Simultaneous NOx and NH3 slip prediction in a SCR catalyst under real driving conditions including potential urea injection failures Article Swipe
Benjamín Plá
,
Pedro Piqueras
,
Pau Bares
,
André Nakaema Aronis
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1177/14680874211007646
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1177/14680874211007646
To reach the emission limits imposed by governments and reduce the negative impact on the environment, the use of aftertreatment systems has become essential for internal combustion engine (ICE) based powertrains. In particular, the selective catalytic reduction (SCR) system is a widespread aftertreatment technology with high efficiency for [Formula: see text] abatement which shows complex dynamics and requires urea injection as reducing agent. Current urea injection strategies usually rely on the [Formula: see text] emissions feedback. This work presents a model for the on-line simultaneous prediction of [Formula: see text] and [Formula: see text] emissions after the SCR catalyst, allowing the emissions estimation even in conditions of urea injector failure, when it is not possible to rely on the injector feedback signal. The proposed model is based on state of the art on-board after-treatment instrumentation and proposes an extended Kalman filter (EKF) to combine a data-based model and the analysis of sensor signals to provide a reliable estimation of [Formula: see text] and [Formula: see text] slip. The proposed strategy is experimentally assessed in dynamic driving cycles, such as Worldwide harmonised Light vehicles Test Cycle (WLTC) and Standardised Random Test (RTS). The proposed method is evaluated in standard conditions (without failures) and with urea injection failures of 25% and 120% of the nominal injection amount. As a result, the prediction on [Formula: see text] and [Formula: see text] slip has been improved in all injection failure conditions, by an overall average of 47.8% and 61.8%, respectively, when compared to state-of-the-art control oriented models (physically based zero dimensional model or data-based).
Related Topics
Concepts
Fuel injection
NOx
Extended Kalman filter
Slip (aerodynamics)
Computer science
Urea
Automotive engineering
Selective catalytic reduction
Powertrain
Control theory (sociology)
Combustion
Environmental science
Chemistry
Simulation
Kalman filter
Catalysis
Engineering
Thermodynamics
Physics
Organic chemistry
Torque
Control (management)
Biochemistry
Artificial intelligence
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1177/14680874211007646
- OA Status
- green
- Cited By
- 20
- References
- 43
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3140454555
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3140454555Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1177/14680874211007646Digital Object Identifier
- Title
-
Simultaneous NOx and NH3 slip prediction in a SCR catalyst under real driving conditions including potential urea injection failuresWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-04-02Full publication date if available
- Authors
-
Benjamín Plá, Pedro Piqueras, Pau Bares, André Nakaema AronisList of authors in order
- Landing page
-
https://doi.org/10.1177/14680874211007646Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://hdl.handle.net/10251/195299Direct OA link when available
- Concepts
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Fuel injection, NOx, Extended Kalman filter, Slip (aerodynamics), Computer science, Urea, Automotive engineering, Selective catalytic reduction, Powertrain, Control theory (sociology), Combustion, Environmental science, Chemistry, Simulation, Kalman filter, Catalysis, Engineering, Thermodynamics, Physics, Organic chemistry, Torque, Control (management), Biochemistry, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
20Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 6, 2023: 5, 2022: 5, 2021: 1Per-year citation counts (last 5 years)
- References (count)
-
43Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| publication_date | 2021-04-02 |
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