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Real Driving Emission Efficiency Potential of SDPF Systems without an Ammonia Slip Catalyst DENSO Automotive Deutschland GmbH

SAE Technical Papers (1906-current) Available online

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Format:
Conference/Event
Author/Creator:
Georgiadis, Georgiadis, author.
Contributor:
Hagen, Juergen
Herrmann, Olaf
Kudo, Toru
Uchiyama, Ken
Conference Name:
WCX 17: SAE World Congress Experience (2017-04-04 : Detroit, Michigan, United States)
Language:
English
Physical Description:
1 online resource
Place of Publication:
Warrendale, PA SAE International 2017
Summary:
AbstractIn order to comply with emission regulation, reach their profitability targets and minimise the in-use cost of their vehicles, OEMs are seeking solutions to optimise their aftertreatment systems. For Selective Catalytic Reduction (SCR) system engineers, one of the most important challenges is to reduce the system's cost, while keeping its high level of NOx emission reduction performance. Ways to achieve this cost reduction include 1. using an engine out NOx estimation model instead of a NOx sensor upstream of the SDPF (DPF coated with SCR) catalyst and 2. eliminating the Ammonia Slip Catalyst (ASC) downstream of the SDPF catalyst. Achieving these challenging targets requires actions on the complete SCR system, from the optimisation of mixing and uniformity in the SDPF catalyst to the development of robust controls. To face these challenges, a novel exhaust reverse flow concept with a blade mixer was developed. With this concept, total mixing length and mixing performance were increased. The SDPF catalyst uniformity during transient and steady state operation of the engine has been investigated. On the controls side, robust engine out NOx, advanced ammonia storage modelling and robust system adaption controls were developed. Finally, the robustness of the whole SCR system against tolerances and errors through extended sensitivity analysis has been evaluated
Notes:
Vendor supplied data
Publisher Number:
2017-01-0913
Access Restriction:
Restricted for use by site license

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