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Development and Experimental Validation of a Control Oriented Model of a Catalytic DPF
Technical Paper
2019-01-0985
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
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The wall-flow Diesel Particulate Filter (DPF) is currently the most common after-treatment system used to meet the particulate emissions regulations for automotive engines. Today’s technology shows the best balance between filtration efficiency and back-pressure in the engine exhaust pipe. During the accumulation phase the pressure drop across the filter increases, thus requiring periodic regeneration of the DPF through after and post fuel injection strategies. This paper deals with the development of a control oriented model of a catalytic silicon carbide (SiC) wall flow DPFs with CuFe2O4 loading for automotive Diesel engines. The model is intended to be used for the real-time management of the regeneration process, depending on back-pressure and thermal state. In order to ensure suitable computational demand and to realize emissions control and fuel economy objectives, the 0-D model has been developed with the aim of investigating the essential behavior of the system, such as the chemical kinetic of filtered soot oxidation, the thermal and backpressure dynamics along accumulation and regeneration processes. Parameters identification and model validation have been performed vs. experimental data measured on the engine test bench at the exhaust of a EURO 5 light-duty Diesel engine, in different operating conditions. During the accumulation process, engine speed, load and rail pressure are controlled to ensure several levels of trapped soot; on the other hand, the injections pattern, which directly affects the DPF inlet temperature, is handled during regeneration tests. The results show that the model simulates the thermal dynamics and the pressure drop across the filter with good accuracy.
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D'Aniello, F., Rossomando, B., Arsie, I., and Pianese, C., "Development and Experimental Validation of a Control Oriented Model of a Catalytic DPF," SAE Technical Paper 2019-01-0985, 2019, https://doi.org/10.4271/2019-01-0985.Data Sets - Support Documents
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