Diesel Particulate Filters (DPFs) are well assessed exhaust aftertreatment devices currently equipping almost every modern diesel engine to comply with the most stringent emission standards. However, an accurate estimation of soot content (loading) is critical to managing the regeneration of DPFs in order to attain optimal behavior of the whole engine-after-treatment assembly, and minimize fuel consumption.
Real-time models can be used to address challenges posed by advanced control systems, such as the integration of the DPF with the engine or other critical aftertreatment components or to develop model-based OBD sensors.
One of the major hurdles in such applications is the accurate estimation of engine Particulate Matter (PM) emissions as a function of time. Such data would be required as input data for any kind of accurate models. The most accurate way consists of employing soot sensors to gather the real transient soot emissions signal, which will serve as an input to the model.
Objective of this study is model a DPF in real-time by means of the 1-D code ExhAUST (Exhaust Aftertreatment Unified Simulation Tool). ExhAUST is characterized by a high degree of accuracy in capturing the essential phenomena, such as wall/cake collection and continuous/forced regeneration processes occurring inside the DPF. Moreover, it exhibits high computational efficiency due to its peculiar analytical formulation.
In this paper, ExhAUST has been coupled to instruments, such as the micro-soot sensor and TSI-EEPS, which are capable of measuring transient PM emissions. Data acquired by the soot sensor have been used as continuous input in terms of PM flow rate entering the DPF, so that deposition and oxidation rates could be developed depending on those information. Numerical results are compared with experimental data gathered at the WVU engine laboratory using a Mack heavy-duty diesel engine coupled to a Johnson Matthey CCRT aftertreatment system tested on a transient FTP cycle. Results show that ExhAUST is capable of correctly capturing the evolution of back pressure during transient cycles, with minor model tuning during operation; thus, showing optimal pre-requisites for real-time vehicle applications.