morse: model-based real-time systems engineering … · –physics-based predictive models for the...

Copyright © Claytex Services Limited 2017

Mike Dempsey

Claytex

Future Powertrain Conference 2017

MORSE: MOdel-based Real-time Systems

Engineering

Reducing physical testing in the calibration of

diagnostic and driveabilty features


MORSE project

• MOdel-based Real-time Systems Engineering (MORSE)

– Collaborative research project with Ford and AVL

Powertrain

– Co-funded by Innovate UK as part of the “Towards zero

prototyping” competition

• UK government organisation

– 2 year project

• The project aim was to address some of the challenges

of validating the functional requirements of electronic

control systems using real-time simulation of multi-

domain physical models created in Dymola


MORSE – Development activities

• Library development

– Physics-based predictive models for the engine,

transmission and driveline

– Combustion models, airpath models, thermodynamics and

mechanics

• Driveability calibration

– Virtual development process coupled to optimisation

tools

• OBD validation

– HiL based validation of real controllers using the

physics based plant models


Transmission model enhancements

• Predictive temperature-

dependent drag and friction

loss models

• Allows the investigation of

warm up and performance

predictions:

– Launch performance

– Fuel consumption and

emissions

– Thermal management


Detailed gear set model reduction

• A model reduction method was developed for the

Powertrain Dynamics library

• Starting from a detailed transmission model (above

diagram), the model reduction function lumps the

individual component inertias and losses for each gear

for a range of speed-load points

• Yields 30 – 60% saving in CPU time depending on

transmission complexity

Lumped

inertia

Lumped

losses

Variable

ratio


Predictive Combustion Model

• A 0D predictive combustion model was developed for the Engines library

• A parameter sensitivity analysis returned the parameters which had most influence on

the predicted burn rate:

– Flame centre

– Initialization of flame kernel radius and mass

– Expansion factor of turbulent flame speed

• Calibration of all the model parameters was based on part load and full load test data

provided by Ford

• The model was then validated post calibration against a further set of part and full load

operating points


Predictive Combustion Model Reduction

• Model reduction process diagram for Wiebe coefficient derivation from predicted burn rate

MFB: Mass Fraction Burnt

EOC: End Of Combustion

Optimisation of

Wiebe

coefficients via

VisualDOC

Predicted MFB

Derived Wiebe coefficients

f(rpm,load,afr,timing)

Error

Minimization


Parameterised Split Engine Model validation

• The Simulink split engine model subsystems

(air paths, mechanics & combustion models)

were compiled from the Dymola physical

models

• The combustion models incorporate the

Wiebe coefficients derived from the

predictive combustion models.

• Validation shows +/-4% accuracy trend

across speed range

CylindersMechanics

Air-paths


Vehicle Model Architecture

• VMA structure was adopted and modified for the MORSE project, used worldwide by several OEMs. Splits the model into sub-systems for easy management. Structure composed of five high level signal buses to carry all model signals

• Model includes HIL interface blocks for dSPACE HIL rig


Single & Multicore Architecture

• The single core

architecture requires

minor changes, only

adding some dSPACE

blocks when moving to

multi core

• This means model

structure and signal

structure is maintained

between the two

models

• The model can be

developed for single

core and SIL

applications and

converted to multicore

for HIL applications

Single-core implementation

Multi-core implementation


Vehicle model validation vs. test data

• Several manoeuvres tested in Dunton with a

family car equipped with AVL-DRIVE

sensors and unit

• The goal was to collect required data used to

tune the relevant Dymola vehicle model

parameters

• The comparison of Engine Speed and

Vehicle Acceleration in terms of frequency

and amplitude of oscillations during tip-in/tip-

out, as well as the coast-down curve, is in

agreement with the measured data


AVIDO (Automated Virtual Driveability

OptimizationAutomated MATLAB routine to:

• Communicate with calibration

software ATI Vision

• Run the SIL test of the model

(controller, driver, Dymola vehicle)

• Call AVL DRIVE and import test

recorded data to generate the

objective driveability ratings

• Optimize driveability ratings and

update the set of test parameters

for the next iteration

Test definition

START

END

PCM online

Initial calibration

Run automatic

manoeuvre test

Data recording

during test

AVL DRIVE

OptimizationCalibration change

User

criteria

met?

PCM offline

User’s manual

steps

Yes

No


Calibration Validation with AVL-AVIDO

• Tip-in test at 50% accelerator in 1st gear or at

100% accelerator in 2nd gear

• 2 parameters are investigated from the TipIn and

AntiShuffle functions

• The Response Surfaces from a “sweep test”

show the higher Driver Rating around the Ford

base values (red square)

• The density of design from an optimization test

show convergence in the same area

• Results confirmed that the existing calibration is

the optimal one


Further work

• Use of the split engine model within a HiL environment to test:

– Real time capability of:

• Crank Angle Resolved Engine Models

• Vehicle transmission and driveline models with increased levels of compliance and lash

– OBD, sensor and actuator fault testing using physical models

• Inclusion of developed capability within Engines and Powertrain Dynamics libraries


Conclusions

• Integration of predictive combustion model into Engines library

• Enhancement of Powertrain Dynamics library with increased fidelity and predictive

capabilities of thermal models

• Model reduction techniques have been developed to allow users to efficiently reduce

the computational effort without manual rearrangement of the models

• Driveability calibration optimisation for tip-in manoeuvres using SIL model successfully

completed

• New SIL and HIL multi-core architectures developed to maximise use of multiple cores

morse: model-based real-time systems engineering … · –physics-based predictive models for the...

Documents