MORSE: MOdel-based Real-time Systems Engineering. Reducing physical testing in the calibration of diagnostic and driveabilty features

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1 MORSE: MOdel-based Real-time Systems Engineering Reducing physical testing in the calibration of diagnostic and driveabilty features Mike Dempsey Claytex Future Powertrain Conference 2017

2 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 multidomain physical models created in Dymola

3 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

4 Transmission model enhancements Predictive temperaturedependent drag and friction loss models Allows the investigation of warm up and performance predictions: Launch performance Fuel consumption and emissions Thermal management

5 Detailed gear set model reduction Lumped losses 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 Variable ratio

6 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

7 Predictive Combustion Model Reduction Model reduction process diagram for Wiebe coefficient derivation from predicted burn rate Predicted MFB Optimisation of Wiebe coefficients via VisualDOC Error Minimization MFB: Mass Fraction Burnt EOC: End Of Combustion Derived Wiebe coefficients f(rpm,load,afr,timing)

8 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 Air-paths Cylinders Mechanics

9 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

10 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

11 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/tipout, as well as the coast-down curve, is in agreement with the measured data

12 AVIDO (Automated Virtual Driveability Optimization Automated MATLAB routine to: START 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 Test definition PCM online Initial calibration Calibration change User s manual steps Optimization Optimize driveability ratings and update the set of test parameters for the next iteration Data recording during test Run automatic manoeuvre test No AVL DRIVE User criteria met? Yes END PCM offline

13 Calibration Validation with AVL-AVIDO Tip-in test at 50% accelerator in 1 st gear or at 100% accelerator in 2 nd 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

14 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

15 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

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