Recommended
More Related Content
Similar to Holistic Approach for EU7, OBM, OBD and Controls for Diesel Emission
Similar to Holistic Approach for EU7, OBM, OBD and Controls for Diesel Emission (20)
Recently uploaded
Recently uploaded (18)
Holistic Approach for EU7, OBM, OBD and Controls for Diesel Emission
- 1. IAV 10/2021 Marco Moser, IAV - TP-D4 - Holistic OBM/OBD/Controls Holistic Approach for OBM, OBD and Controls for Diesel Emissions Marco Moser, IAV, Berlin
- 2. Model PN Model CH2O Motivation Challenges … Possible EU7 Exhaust Treatment OBM IAV 10/2021 Marco Moser, IAV - TP-D4 - Holistic OBM/OBD/Controls 2 Model HC Model CO Engine DOC DPF SCR SCR ASC NOx Model PM Model NOX T T CH4 HC CO NOX NH3 N2O dp PM CH2O NOx T NOx PM T PN ? Sensor-based emission monitoring difficult due to missing and none continuous measuring sensors Potential: sensor-adapted model-based approach OBD limits for EU7 and OBM tolerances so far not defined OBM tolerances main focus and enabler NH3 NH3 Model CH4 no HC sensor no CO sensor PM sensor not continuous NOX sensor readiness and cross sensitivity no CH4 sensor no CH2O sensor currently not all models available new new new new new Path to model proposals for EU7 / OBM particles from tires / brakes
- 3. Content IAV 10/2021 Marco Moser, IAV - TP-D4 - Holistic OBM/OBD/Controls 3 Engine 5. Nitrogen Oxides Model 4. Particulate Matter Model 1. Engine Out Emission Model 2. Engine Out Validation 3. Carbon Oxidation Model 6. Tailpipe Emission Validation Emissions
- 4. Model PN 1. Engine Out Emission Models Physico-Chemical / Machine Learning IAV 10/2021 Marco Moser, IAV - TP-D4 - Holistic OBM/OBD/Controls 4 Model HC Model CH2O Model CO Engine DOC DPF SCR SCR ASC Model PM Model NOX Model CH4 Engine Output 1. NOx 2. CO 3. HC 4. Soot 5. T31 6. pmi 7. … Excitation 1. nM 2. mFuel 3. p2 4. SOI 5. EGR 6. pRail 7. Swirl DoE-Model (Volterra or Gauß etc.) Map-based / Physico-Chemical Machine Learning Expert Knowledge Features and / or NH3 NH3 CH4 HC CO NOX NH3 N2O PM CH2O PN SciML – scientific machine learning physical loss function physics-informed neural network
- 5. 1. Engine Out Emission Models Machine Learning from Expert Knowledge IAV 10/2021 Marco Moser, IAV - TP-D4 - Holistic OBM/OBD/Controls 5 *.dat *.dat *.dat *.dat *.dat *.dat *.dat *.dat *.dat *.dat *.dat *.dat *.dat *.dat *.dat *.dat *.dat *.dat Proven in series: Machine learned emission models through engineering from physico chemical knowledge Environment Knowledge Measurements Data Analysis Emission Models Meta Model Cross Correlation Machine Learning Risk: Sensor tolerances Humidity sensor physico chemical models for well-known correlations Environment O2 from sensor or high-class model boosts accuracy boosts accuracy
- 6. 1. Engine Out Emission Models Machine Learning – Examples (NOX engine out) IAV 10/2021 Marco Moser, IAV - TP-D4 - Holistic OBM/OBD/Controls 6 Higher accuracy compared to "state of the art" approaches < ± 20% @ 2σ (95,45%) • for altitude: 0m … 4000m // ambient temperature: -30°C … +50°C General approach, applicable for different emissions or sensors Proven in series application Emission measurements on Altitude-Climate-Test-Bench: Environment variation: altitude temperature humidity WLTC
- 7. 1. Engine Out Emission Models Machine Learning – Examples (CO, Soot) IAV 10/2021 Marco Moser, IAV - TP-D4 - Holistic OBM/OBD/Controls 7 Machine learned emission models for all emissions possible (HC, CH4, CH2O, CO2, O2) accuracy requirements might be challenging CO Soot WLTC WLTC
- 8. dp Model PN 2. Engine Out Validation Sensors for Tolerance Detection IAV 10/2021 Marco Moser, IAV - TP-D4 - Holistic OBM/OBD/Controls 8 Model HC Model CH2O Model CO Engine DOC DPF SCR SCR ASC NOx Model PM Model NOX Model CH4 T fault detection of engine and emission adaption Engine-out sensors are essential to detect engine specific tolerances and correct raw emission models NH3 NH3 CH4 HC CO NOX NH3 N2O PM CH2O PN
- 9. 2. Engine Out Validation Sensors as Fix Point for Model Correction IAV 10/2021 Marco Moser, IAV - TP-D4 - Holistic OBM/OBD/Controls 9 Emission [] λ [] NOX Soot CO HC 1 6 Cross correlation • change in NOX change in HC, CO, soot, CH4, CH2O … to adapt models Exact engine out models required raw emission models created for norm case tolerances of engine and surrounding components not completely modelable Reliable sensor layout what is needed to detect tolerances and failures NOX the only available emission sensor to detect engine failures but needs confirmation: Alternatives 2 NOX sensors (or 3?) NOX and PM (only if for high soot ready) … dpDPF sufficient sensitivity? NOX and lambda lambda probe with sensitivity to more than O2?
- 10. Model PN 3. Carbon Oxidation Model Modeling Carbon Oxidation IAV 10/2021 Marco Moser, IAV - TP-D4 - Holistic OBM/OBD/Controls 10 Model HC Model CH2O Model CO Engine DOC DPF SCR SCR ASC NOx Model PM Model NOX Model CH4 T T Conversion HC,CO,CH4,CH2O fault detection DOC OBM adaption T T fault detection ASC OBM adaption Conversion HC,CO,CH4,CH2O NH3 NH3 Accurate temperature sensor recommended for exothermic modeling carbon compound oxidation CH4 HC CO NOX NH3 N2O PM CH2O PN dp
- 11. 3. Carbon Oxidation Model Modeling Carbon Compound Oxidation IAV 10/2021 Marco Moser, IAV - TP-D4 - Holistic OBM/OBD/Controls 11 Adaptation of carbon compound models: Extended Kalman Filter for parameter estimation (e.g. estimation aging factor) https://dieselnet.com/tech/catalyst_methane_oxidation.php 1D-Flow-through catalyst model Measurement of exothermic Technical knowledge closed loop different species different behavior
- 12. Model PN 4. Particulate Matter Model Soot Mass Model IAV 10/2021 Marco Moser, IAV - TP-D4 - Holistic OBM/OBD/Controls 12 Model HC Model CH2O Model CO Engine DOC DPF SCR SCR ASC NOx Model PM Model NOX Model CH4 T T dp fault detection DPF OBM adaption Conversion PM,PN T PM T fault detection DPF OBM adaption NH3 NH3 PM sensor detects defect DPF if soot is measured, DPF is defect dp sensor for soot mass measurement HC/CO emissions during DPF regeneration CH4 HC CO NOX NH3 N2O PM CH2O PN ?
- 13. 4. Particulate Matter Model Filtering Model IAV 10/2021 Marco Moser, IAV - TP-D4 - Holistic OBM/OBD/Controls 13 1D Filtering model simplified Flow pattern: Flow through Simplified pressure drop model Simplified soot deposition model and axial distribution Simplified soot reactivity Adaption of filter efficiency (PM and PN) Measurement of - pressure drop - particle mass Technical knowledge • dp sensor for detecting defects and soot cumulation • PM sensor measures non-continuous interval-based diagnostics
- 14. Model PN 5. Nitrogen Oxides Model Modeling Nitrogen Oxides IAV 10/2021 Marco Moser, IAV - TP-D4 - Holistic OBM/OBD/Controls 14 Model HC Model CH2O Model CO Engine DOC DPF SCR SCR ASC NOx Model PM Model NOX Model CH4 T T Conversion NOX,NH3,N2O dp NOx T PM T NOx fault detection SCR OBM adaption NH3 NH3 models required for all SCR components CH4 HC CO NOX NH3 N2O PM CH2O PN
- 15. 5. Nitrogen Oxides Model SCR Modeling Approaches IAV 10/2021 Marco Moser, IAV - TP-D4 - Holistic OBM/OBD/Controls 15 Physical models, data driven models and smart combinations can be used for SCR modeling Reaction Rates: Modeled Reactions: ̇ 𝑟𝑟𝑎𝑎𝑎𝑎𝑎𝑎 = 𝑘𝑘0,𝑎𝑎𝑎𝑎𝑎𝑎 exp −𝐸𝐸𝑎𝑎𝑎𝑎𝑎𝑎/(𝑅𝑅 𝑇𝑇𝑜𝑜𝑜𝑜𝑜𝑜) 1 − 𝜃𝜃 𝑐𝑐𝑁𝑁𝑁𝑁𝑁 NH3 adsorption ̇ 𝑟𝑟𝑑𝑑𝑑𝑑𝑑𝑑 = 𝑘𝑘0,𝑑𝑑𝑑𝑑𝑑𝑑 exp −𝐸𝐸0,𝑑𝑑𝑑𝑑𝑑𝑑(1 − Ω 𝜃𝜃)/(𝑅𝑅𝑇𝑇𝑜𝑜𝑜𝑜𝑜𝑜) 𝜃𝜃 NH3 desorption ̇ 𝑟𝑟𝑁𝑁𝑁𝑁 = 𝑘𝑘0,𝑁𝑁𝑁𝑁 exp −𝐸𝐸𝑁𝑁𝑁𝑁/(𝑅𝑅� 𝑇𝑇𝑆𝑆𝑆𝑆𝑆𝑆) 𝑐𝑐𝑁𝑁𝑁𝑁3,𝑠𝑠 𝑐𝑐𝑁𝑁𝑁𝑁 Standard SCR ̇ 𝑟𝑟𝑁𝑁𝑁𝑁𝑁𝑁 = 𝑘𝑘0,𝑁𝑁𝑁𝑁𝑁𝑁 exp −𝐸𝐸𝑁𝑁𝑁𝑁𝑁𝑁/(𝑅𝑅� 𝑇𝑇𝑆𝑆𝑆𝑆𝑆𝑆) 𝑐𝑐𝑁𝑁𝑁𝑁𝑁,𝑠𝑠 𝑐𝑐𝑁𝑁𝑁𝑁 𝑐𝑐𝑁𝑁𝑁𝑁𝑁 Fast SCR ̇ 𝑟𝑟𝑁𝑁𝑁𝑁𝑁 = 𝑘𝑘0,𝑁𝑁𝑁𝑁𝑁 exp −𝐸𝐸𝑁𝑁𝑁𝑁𝑁/(𝑅𝑅� 𝑇𝑇𝑆𝑆𝑆𝑆𝑆𝑆) 𝑐𝑐𝑁𝑁𝑁𝑁𝑁,𝑠𝑠 𝑐𝑐𝑁𝑁𝑁𝑁𝑁 Slow SCR ̇ 𝑟𝑟𝑂𝑂𝑂𝑂 = 𝑘𝑘0,𝑂𝑂𝑂𝑂 exp −𝐸𝐸𝑂𝑂𝑂𝑂/(𝑅𝑅� 𝑇𝑇𝑆𝑆𝑆𝑆𝑆𝑆) 𝑐𝑐𝑁𝑁𝑁𝑁𝑁,𝑠𝑠 NH3 Oxidation State Equations: 1) 𝑑𝑑𝑐𝑐𝑁𝑁𝑁𝑁𝑁,𝑠𝑠 𝑑𝑑𝑑𝑑 = ̇ 𝑟𝑟𝑎𝑎𝑎𝑎𝑎𝑎 − ̇ 𝑟𝑟𝑑𝑑𝑑𝑑𝑑𝑑 − ̇ 𝑟𝑟𝑁𝑁𝑁𝑁 − ̇ 𝑟𝑟𝑁𝑁𝑁𝑁𝑁𝑁 − ̇ 𝑟𝑟𝑁𝑁𝑁𝑁𝑁 − ̇ 𝑟𝑟𝑂𝑂𝑂𝑂 2) 𝑑𝑑𝑓𝑓𝑖𝑖𝑖𝑖𝑖𝑖 𝑑𝑑𝑑𝑑 = 0 3) 𝑑𝑑𝑐𝑐𝑁𝑁𝑁𝑁𝑁 𝑑𝑑𝑑𝑑 = 0 = 𝑣𝑣𝑠𝑠 𝑓𝑓𝑖𝑖𝑖𝑖𝑖𝑖 𝑐𝑐𝑁𝑁𝑁𝑁𝑁,𝑖𝑖𝑖𝑖 − 𝑐𝑐𝑁𝑁𝑁𝑁𝑁 − ̇ 𝑟𝑟𝑎𝑎𝑎𝑎𝑎𝑎 + ̇ 𝑟𝑟𝑑𝑑𝑑𝑑𝑑𝑑 4) 𝑑𝑑𝑐𝑐𝑁𝑁𝑁𝑁 𝑑𝑑𝑑𝑑 = 0 = 𝑣𝑣𝑠𝑠 𝑐𝑐𝑁𝑁𝑁𝑁,𝑖𝑖𝑖𝑖 − 𝑐𝑐𝑁𝑁𝑁𝑁 − ̇ 𝑟𝑟𝑁𝑁𝑁𝑁 − 0.5 ̇ 𝑟𝑟𝑁𝑁𝑁𝑁𝑁𝑁 5) 𝑑𝑑𝑐𝑐𝑁𝑁𝑁𝑁𝑁 𝑑𝑑𝑑𝑑 = 0 = 𝑣𝑣𝑠𝑠 𝑐𝑐𝑁𝑁𝑁𝑁𝑁,𝑖𝑖𝑖𝑖 − 𝑐𝑐𝑁𝑁𝑁𝑁𝑁 − 0.75 ̇ 𝑟𝑟𝑁𝑁𝑁𝑁𝑁 − 0.5 ̇ 𝑟𝑟𝑁𝑁𝑁𝑁𝑁𝑁 Low-Dimensional Physical Models • models derived from first order physical and chemical principles • all essential phenomena are modeled • real-time capability is often achieved by 0D-modeling or quasi 1D-modeling (multi-brick models) Gelbert, MTZ 2/2017 ML-Models • pure machine learn models are derived from data only • better models can be created with additional usage of expert knowledge • also combinations of physical and ML-models can be useful ML-Model 1: Temperatures • NARX model for gas and wall temperatures in SCR ML-Model 2: NH3-Filling Level: • NARX model for NH3- Filling Level Estimate ML-Model 3: Gas Concentrations: • FNN models for NO, NO2, NH3,… Inputs Outputs März, Emission Control Science and Technology 6/2020
- 16. Model PN 6. Tailpipe Emission Validation Sensors as Fix Point IAV 10/2021 Marco Moser, IAV - TP-D4 - Holistic OBM/OBD/Controls 16 Model HC Model CH2O Model CO Engine DOC DPF SCR SCR ASC NOx Model PM Model NOX Model CH4 T T Conversion HC,CO,CH4,CH2O Conversion NOX,NH3,N2O dp Conversion PM,PN NOx T NOx PM fault detection DPF OBM adaption fault detection SCR OBM adaption Conversion HC,CO,CH4,CH2O T fault detection ASC OBM adaption NH3 NH3 at least 1 sensor has to be redundant as a fixed point NOX sensor most sensible candidate CH4 HC CO NOX NH3 N2O PM CH2O PN
- 17. New sensor diagnose - NOX tailpipe concentration very low - Using of sensor internal controls to prove sensor accuracy (not available at the moment) 6. Tailpipe Emission Validation Sensors as Fix Point IAV 10/2021 Marco Moser, IAV - TP-D4 - Holistic OBM/OBD/Controls 17 NOX sensor tailpipe as fix point Sensor redundancy 2 NOX sensors (or 3 ?) NOX and NH3 using cross correlation NOX and Lambda using cross correlation NOX Dosimeter (add) source: https://www.cpk-automotive.com/ high accuracy at low NOX in development with different manufacturers
- 18. Subtask Status Assessment Engine out models: • Accuracy depends on data availability • external measurement equipment Carbon: HC, CO, CH2O, CH4 • Accuracy depends on different conversion rates for the different species Particle: PM, PN • PN modeling very difficult Nitrogen: NOx, NH3, N2O • Modeling of the different species very challenging • NOX tailpipe sensor helps Assessment IAV 10/2021 Marco Moser, IAV - TP-D4 - Holistic OBM/OBD/Controls 18 DOC Engine SCR/ASC DPF-SCR DOC Engine SCR/ASC DPF-SCR DOC Engine SCR/ASC DPF-SCR T T PM PN dP PM CH2O CO HC NOX NH3 NOx NOx NOx N2O CH4 T T DOC Engine SCR/ASC DPF-SCR
- 19. Summary IAV 10/2021 Marco Moser, IAV - TP-D4 - Holistic OBM/OBD/Controls 19 Subsystem specific assessment of model, available sensors and closed-loop approach • Modelling of emissions from engine to tailpipe required Different approaches available for engine and EAT components ECU Hardware EAT Sys. 1 Engine S Model 1 with Tolerance Adaption S States Emission Aging Raw Emission Models EAT Sys. 2 EAT Sys. 3 NOx NH3 HC N2O PM CH4 CO PN HC HO S S Model 2 with Tolerance Adaption Model 3 with Tolerance Adaption S Sensor(s) • Continuous adaption of emission models based on sensors Open-loop models cannot cover full range of vehicle lifecycle • Closed-loop control and adaption algorithms Compensation of system tolerances or model inaccuracies
- 20. Contact Marco Moser IAV GmbH Carnotstrasse 1, 10587 BERLIN (GERMANY) Phone +49 30 3997-89176 marco.moser@iav.de www.iav.com Philipp Brinkmann, philipp.brinkmann@iav.de Dr. Gregor Gelbert, gregor.gelbert@iav.de Torsten Hein, torsten.hein@iav.de Steve Kipping, steve.kipping@iav.de Patrick Stracke, patrick.stracke@iav.de Paul Tourlonias, paul.tourlonias@iav.de IAV 10/2021 Marco Moser, IAV - TP-D4 - Holistic OBM/OBD/Controls