Study on particulate matter emission amount estimation model for engine control

Abstract

Real drive emissions (RDE) regulation were introduced from Euro6-d regulation. In order to achieve the regulation value of PM(Particulate Matter) emission in the RDE regulation, it is effective to install GPF(Gasoline Particulate Filter) system. To prevent GPF corruption , GPF system needs “PM emission amount estimation model” that estimates PM emission amount from direct injection gasoline engine to estimate PM corrected amount. Some models, for example packet model and map-based model, have been proposed as PM emission amount estimation model. However, these models have problems in terms of calculation load and accuracy for On-board calculation. Purpose of this research is to study On-board PM emission amount estimation model . We proposed a new model method of physical quantities that dominate PM production. Developed model uses probability density function of mixture fraction space so that mixture and fuel adhesion can be treated uniformly as a fuel distribution in cylinder. Also, variance of mixture-derived probability density function and fuel adhesion ratio are functionalized by a second-order polynomial of engine control parameters, and can be applied to multiple operating conditions. The developed model was verified by comparison with measurement data. Under 25 degC of coolant temperature(19 operation points), the estimated error was during 0% and 59% and average estimated error was 19%.

Keywords

• internal combustion engine
• spark ignition engine
• engine control
• particulate matter
• catalyzer