This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Model Based Calibration Generation for Gasoline Particulate Filter Regeneration
Technical Paper
2021-01-0600
ISSN: 0148-7191, e-ISSN: 2688-3627
This content contains downloadable datasets
Annotation ability available
Sector:
Event:
SAE WCX Digital Summit
Language:
English
Abstract
Gasoline Particulate Filters (GPF) are widely employed in exhaust aftertreatment systems of gasoline engines to meet the stringent particulate emissions requirements of Euro 6 and China 6 standard. Optimization of GPF performance requires a delicate trade-off between fuel economy, engine performance and drivability. This results in a complex lengthy and iterative calibration development process which uses a lot of hardware resources. To improve the calibration process and reduce hardware testing, physics-based modeling of the GPF system is used. A 1-D chemical model supplemented with 3D CFD solver is utilized to evaluate pressure drop and soot burning performance characteristics of the GPF under engine dynamometer test conditions. The chemical kinetics of soot burning for the 1D model is developed using test data obtained from well controlled laboratory environment. Later, the model was applied to engine dynamometer conditions where it demonstrated robust pressure drop and soot burning predictions against test data under various exhaust flow rate, temperature and soot load conditions. The validation of this 1-D kinetic model enabled us to come up with a new method for the generation of optimized calibration maps for GPF control system in the vehicle. Compared to the conventional calibration method, the new physics-based calibration generation method uses less test data. This results in faster calibration development process at a lower cost as dependency on complicated testing in engine dynamometer and development vehicles is reduced.
Recommended Content
Authors
Topic
Citation
Varia, A., Paramadhayalan, T., Yadav, A., Kannan, R. et al., "Model Based Calibration Generation for Gasoline Particulate Filter Regeneration," SAE Technical Paper 2021-01-0600, 2021, https://doi.org/10.4271/2021-01-0600.Data Sets - Support Documents
Title | Description | Download |
---|---|---|
Unnamed Dataset 1 | ||
Unnamed Dataset 2 |
Also In
References
- Parks , J. , Storey , J. , Prikhodko , V. , Debusk , M. et al. Filter-Based Control of Particulate Matter from a Lean Gasoline Direct Injection Engine SAE Technical Paper 2016-01-0937 2016 https://doi.org/10.4271/2016-01-0937
- European Commission
- Saito , C. , Nakatani , T. , Miyairi , Y. , Yuuki , K. et al. New Particulate Filter Concept to Reduce Particle Number Emissions SAE Technical Paper 2011-01-0814 2011 https://doi.org/10.4271/2011-01-0814
- Nieuwstadt , M.V. , and Ulrey , J. Control Strategies for Gasoline Particulate Filters SAE Technical Paper 2017-01-0931 https://doi.org/10.4271/2017-01-0931
- Bissett , E.J. Mathematical Model of the Thermal Regeneration of a Wall-Flow Monolith Diesel Particulate Filter Chemical Engineering Science 39 1233 1244 1984
- Konstandopoulos , A.G. , and Kostoglou , M. Reciprocating Flow Regeneration of Soot Filters Combustion and Flame 121 488 500 2000
- Arunachalam , H. , Pozzato , G. , Hoffman , M.A. , Onori , S. Modeling the Thermal and Soot Oxidation Dynamics Inside a Ceria-Coated
- Takahashi , A. , Korneev , S. , and Onori , S. Sensitivity Study on Thermal and Soot Oxidation Dynamics of Gasoline ParticulateFilters SAE Technical Paper 2019-01-0990 2019 https://doi.org/10.4271/2019-01-0990
- Boger , T. , Rose , D. , Nicolin , P. , Coulet , B. et al. Severe Soot Oxidations in Gasoline Particulate Filter Applications SAE Technical Paper 2018-01-1699 2018 https://doi.org/10.4271/2018-01-1699
- Straten , T. and van den Berk , J. Model-Based Approach for Calibration and Validation by Simulation of Emission Control Solutions for Next Generation Off-Road Vehicles SAE Technical Paper 2011-01-0309 https://doi.org/10.4271/2011-01-0309
- Langeheinecke , K. , Schrade , F. , Dusemund , S. , Berner , T. et al. Virtual Exhaust-Gas Aftertreatment Test Bench - A Contribution to Model-Based Development and Calibration of Engine Control Algorithms SAE Technical Paper 2012-01-0897 https://doi.org/10.4271/2012-01-0897
- Cloudt , R. , Saenen , J. , van den Eijnden , E. and Rojer , C. Virtual Exhaust Line for Model-Based Diesel Aftertreatment Development SAE Technical Paper 2010-01-0888
- GT-Power 2019 Gamma Technologies 2019
- Field , M.A. , Gill , D.W. , Morgan , B.B. and Hawksley , P.G.W. Combustion of Pulverized Coal Banbury Cheroy and Sons Ltd. 1967
- Koteswara Rao , S. , Imam , R. , Ramanathan , K. , and Pushpavanam , S. Sensitivity Analysis and Kinetic Parameter Estimation in a Three Way Catalytic Converter 10.1021/ie801244w
- Nicolin , P. , Rose , D. , Kunath , F. , and Boger , T. Modeling of the Soot Oxidation in Gasoline Particulate Filters SAE Technical Paper 2015-01-1048 https://doi.org/10.4271/2015-01-1048
- 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
- Rask , E. , and Sellnau , M. Simulation-Based Engine Calibration SAE Technical Paper 2004-01-1264 https://doi.org/10.4271/2004-01-1264