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Robust DPF Regeneration Control for Cost-Effective Small Commercial Vehicles
ISSN: 0148-7191, e-ISSN: 2688-3627
Published September 04, 2017 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Small commercial vehicles (SCV) with Diesel engines require efficient exhaust aftertreatment systems to reduce the emissions while keeping the fuel consumption and total operating cost as low as possible. To meet current emission legislations in all cases, a DOC and DPF and some NOx treatment device (e,g. lean NOx trap or SCR) are required.
Creating a cost-effective SCV also requires keeping the cost for the exhaust aftertreatment system as low as possible because the contribution to total vehicle cost is high. By using more sophisticated and more robust operating strategies and control algorithms, the hardware cost can be reduced. To keep the calibration effort at a low level, it is necessary to apply only algorithms which have a time-efficient calibration procedure.
This paper will focus on the active regeneration of the DPF. For safe and efficient DPF regeneration, a very reliable and stable DOC out temperature control is required. DOC characteristics and design are often limited by cost and available space but also strongly influence the control requirements and thus the performance. This leads to more sophisticated and more robust control algorithms.
In this paper an advanced control algorithm for DOC outlet temperature control for a SCV is presented. The control algorithm applies model-based control and gain-scheduling techniques. An overview over the control algorithm is given and its performance is evaluated on engine test bench, chassis dynamometer and on the public road and compared to the traditional concept which was used before. The results and experiences are presented and analyzed.
CitationEck, C. and Nakano, F., "Robust DPF Regeneration Control for Cost-Effective Small Commercial Vehicles," SAE Technical Paper 2017-24-0123, 2017, https://doi.org/10.4271/2017-24-0123.
Data Sets - Support Documents
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