Model based development and calibration Innovative ways to increase calibration quality within the limits of acceptable development effort!

Transcription

1 Model based development and calibration Innovative ways to increase calibration quality within the limits of acceptable development effort! 1

2 MODEL BASED DEVELOPMENT Challanges 2

3 EUROPEAN EMISSION LEGISLATION PASSENGER CARS Next steps EC/ JRC: Definition of PEMS boundaries (temperature, idling, altitude, inclination, ) Beginning of 213: Screening of small fleet PEMS most likely to come, methodology to be defined until mid 213 Definition how EC will survey ISC (in addition to OEM tests) 3

4 ENGINE SPEED / LOAD DISTRIBUTION EXAMPLES OF REAL WORLD DRIVING VS. TEST 1 <1 km/h dynamic Driver RDE PEMS 8 6 NEDC Engine Speed, rpm WLTP 8 3 <1 km/h moderate Driver 2 Relative Load, % Relative Load, % 7 9 Relative Load, % Engine Speed, rpm Engine Speed, rpm 4

5 Load Collective NEDC vs. RDE Md - Nm RandomTest Random Test PEMS: all modes possible Options for RDE: Random test cycle: Chassis Dyno Simulation PEMS: Measurement in customer driving with PEMS NEDC Decision open N - upm 5

6 MODEL BASED DEVELOPMENT Intention 6

7 Challenges in the Powertrain Development and AVLs Solutions CO2 / Fuel Consumption Real Driving Emissions Reduction of development costs Increased system complexity (EAS, OBD, Hybridization) 1 Keep quality standards Reduction of development time Broad Vehicle Portfolio AVL Solutions Clustering of hardware and engineering activities Multi-variant simulation with the same engine family (RDE, OBD, EAS) Reduced test facilities/variant through front-loading 1 Improved quality and robustness through additional virtual validation Independence of environmental testing from seasonal conditions and vehicles availability Model-Based Development 7

8 Model Based Development What is it? Model based development using a real time capable engine model Starting from concept phase until SOP calibration Engine model based on semi-physical modeling approach empirical model components derived from AVL experience and test bed data physical components increase the range of application due to better extrapolation Easy usability due to the use of suitable simulation environments Increasing system robustness within given development duration and budget by transferring development from real to virtual testing 8

9 Definitions - Model Accuracy Levels Maturity Level Description Use Cases Level 1 Only the main geometrical data of the engine are used as input for model set-up Concept study and decision ECU algorithm design Exhaust gas aftertreatment (EAS) concept Level 2 Level 3 Measurement data is used to make a refinement of the model to increase accuracy. Model is adapted to steady state and transient data, measured at AVL. Highest accuracy which is needed for model based calibration. Pre-Calibration: the possible calibration tasks depends on focus of the model parameterization Used for specific calibration tasks Variant calibration support Ambient correction calibration (altitude/hot/cold) EAS calibration strategy OBD calibration support Robustness investigations ECU algorithm verification 9

10 Virtual Model based Development Modelling Process MoBEO basic model setup Semi-physical Basic Model without measurement data MoBEO refined model setup Semi-physical Thermodynamic NOx-Emission EAS System (DOC, DPF, SCR, NLT) HC, CO, Soot, SPL, Combinedmodel Increased Empirical number of engine static global specific outputs Model refinement HiL Setup MiL Setup Model-based calibration of various variants Variant specific hardware change (e.g. intake piping, ) (No combustion HW change) Robustness analysis Pre-calibration Testbed results DoE Test Results Pre-calibration Field data Emission validation First engine run Base engine testbed development DoE Measurements Field data Environmental validation Real 1

11 Model Accuracy High model accuracy as base for model based calibration Engine speed [%] Intake air mass flow [%] Temp. turb. inlet [ C] Measurement Simulation time [s] Tourque [%] NOx Cconcentration [ppm] Cycle results NOx [mg/km] Zyklus Ergebnis NOx [mg/km] Cycle results - NEDC Typical deviations of the cycle emissions and fuel consumption as well as achievable temperature accuracy: Cycle Kraftstoffverbrauch fuel consumption [L/km] [mg/km] Fuel Consumption < 3% NOx Emission < 5% Insoluble Particulate Emission < 1% Temperature Intake Side < 1 C Temperature Exhaust Side < 2 C 11

12 Engine Speed [%] Intake Air Massflow [%] Opacity [%] Model Accuracy High model accuracy as base for model based calibration Measurement Simulation Time [s] Torque [%] NOx Concentration [ppm] T. Turbine-Inlet [ C] Typical deviations of the cycle emissions and fuel consumption as well as achievable temperature accuracy: Fuel Consumption < 3% NOx Emission < 5% Insoluble Particulate Emission < 1% Temperature Intake Side < 1 C Temperature Exhaust Side < 2 C 12

13 MODEL BASED DEVELOPMENT Application Environment 13

14 Model Based Development Virtual test beds Comparison Simulation Environment Model in the Loop (MiL) Advantages + Simulation faster than real time (app. 5 times) + No hardware parts needed + Simulation on normal PC possible Disadvantages - Setup of software ECU time consuming - typically not all ECU functionalities available - Hard to achieve equal control behaviour as real engine Hardware in the Loop (HiL) Advantages + All ECU functions available + Pre-Calibration of all ECU functions possible + Possibility of ECU software and dataset validation Disadvantages - Only real time simulation possible - Need of hardware in the loop test bed - Need of hardware parts Both environments can be used for pre-calibration of specific tasks 14

15 Suitable Application Environment As Prerequisite to Integrated a Model Based Calibration Methodology in an exciting Application Team Standard Hardware-in-the-Loop test bed HiL operation system HiL automation system Calibration software (INCA,..) Advanced AVL Hardware-in-the-Loop test bed extended with Advanced semi-physical powertrain model PUMA & CAMEO Same interface for the calibration engineer as real test bed PUMA Host Connection Simulation results stored in same format as real test data Post-Processing Same CONCERTO Layouts can be used for data analyzing Calibration Software (INCA, ) 15

16 Application From Virtual Test Bed to SOP Virtual Test Beds as Extension of Real Test Facilities Calibration Load Profiles Environment Production Tolerances Aging Effects Test Bed Virtual Test Bed Vehicle Validation 16

17 MODEL BASED DEVELOPMENT Use - Cases 17

18 Model Based Development Concept Investigations Model based concept investigations Assessment of technology route Simulation of transient behaviour of engine in early concept phase on MiL environment Definition of possible concepts considering the interaction between engine exhaust aftertreatment system software and calibration Sensors and actuators environmental conditions Vehicle & drivetrain simulation 18

19 Model Based Development Concept Investigations DPF Soot Loading HDDE 2ltr/cyl. class EGR + DPF Boundary conditions: Tier4i engine NO X /soot ratio < 1 [g/g] DPF Loading Limit active Regeneration If NO X /soot ratio increases DPF soot load in balance point will be lowered. Low engine out total soot emissions allows also for conditions were no CRT effect is observable a long operation without DPF regeneration. Soot Model accuracy can be reduced to a worst case scenario. passive Regeneration Simulation of specific duty cycles for different applications with respect to DPF soot loading behavior on HiL system Calibration validation 19

20 Model Based Development Calibration of Ambient Corrections Simulation of full load altitude operation for validation of ambient correction and engine protection functions 97mbar = 35m (Graz) 75mbar = 25m 66mbar = 35m 54mbar = 5m Limits for component protection Temp. upstr.turbine [ C] Pressure upstr. Turbine [kpa] HP TC Speed [rpm] Limit temperature upstream turbine Limit temperature downstream compressor Limit pressure upstream turbine Limit LP turbochargerspeed Limit HP turbochargerspeed LP TC Speed [rpm] Temp. ds. Compressor [ C] Engine Speed [1/min] No derating up to 25 m BMEP [kpa] 2

21 Model Based Development Calibration of Component Protection Functions Simulation of engine failure at full load for validation of engine protection functions Limits for component protection HP TC Speed [rpm] Pressure upstr. Turbine [kpa] Temp. upstr.turbine [ C] Limit temperature upstream turbine Limit temperature downstream compressor Limit pressure upstream turbine Limit LP turbochargerspeed Limit HP turbochargerspeed Temp. ds. Compressor [ C] LP TC Speed [rpm] Engine Speed [1/min] BMEP [kpa] 21

22 Ideas for Application of Model-Based-Development in OBD Calibration Validation of calibration Functional check of calibration (Tested-Flag, P- Codes, etc.) Check of IUMPRs at different driving profiles (e.g. using same calibration for a commercial truck and a city bus) Robustness investigation and tolerances Pre-calibration Pre-calibration of thresholds, enable/release conditions, debouncing Simulation of fault-parts (e.g. EGR-orifice, broken DPF, etc.) Multi-Variant calibration (e.g. adjustment from lead-calibration to a follow-up variant) Evaluation and R&D projects currently ongoing 22

23 Calibration on Hardware-in-the-Loop test beds Virtual Test Beds as Extension of Real Test Facilities Calibration Driving Cycle Environment Powertrain calibration tasks for HiL test bed Pre-calibration of different calibration work packages Calibration for non-standard ambient conditions Calibration of component protection Vehicle/Engine derivate calibration RDE Real Driving Emission evaluation Real world fuel consumption optimization Sensitivity studies taking into account system interactions Production Tolerances Aging Effects Software and dataset validation 23

24 Front-Loading Example Ideal Lead Variant Calibration Project (i.e. no relevant H/W changes) Facilities HiL Engine Testbed Chassy Dyno Road 24

25 Front-Loading Example Ideal Lead Variant Calibration Project (i.e. no relevant H/W changes) Facilities HiL Engine Testbed Chassy Dyno Road Multi-variant projects can be addressed by: an extension of the test environment through HiL (MiL/SiL) Testing Keep calibration quality through additional HiL testing, though high number of variants Multi-variant simulation (calibration clustering, RDE, EAS, OBD) Keep test facilities usage by a feasible level Make environmental testing more flexible and efficient 25

26 Calibration Process and Dataset Management Cal. Quality conventional Calibration process model based Calibration process G5 Validated AVL Diesel-Calibration Quality Gates G4 Robust G3 Target Achievement G2 - Refined G1 - Runnable Validation by: SOt 1. Dataset Management (Dataset Merging, Clustering, Tracing) 2. Automated HiL Dataset validation und consideration of different production tolerances in advance to dataset freeze 3. A extensive model based fleet validation with active search for critical events Time Month before SOP 26

27 Conclusion - Innovative ways to increase calibration quality within the limits of acceptable development effort! Shifting of development tasks in earlier phases and well proven concept decisions Reduction of project duration due to additional virtual test facilities Independent from environmental conditions Independent from vehicle availability Higher efficiency for real testing High dataset quality due to good monitoring and solid validation Dataset - Quality Test bed development Vehicle development Fleet validation model based Concept Test bed development Vehicle development Fleet validation Concept Test bed development Vehicle development Fleet validation 27

28 AVL LIST GMBH Your Independent Engineering Partner Thank you for your attention 28