Engine modelling and optimisation for RDE
Overview
The need to consider system influences on engine performance for RDE
How can we achieve this?
Component selection Simulation requirements Experimental requirements
The need
The engine is the source of greatest non-linearity (apart from the driver) and so receives the greatest attention in simulation, testing & calibration
But, powertrains continue to grow in complexity Legislation is becoming more real world
Drivers expectations continue to rise
Effect of boundary conditions imposed by the powertrain, and system interactions are critical to engine operation
The need for effective system level calibration becomes greater
Legislative drive is just one factor
WLTP cycle and RDE give added topicality to the issue BUT, this is the same need that has existed for decades Most production calibrations (and architectures) are found wanting in at least some respects when exposed to real life operation
Robustness is already an issue even over NEDC
4 Factor 1
Facto
r 2
Real world NOx and CO
26
Source –
REAL-WORLD EXHAUST EMISSIONS FROM MODERN DIESEL CARS
A META-ANALYSIS OF PEMS EMISSIONS DATA FROM EU (EURO 6) AND US (TIER 2 BIN 5/ULEV II) DIESEL PASSENGER CARS. PART 1: AGGREGATED RESULTS
Vicente Franco, Francisco Posada Sanchez, John German, and Peter Mock The International Council on Clean transportation 2014
Real world operation is unpredictable
Real world operating envelope several orders of magnitude larger than even WLTP operation
WLTP has broader Coverage than NEDC, < RDE
Possible Solutions:
More steady state calibration points – Lots of effort, doesn’t address transients
Drive cycle based optimisation – Duty Cycle Specific
How can we access the benefits of Design of Experiments with full operating region coverage with dynamic events?
CO
2remains the long term focus
RDE emissions compliance will be achieved through
hardware design and robust calibration
Currently the RDE procedures will allow
measurement of real world CO2 but is not subject to mandatory limits
This will surely change in time
Real world CO2 will only become more important from here on
A global approach is difficult, but necessary
WLTP, RDE are a target setting and
validation exercise, NOT a development
process
For development we need better insight into
system performance and a global
optimisation approach
Full map compliance at steady state
Consideration of driver, powertrain and boundary condition interactions
Competent dynamic control at all times
Optimisation Hierarchy
Steady state optimisation Dynamic optimisation 0 200 400 600 800 1000 1200 0 500 1000 Time (s) N O x ( p p m ) Measured Predicted 0 500 1000 0 2000 4000 measured p re d ic te d 0 500 1000 -5000 0 5000 measured e rr o r 0 2000 4000 6000 8000 10000 12000 -5000 0 5000 samples e rr o r 1000 2000 3000 S p e e d (r p m ) Hardware selection ValidationOptimisation workflow
Advanced powertrain test
Optimise for full map steady state compliance
Generate dynamic engine and aftertreatment models
Dynamic simulation of real manoeuvres00 200 400 600 800 1000 1200 500 1000 Time (s) N O x ( p p m ) Measured Predicted 0 500 1000 0 2000 4000 measured p re d ic te d 0 500 1000 -5000 0 5000 measured e rr o r 0 2000 4000 6000 8000 10000 12000 -5000 0 5000 samples e rr o r 1000 2000 3000 S p e e d (r p m ) Optimisation of calibration Hardware selection Validation testing on CD over preset cycles Dynamic
characterisation on CD Steady state test
Validation testing on road with PEMS
Develop models robust to boundary conditions
Develop models robust to boundary conditions
Steady state test
Optimisation workflow
Advanced powertrain test
Optimise for full map steady state compliance
Generate dynamic engine and aftertreatment models
Dynamic simulation of real manoeuvres00 200 400 600 800 1000 1200 500 1000 Time (s) N O x ( p p m ) Measured Predicted 0 500 1000 0 2000 4000 measured p re d ic te d 0 500 1000 -5000 0 5000 measured e rr o r 0 2000 4000 6000 8000 10000 12000 -5000 0 5000 samples e rr o r 1000 2000 3000 S p e e d (r p m ) Optimisation of calibration Validation testing on CD over preset cycles Dynamic
characterisation on CD
Validation testing on road with PEMS
Short term need for SI
Will need to run at stoichiometry over full
operating envelope to allow 3 way catalyst to work – exhaust temperature over 1000C
Probably need larger catalyst to cope with high mass flows
Short term need for Diesel
DPF, DOC, with selective catalytic reduction (SCR) to reduce NOx
Balance in cylinder NOx reduction (EGR, SOI) with SCR to give compliance and acceptable urea consumption
Longer term impact of RDE
We have been designing and calibrating around UDC then NEDC since 1970
This has profoundly influenced the thinking of several generations of engineers
Future powertrains need to comply in all situations
Segmented solutions targeted at low load are becoming less favourable
Downsizing with driveability enablers (such as eBoost, hybridisation) will accelerate
Peak power augmentation and waste energy capture becoming more relevant
Component selection and sizing
Effective selection of powertrain architecture is critical but largely left to custom and practice guided by expert knowledge
Formal optimisation is needed at an early stage Places great emphasis on modelling environment More work needed on architecture optimisation with sizing and through life costing as an
integrated activity
Cost Performance
Emissions
Steady state test
Optimisation workflow
Advanced powertrain test
Generate dynamic engine and aftertreatment models
Dynamic simulation of real manoeuvres00 200 400 600 800 1000 1200 500 1000 Time (s) N O x ( p p m ) Measured Predicted 0 500 1000 0 2000 4000 measured p re d ic te d 0 500 1000 -5000 0 5000 measured e rr o r 0 2000 4000 6000 8000 10000 12000 -5000 0 5000 samples e rr o r 1000 2000 3000 S p e e d (r p m ) Optimisation of calibration Hardware selection Validation testing on CD over preset cycles Dynamic
characterisation on CD
Validation testing on road with PEMS
Develop models robust to boundary conditions Optimise for full map steady state compliance
Optimising for full map RDE compliance
Initial task is to balance engine out emissons with aftertreatment duty cycle
Urea refill schedule needs balancing with DPF loading and CO2 for Diesel
Catalyst sizing and placement tradeoff for lightoff v full load on SI
Thermal and environment impact on engine out emissions and aftertreatment performance needs greater insight
When tough hardware tradeoffs are needed consider likely contribution to overall running based on
observed probability
Weighted contribution of high speed, high load running is low Medium speed, medium load is dominant
Low ambient temperatures much more significant than in NEDC
Optimisation workflow
Advanced powertrain test
Optimise for full map steady state compliance
Generate dynamic engine and aftertreatment models
Dynamic simulation of real manoeuvres00 200 400 600 800 1000 1200 500 1000 Time (s) N O x ( p p m ) Measured Predicted 0 500 1000 0 2000 4000 measured p re d ic te d 0 500 1000 -5000 0 5000 measured e rr o r 0 2000 4000 6000 8000 10000 12000 -5000 0 5000 samples e rr o r 1000 2000 3000 S p e e d (r p m ) Optimisation of calibration Hardware selection Validation testing on CD over preset cycles Dynamic
characterisation on CD
Validation testing on road with PEMS
Develop models robust to boundary conditions
Steady state test enhancements
Incorporation of prior knowledge to speed up limit search
Iterative on-line DoE to minimise data requirement
Sweep mapping to yield data more rapidly Bayesean techniques to incorporate prior knowledge into response models
Iterative online DoE Process
26
Start with a simple DoE design
Select next points to test based on points of least confidence
Iterative DoE Process
Start with a simple DoE design
Select next points to test based on points of least confidence
Iterative DoE Process
Start with a simple DoE design
Select next points to test based on points of least confidence
Iterative DoE Process
Start with a simple DoE design
Select next points to test based on points of least confidence
Iterative DoE Process
Start with a simple DoE design
Select next points to test based on points of least confidence
Iterative DoE Process
Start with a simple DoE design
Select next points to test based on points of least confidence
Iterative DoE Process
Start with a simple DoE design
Select next points to test based on points of least confidence
Iterative DoE Process
Start with a simple DoE design
Select next points to test based on points of least confidence
Iterative DoE Process
Start with a simple DoE design
Select next points to test based on points of least confidence
Iterative DoE Process
Start with a simple DoE design
Select next points to test based on points of least confidence
Iterative DoE Process
Start with a simple DoE design
Select next points to test based on points of least confidence
Iterative DoE Process
Start with a simple DoE design
Select next points to test based on points of least confidence
Iterative DoE Process
Start with a simple DoE design
Select next points to test based on points
of least confidence
Iterative DoE Process
Start with a simple DoE design
Select next points to test based on points of least confidence
Iterative DoE Process
Depends upon close integration between cell and DoE tool Recalculates models of mean and variance on the fly
Also opportunity to use a Bayesian approach
Dynamic
characterisation on CD Steady state test
Optimisation workflow
Optimise for full map steady state compliance
Generate dynamic engine and aftertreatment models
Dynamic simulation of real manoeuvres00 200 400 600 800 1000 1200 500 1000 Time (s) N O x ( p p m ) Measured Predicted 0 500 1000 0 2000 4000 measured p re d ic te d 0 500 1000 -5000 0 5000 measured e rr o r 0 2000 4000 6000 8000 10000 12000 -5000 0 5000 samples e rr o r 1000 2000 3000 S p e e d (r p m ) Optimisation of calibration Hardware selection Validation testing on CD over preset cycles
Validation testing on road with PEMS
Develop models robust to boundary conditions
Advanced powertrain test
Steady state test
Optimisation workflow
Advanced powertrain test
Optimise for full map steady state compliance
Generate dynamic engine and aftertreatment models
Dynamic simulation of real manoeuvres00 200 400 600 800 1000 1200 500 1000 Time (s) N O x ( p p m ) Measured Predicted 0 500 1000 0 2000 4000 measured p re d ic te d 0 500 1000 -5000 0 5000 measured e rr o r 0 2000 4000 6000 8000 10000 12000 -5000 0 5000 samples e rr o r 1000 2000 3000 S p e e d (r p m ) Optimisation of calibration Hardware selection Validation testing on CD over preset cycles
Validation testing on road with PEMS
Develop models robust to boundary conditions
Dynamic
Requirements for the chassis dynamometer
All of the precision, control of an engine dyno
but with the boundary conditions of a chassis
dyno
Full instrumentation suite
Control over engine actuators, speed, load
Full integration with the optimisation suite (and
accessible by calibration engineers)
Implies a relatively mature powertrain and
mule vehicle is available
Chassis dynamometer at Bath
Chassis dynamometer rebuild at Bath
Four wheel drive dynamometer
Robot driver with real driving characteristics and direct mode for mapping
Wheel torque measurement and control
Comprehensive raw emissions measurements In cylinder measurements
Battery emulation and EV instrumentation Full PCM access
-10 to +50C temperature control Full frontal area road speed fan
Humidity control, Combustion air conditioning Altitude simulation?
Operating point control options
1.
Vehicle model – tractive effort, rolls
speed feedback
2.
Vehicle and tyre model - wheel torque &
speed feedback
3.
Vehicle, tyre, TX model - engine torque
& speed feedback
4.
Engine speed and Relativ Luft control
Critical need for precision of CD test
Even the NEDC is a complex test
procedure
Many factors can adversely affect precision, masking observed changes
DoE approach used to assess impact of these setup variables, on
chassis dyno
Main noise factors in chassis dyno NEDC
-5% 0% 5% 10% 15% Battery di scharge (V) PAS pumpSpee d error Tyre type Engi ne o il le vel Peda l Bu syness Tyre pre ssure Vehi cle a lign men t Road sp eed f an Vehi cle ma ss Tie d own strap s Engi ne start temp erature Absolu te Fu el con sumptio n cha ng e (g) Effect of HTHS change of 0.6cPIndividual cycle comparison on a crank angle basis Allows detailed analysis of system interactions
Use of high bandwidth data on CD
0 0.5 1 1.5 2 2.5 3 x 105 0 20 40 60 80 100 P re ssu re ( b a r) 0 0.5 1 1.5 2 2.5 3 x 105 0 0.1 0.2 0.3
Crank angle (deg)
In je ct io n D e m a n d UDC1 UDC2 UDC3 UDC4 0 195 390 585 780 1200 0 50 100 Time (s) Veh ic le Spe ed (k m /h) 0 10 20 30 40 50 60 70 Pres s ure (ba r) 0 0.1 0.2 Crank Angle Inj D m d UDC2 UDC3 UDC4 UDC1
e.g. Hot v Cold FMEP
Integral of FMEP removes noise from plot and clearly shows difference between hot and cold start tests
0 2000 4000 6000 8000 10000 12000 14000 16000 18000 0 1000 2000 3000 E n g in e S p e e d ( R P M ) 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 0 0.5 1 1.5 2 2.5 3 3.5 Cycle C u m u la ti ve F M E P ( b a r. cycl e s) Cold Start Hot Start Difference
Ability to visualise transient events
0 195 390 585 780 1200 0 50 100 Time (s) Veh ic le Spe ed (k m /h) -20 TDC 20 40 60 0 100 200 3000 50 100 Crank Angle Cycle Number Pres s ure (ba r) -30 -20 -10 TDC 10 20 30 0 100 200 300 400 20 30 40 50 60 Crank Angle Cycle Number Pres s ure (ba r) 51Dynamic design of experiments
Based on tools developed by IAV
Addresses need to cover the entire design space including rate of change of inputs
Very demanding control requirements for host system direct to engine actuators
Allows hardware to be characterised
independently of control strategy and calibration
Dynamic schedule design
0 0.02 0.04 0.06 0.08 0.1 0.12 Frequency (Hz) A m p lit u d e Linear Chirp Logarithmic Chirp NEDC1. Define static and dynamic operating range
Optimise space coverage of multi-signal test
Example – Emissions Modelling
Problem Definition•Dynamic inputs •Temperature input •Emissions output
Hot engine test plan
Hot engine data acquisition Data Pre-processing Dynamic-Hot engine model Temperature scaling
factor Combine for general
dynamic/thermal model
Cold start test design
Cold start data acquisition Data Pre-processing Temperature scaling function modelling 20 40 60 80 100 0 0.5 1 1.5 Oil Temperature ( oC) N O x ( C o ld /H o t)
Torque Based Input
20 40 60 80 100 0 0.5 1 1.5 Oil Temperature ( oC) N O x ( C o ld /H o t)
Pedal Based input
T f s Hot/Dynamic modelling x f y Hot/dynamic model validation 0 200 400 600 800 1000 1200 0 500 1000 1500 2000 Time (s) N O x ( p p m ) Target Predicted 0 500 1000 1500 0 5000 measured p re d ic te d 0 500 1000 1500 -5000 0 5000 measured e rr o r 0 2000 4000 6000 8000 10000 12000 -5000 0 5000 samples e rr o r 1000 2000 3000 S p e e d (r p m ) R2 General temperature dependant model 0 600 1200 0 0.5 1 Time (s) N O x S c a lin g 0 600 1200 0 0.5 1 Time (s) N O x S c a lin g 0 600 1200 0 0.5 1 Time (s) N O x S c a lin g 0 600 1200 0 0.5 1 Time (s) N O x S c a lin g 0 600 1200 0 0.5 1 Time (s) N O x S c a lin g 0 600 1200 0 0.5 1 Time (s) N O x S c a lin g x T f y , Model Validation 0 200 400 600 800 1000 1200 0 500 1000 Time (s) N O x ( p p m ) Measured Predicted 0 500 1000 0 2000 4000 measured p re d ic te d 0 500 1000 -5000 0 5000 measured e rr o r 0 2000 4000 6000 8000 10000 12000 -5000 0 5000 samples e rr o r 1000 2000 3000 S p e e d (r p m )
Challenges for dynamic DoE
Accurate control/measurement of dynamic conditions
Sensor and actuator accuracy and response time in complex measurement chains
Non-linear dynamic modelling methods
Complex mathematical functions with many coefficients Tend towards physics based models
Automated model fitting
Dynamic optimisation
Deterministic – Duty cycle specific
Stochastic – Requires statistical information about duty cycle
Steady state test
Optimisation workflow
Advanced powertrain test
Optimise for full map steady state compliance
Generate dynamic engine and aftertreatment models
Dynamic simulation of real manoeuvres00 200 400 600 800 1000 1200 500 1000 Time (s) N O x ( p p m ) Measured Predicted 0 500 1000 0 2000 4000 measured p re d ic te d 0 500 1000 -5000 0 5000 measured e rr o r 0 2000 4000 6000 8000 10000 12000 -5000 0 5000 samples e rr o r 1000 2000 3000 S p e e d (r p m ) Optimisation of calibration Hardware selection Validation testing on CD over preset cycles Dynamic
characterisation on CD
Validation testing on road with PEMS
Develop models robust to boundary conditions
Requirements for modelling
Data driven models can be very accurate but give little insight and no predictive power
Physics based dynamic models offer insight and re-useability
But today’s physics based models are not good enough for calibration
Must respond appropriately to changing boundary conditions (e.g. thermal)
Need to pass boundary conditions to neighbouring models (e.g. heat rejection)
Controller and calibration must be included
Must respond appropriately to calibration inputs (such as divided injections)
Ultraboost – exploring the limits of downsizing
Improving turbocharger modelling
601D and 3D simulation
Hot, pulsed,
gas stand
On engine mapping
Steady state test
Optimisation workflow
Advanced powertrain test
Optimise for full map steady state compliance
Generate dynamic engine and aftertreatment models
Optimisation of calibration Hardware selection
Validation testing on CD over preset cycles Dynamic
characterisation on CD
Validation testing on road with PEMS
Develop models robust to boundary conditions Dynamic simulation of real manoeuvres00 200 400 600 800 1000 1200 500 1000 Time (s) N O x ( p p m ) Measured Predicted 0 500 1000 0 2000 4000 measured p re d ic te d 0 500 1000 -5000 0 5000 measured e rr o r 0 2000 4000 6000 8000 10000 12000 -5000 0 5000 samples e rr o r 1000 2000 3000 S p e e d (r p m )
Simulation requirements for optimisation
Not enough to optimise over one preset velocity based cycle, or even a family
Need a stochastic approach with probability of given manoeuvres factored in
62
At least the system level goals and constraints are given to us!
Need to decompose to sub-system level
Optimisation workflow
Advanced powertrain test
Optimise for full map steady state compliance
Generate dynamic engine and aftertreatment models
Dynamic simulation of real manoeuvres00 200 400 600 800 1000 1200 500 1000 Time (s) N O x ( p p m ) Measured Predicted 0 500 1000 0 2000 4000 measured p re d ic te d 0 500 1000 -5000 0 5000 measured e rr o r 0 2000 4000 6000 8000 10000 12000 -5000 0 5000 samples e rr o r 1000 2000 3000 S p e e d (r p m ) Optimisation of calibration Hardware selection Validation testing on CD over preset cycles Dynamic
characterisation on CD Steady state test
Validation testing on road with PEMS
Develop models robust to boundary conditions
Workflow and facilities need improvements
Perhaps the biggest challenge is the way large companies traditionally work
Design, simulation, build, test ops., calibration all need to be joined up
Re-use and improve the initial models throughout the process
Most powertrain dynos and chassis dynos are not flexible or precise enough today
Neither are their operating practices
Conclusions
RDE will require systematic engine optimisation in vehicle system context
Behaviour dominated by steady state capability Boundary conditions and interactions critical
Signoff on random CD cycles unlikely to be a robust process on its own
Better use of software tools essential Use the CD to generate a rich dataset
Validate advanced models Optimise in software
Signoff on random cycles with more confidence
Significant implications for test design/operation
Contact
Chris Brace FIMechE
Professor of Automotive Propulsion
Deputy Director, Powertrain and Vehicle Research Centre University of Bath
BA2 7AY
+44 1225 386731
