'HULYDWLRQRID0HDQ 9DOXH0RGHOIURPD'HWDLOHG0RGHO 1 · 2015-08-20 · TB/Scher -- fevgti-meanvalue.ppt...

TB/Scher -- fevgti-meanvalue.ppt 1

TB/Scher -- fevgti-meanvalue.ppt

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This presentation is the continuation of the work on Mean Value engine models, that John Silvestri has presented at the Aachen Colloquium of Automobile and Engine Technology in October 2000.

As for the GT-Suite user conference, the intention of this presentation is to provide more information about the handling and application of this modeling strategy.



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� Introduction

� Definition: Mean Value Model

� Detailed Engine Model

� Mean Value Engine Model

� Steps of Mean Value Model Generation

� Engine Simulation with detailed and MV Model

� Improving Universal Validity of Mean Value Models

� Summary and Conclusions

The presentation today is structured as follows:

8 After a first introduction follows the 8 definition of the Mean Value Model.

Then, the 8 detailed engine model will be shown, that served as the base for

the generation of a 8 mean value model, that will be discussed afterwards.

8 Next, the steps of generating a mean value model will be shown.

Then, 8 results of engine simulations with the detailed and with the mean value model will be discussed.

8 Afterwards, it will be outlined, how the universal validity of the MV model can be improved.

8 At the end of the presentation, a summary will be given and conclusions will be drawn.



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6FHQDULR� Variety of Vehicle Installations for individual engines Î� Complexity of Engine/Vehicle Controllers Î� Calibration Effort Î Î� Virtual Vehicle Integration in

� Earlier Phases of the Development Processî VIRTUAL CALIBRATION

9LUWXDO�&DOLEUDWLRQ�0HWKRGV� Real Time Models

� e.g. HIL

� Non-Real Time

� e.g. Detailed GTpower Model - SIMULINK/MATRIXx

� Close to Real-Time

� e.g. GTpower Mean Value Model

In the recent years, car manufacturers have increased the variety of vehicle

models to serve both major and niche markets. 8 Following building block strategies, all these vehicles shall make use of the same or less engine families.

This increasing number of vehicle models with different requirements to drivability and the increasing complexity of vehicle and powertrain control units will lead to significant rise of calibration effort.

To meet shorter product development schedules, the vehicle integration has to start already in the virtual development phases. Virtual Calibration can help to save time by developing new controller functions, that can make calibration more efficient and by providing prototype data for the powertrain controllers.

8 Such methods of Virtual Calibration can be distinguished by their time-scale:

Real-Time models are used e.g. for Hardware-In-The-Loop applications and allow the testing of the ECU and its calibration in the same time-scale as the physical system operates in.

Physically based models usually are not able to run at real-time. 8 For this User Conference, the presentation will focus on GTpower applications.

8 Last year, we have shown the application of a detailed GTpower engine/vehicle model in linked execution with a SIMULINK controller model.

8 Today, we want to demonstrate, that this engine model can be made quicker by converting it to a Mean Value Model.



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� What is a Mean Value Model

� Provides steady (non-fluctuating) representation of engine processes

� Generally Map-Based

� Why is it Used

� Control engineers moving from simple map-based models to MVM

î Better suited for transient simulations

î Models entire intake/exhaust system volume

¤ Essential for representation of turbocharged engines

î Reduces requirements for model correction factors

� Current Practice

� MV models primarily built based on test cell engine data

8 Before going into details, the expression "Mean Value Model" shall be defined.

A Mean Value Model represents the engine not by its high-frequent physical process, but by its quasi-steady and non-fluctuation changes of the Mean Values, as volumetric efficiency, indicated cylinder work, exhaust temperature, only to mention a few. As likewise the flow representation does not show fluctuations, this modeling technique is not applicable to NVH studies.

A Mean Value Model generally is based on maps, that represent the engine characteristics in dependency of the most important input parameters.

8 The reason for using this type of models for the total engine process is, that control engineers have found, that the use of Mean Value Models is better suited to transient simulations than simple steady-state map-based models. With the additional dynamic effects of intake and exhaust system volume introduced by mean value models, and that are essential for the representation of turbocharged engines, the requirement for model correction factors is reduced in calibration.

8 The current practice is, to build mean value models on the base of test bench data. The idea of this presentation is, to do it starting from a detailed GTpower engine model.



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This figure shows the detailed model of the FEV Diesel Future II engine.

It is a 4 cylinder 4 valve DI diesel engine with a VNT turbocharger and EGR.

The major components of the engine model are highlighted:

8 Turbocharger,

8 Intercooler,

8 Intake Manifold,

8 Exhaust Manifold and

8 EGR system with heat exchanger.

On the right side, a simplified controller structure is shown with

•8 Fuel Rate controller reading load command, engine speed and air flow,

•8 Boost Pressure controller acting on VNT rack position and

•8 EGR controller, which implicitly controls EGR rate by adjusting air flow in front of the compressor.

From this detailed model with a total number of about 250 volumes and sub-volumes, the mean value model is derived.



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This is done by lumping together pipes and flow splits of the detailed model and by replacing them with flow splits, that have the same total volume, that add up to the same total duct length, but have significantly longer characteristic lengths.

The model shown here now has only 13 volumes and sub-volumes left.

The characteristic lengths have been increased from 25 to 300 mm on the intake and from 45 to 400 mm on the exhaust side.

This alone would not be enough to reduce the number of time steps. The next big step in performance is to replace the cylinders and the valves by the

8Mean Value Cylinder. This cylinder now has no more suction and exhaust stroke, but delivers a continuous mass flow rate.

All measures together enable the use of much larger time steps of up to 7.5 degrees crank angle without running into instabilities.

8 The controller unit on the right side is the same as in the detailed model.



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� Volumetric Efficiency� Indicated Efficiency� Exhaust Efficiency

$GMXVW�0HDQ�9DOXH�0RGHO� Pipes and ducts lumped together into larger volumes, have to adjust:� Heat transfer surfaces� Wall structure� External heat sinks

� Pressure losses� Continuous non-pulsating flow, have to adjust:� Heat transfer multipliers� Turbo efficiency

So, once the structure of the MV model is set, which are the next steps to go during the generation of a Mean Value Model?

8 First at all, the input data for the Mean Value Cylinder have to be generated. The GTpower object requires input data for

Volumetric efficiency as measure for flow rate indicated efficiency as measure for power output andso-called exhaust efficiency representing exhaust temperature.

These data can be derived from simulations or from test bench results. In the very early state of virtual engine development, no prototypes may be available. Then, the detailed GTpower model alone will have to generate the data for the MV model.

8 Afterwards, the MV model will have to be adjusted in some aspects.

The flow splits, that have replaced detailed subsystems, now only feature one average wall heat transfer model per subsystem. In order to represent the behavior of the former detailed subsystem, its heat transfer surface, wall structure and external heat sinks have to be adjusted.

Further adjustment is due on pressure losses.

Representing the truly pulsating flow by continuous flow effects time average heat transfer coefficient. Furthermore, the operating points of turbine and compressor do not show any more high-frequent excursions, and their efficiency may need to be corrected to represent the behavior of the pulsating flow.



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� Description of MV intake volume flow

� Including possible external EGR

� VolEff = f(engine speed, engine pressure ratio)

� Default PR correction accounts for expansion of exhaust gas. Assumptions:

� Pressure @TDC: Cyl. = Exh. manifold

� Constant boost temperature

� Input: x-y data for VolEff=f(rpm) @ PR=1

� Use Volumetric Efficiency Map to include

� Superimposed effects of thermal throttling

� Internal EGR

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Now, let us have a look on how the input data for the MV cylinder are derived.

8When talking about volumetric efficiency as input data for the mean value model, the user has to remember, that the Mean Value Cylinder will compute its intake volume flow of this input value, no matter what the composition of the intake gas may be. That means, EGR will be part of that flow. Therefore, if the detailed model was run with EGR, the volumetric efficiency input data shall be manually computed from total intake mass flow rate and intake density.

The air flow of the MV model and its output value of volumetric efficiency, which refers to air flow, however, will be reduced by EGR correctly.

8 Volumetric efficiency input data is mapped as a function of engine speed

and engine pressure ratio. 8 The default correction is based on the assumption of pressure equilibrium of the residuals in the compression volume and in the exhaust manifold. It is valid for constant boost temperature and predicts a slight fall of volumetric efficiency at higher pressure ratios.

A superimposed effect of variable boost temperature on thermal throttling can be introduced by using a volumetric efficiency multiplier with a RLT-dependency.

8We avoided the use of a separate correction multiplier for intake temperature by using the volumetric efficiency map instead of the default correction. The choice of the map option is useful as well, if the engine shows larger amounts of internal EGR.



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� ieff = f(engine speed, 2nd parameter)� 2nd parameter may be AFR or Pint

� AFR o.k., if naturally aspirated diesel engine or conventional TC diesel engine� Here: AFR does not vary monotonically with engine load î Effects of → VNT, EGR controlî Pint chosen as second parameter

As second input value, indicated efficiency of the engine is mapped as a function of engine speed and a second parameter. This second parameter has to be AFR or intake pressure.

The use AFR is o.k., if AFR is a monotonous function of load, as it is on a naturally aspirated or a conventionally turbocharged diesel engine.

Here, with VNT and EGR, AFR may show some excursions in load sweeps, so intake pressure had to be chosen instead of AFR.

Intake pressure would be, no doubt, the second parameter for a conventional SI engine, too.



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For this simulation, data triples of engine speed, intake pressure and indicated efficiency from 90 operating points, i.e. 10 loads from 10-100% pedal position at 9 engine speeds, were processed by GTpower’s XYZpoints object.

8 This plot shows, that first a triangulation of the data is done,

8 then an interpolated map is generated. This map is then scaled to a

8 normalized map, that is used for lookup during simulation.



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� Percentage of fuel energy lost as exhaust heat

� ExhEff = f(engine speed, {AFR or Pint})

� compute from RLT-variables:

� ieff: indicated efficiency

� htr: heat transfer losses as % of fuel energy

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Indicated Eff.

Exhaust Eff.

Exhaust efficiency is defined as the percentage of total fuel energy lost as exhaust heat.

It is mapped according to the procedure of indicated efficiency.

The fuel energy in the cylinder is converted into three fractions:

a) Indicated cylinder work

b) In-cylinder wall heat losses and

c) Exhaust heat losses.

According to this split of energy exhaust efficiency data can be computed from RLT-output variables of indicated efficiency and heat transfer according to the equation shown in the figure.



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Before comparing the dynamic behavior of the detailed and the mean value model, the accuracy of the detailed model shall be demonstrated. This figure shows the comparison of simulation results to measured data at full load. It can be seen, that the accuracy is reasonably well.



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The next figure shows the comparison of measured and calculated results in a load sweep at 2000 rpm. Again, a good correlation of measurement and simulation is achieved, with EGR at low and without EGR at higher loads.

After the necessary calibration, the mean value model as well reached almost the same accuracy at steady state.

Now, the interesting point is to see, how far the dynamic behavior of the well calibrated detailed model could be represented by the mean value model.



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CPU time (PII 450 MHz):

Detailed (full): 26 min (1560 s) 312xMean Value: 1:15 min (75 s) 15xRatio: ≈ 21

Further optimization of simulation speed possible by increasing time steps limited by characteristic lengths

For this purpose, a load step was calculated at a constant speed of 2000 rpm. Starting from 30% load at time zero, load continuously increases to about 40%.

The figure shows, that detailed and mean value model are equivalent in predicting transient engine performance.

8 The big advantage of the mean value model is its short execution time, which is shrunk down by a factor of 21 from 26 minutes, 312 times longer than the load step, to 75 seconds on a computer, which is not any more state of the art.

With a faster computer, CPU time may be reduced again by a factor of 1.5 to 2, so a time scale of about 8 compared to real-time seems to be realistic.



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09�PRGHOV�DUH�OHVV�SUHGLFWLYH�WKDQ�'HWDLOHG�0RGHOV� Detailed models include numerous physical sub-models and are, therefore, self-

adjusting even to unconventional boundary conditions� MV models (partly) substitute physics by maps and algebraic equations� MV Indicated Efficiency neglects:� Deviations of PMEP due to transient exhaust backpressure excursions� SOI effects on ISFC

� EGR etc.

� Introduction of these effects by Efficiency Multiplier� Maps of corrections to be generated from î Measured data orî Detailed model simulations

Although we have demonstrated a good ability of the MV model to represent the behavior of the detailed model, there is a risk, that not all excursions of control and operation parameters are handled that properly by a mean value model as by a detailed model.

8Mean Value Models are less predictive than Detailed Models.

8 Because of its physically based sub-models, a detailed model is self-adjusting to most types of boundary conditions.

8 But with many physical models being replaced by lookup tables and algebraic equations, the mean value model can only react correctly on those parameters, that it has explicit dependencies for.

8 Examples for possible inaccuracies are

* PMEP deviations from mapped state, e.g. because of transient excursions of exhaust backpressure

* Modified injection timing shifts the fuel energy balance between all three fractions, indicated efficiency, exhaust losses and coolant heat. The map in the mean value model does not allow for this parameter.

* EGR as well may have an influence on indicated, exhaust and volumetric efficiency.

8 The effects of these parameters can be taken into account by using the efficiency multipliers, that the GTpower Mean Value Cylinder provides.



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This figure shows an example for a calculation scheme for the indicated efficiency multiplier.

The dependency of indicated efficiency of engine speed and intake pressure is already evaluated by the map. This scheme now corrects for the following parameters:

•8 AFR deviation from base value,

•8 pumping work determined from engine pressure difference and rpm,

•8 deviation from optimum injection timing, and

•8 EGR rate.

8 All influences are stored as correction factors, 8 that as a total product form the indicated efficiency multiplier for the actual operating point.

With the amount of maps and dependencies shown in this indicatedefficiency multiplier, the evaluation of these maps and factors can require a large number of detailed model executions. But if such a multiplier scheme is defined according to the algorithm in the ECU, the data generated for the GTpower Mean Value Model can be used as prototype data for the physical ECU.

This can save significant amounts of time later in the phase of experimental calibration, when usually time is running short.



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$�0HDQ�9DOXH�0RGHO�ZDV�GHULYHG�IURP�D�'HWDLOHG�0RGHO� FEV Diesel Future II engine: 4V-DI with VNT and EGR

7KH�PDMRU�UHTXLUHG�LQSXW�GDWD�RI�WKH�0HDQ�9DOXH�0RGHO�ZHUH�VKRZQ� Volumetric, indicated and exhaust efficiency

([WHQVLRQV�RI�D�09�0RGHO�WR�LQFOXGH�HIIHFWV�RI�RWKHU�RSHUDWLRQ�DQG�FRQWURO�SDUDPHWHUV�ZHUH�GLVFXVVHG� Multipliers for e.g. SOI, exhaust backpressure excursions, etc.� MV model input data may be useful as ECU data, too.

7KH�VSHHG�XS�RI�VLPXODWLRQ�LV�PRUH�WKDQ�RQH�RUGHU�RI�PDJQLWXGH� Almost real-time

2QFH�FDOLEUDWHG��D�*7SRZHU�09�PRGHO�LV�YHU\�XVHIXO�IRU�WLPH�VDYLQJ�SHUIRUPDQFH�DQG�KHDW�XS�VLPXODWLRQV�RI�HQJLQH�YHKLFOH�(&8�V\VWHPV�

As a summary, this presentation has shown, 8 how a Mean Value Model was derived from a Detailed GTpower engine model. The engine investigated in this study is the FEV Diesel Future II engine.

8 The major necessary input parameters were shown and

8 possible extensions of the Mean Value Model to introduce influences of other control and operation parameters, such as injection/combustion timing or excursions of exhaust backpressure were discussed.

An interesting aspect is, that the data generated for the GTpower Mean Value Model may be used as prototype data for the physical ECU, too.

8 In comparison to simulations with a detailed engine model, the GTpower Mean Value Model may be run almost at real-time, which means that the execution of a turbo-diesel model on a modern desktop computer takes only about eight times longer than the real physical process.

8 This makes a GTpower Mean Value Model very useful for quick andefficient simulations of dynamic engine performance and heat-up for the integration of engine-vehicle-control systems. The process of generating the Mean Value model itself may help to develop ECU structures, that can be calibrated more efficiently.



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References:

(1) J. Silvestri, T. Morel, O. Lang, C. Schernus, M. Rauscher:Advanced Engine / Drivetrain / Vehicle Modeling Techniques;9. Aachen Colloquium Automobile and Engine Technology,Oct. 4-6, 2000..

(2) M. Rauscher, K. Fieweger, C. Schernus: ’Overall System Simulation - A Tol for Virtual ECU Development’; International Automotive Conference, Stuttgart, May 10-11, 2000.

(3) O. Hild, A. Schloßer, K. Fieweger, S. Pischinger, H. Rake: 'LH5HJHOVWUHFNH�HLQHV 3.:�'LHVHOPRWRUV�PLW�'LUHNWHLQVSULW]XQJ�LP�+LQEOLFN DXI /DGHGUXFN� XQG $EJDVU�FNI�KUUHJHOXQJ. Motortechnische Zeitschrift 60, 1999, 3, 186-192

(4) M. Rauscher, C. Schernus, K. Fieweger, O. Lang, P. Adomeit, B. Kinoo, S. Pischinger: Transient Simulation of a Diesel Engine as a Tool for Virtual Calibration. GT-Suite Users Confernce, Frankfurt/Main, Dearborn, Yokohama, 1999.

(5) M. Muller, E. Hendricks, S.C. Sorenson: Mean Value Modeling of Turbocharged SI Engines. SAE Paper 980784

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