.7 g/s in the wide range of the DPF mass flow and above 99% linear correlation with actual values. With such reasonable uncertainties of the DPF mass flow model, the exhaust pressure was estimated via the application of a nonlinear coordinate transformation to the VGT model. The DPF mass flow based exhaust pressure estimation exhibits below 6% error of the exhaust pressure under steady-state conditions. The method was also validated through the PILS and the vehicle test under transient conditions. The results show a reasonable accuracy within 10% error of the exhaust pressure.
Exhaust Pressure Estimation Using a Diesel Particulate Filter Mass Flow Model in a Light-Duty Diesel Engine Operated With Dual-Loop Exhaust Gas Recirculation and Variable Geometry Turbocharger Systems
Contributed by the Combustion and Fuels Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received December 29, 2013; final manuscript received July 10, 2014; published online July 29, 2014. Assoc. Editor: Stani Bohac.
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Lee, H., Han, M., Sohn, J., and Sunwoo, M. (July 29, 2014). "Exhaust Pressure Estimation Using a Diesel Particulate Filter Mass Flow Model in a Light-Duty Diesel Engine Operated With Dual-Loop Exhaust Gas Recirculation and Variable Geometry Turbocharger Systems." ASME. J. Eng. Gas Turbines Power. November 2014; 136(11): 111507. https://doi.org/10.1115/1.4028018
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