Particulate matter load estimation in diesel particulate filters

Chintan Dilip Shah, Purdue University

Abstract

A reliable diesel particulate filter (DPF) regeneration system requires an accurate estimate of the amount of the particulate matter (PM) collected inside the DPF. The current method of PM load estimation uses the pressure drop signal across the DPF as a reflection of the ‘total PM collected’ inside the DPF. However, this method is not reliable as it is difficult to implement in transient engine operating conditions. An extension of this approach has been developed in this research, the so-called pressure drop model, which relates the pressure drop across the DPF to the ‘instantaneous PM collection rate’. Experimental data shows that this technique may be able to accurately estimate PM load under transient engine operation. Further validation is required in order to identify the scope of this technique. Another approach called the mass flow model is presented that accounts for the mass balance of the exhaust by sensing the state of the exhaust pre- and post-DPF. Known Engine Control Module (ECM) signals are required in this model. This model does not seem to be affected by the distribution inside the DPF, and gives an accurate estimate of the PM load under transient engine conditions. The results from the experimental data suggest that the model has the potential to work well in the presence of non-uniform distribution. This capability is very crucial for the regeneration system and is not available in the current technique. Additionally, both models are easy to implement and have low computation times. On comparison with the highly accurate AVL 415 soot sensor, the mass flow model and the pressure drop model have a maximum error percent of 8.1% and 16%, respectively.

Degree

M.S.M.E.

Advisors

Meckl, Purdue University.

Subject Area

Mechanical engineering

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