Controls, Measurement & Calibration Congress 2014, Brazil

Scalable Modeling Depths for Realtime Engine Simulation Applied for Model Based Calibration

Johann KRAMMER

AVL List GmbH, Hans-List-Platz 1, 8020 Graz, Austria

ABSTRACT

Demands for more functions in an automobile are continuously growing and thereby its complexity. Technologies like Hardware-in-Loop simulation based on physical modeling of the engine with all its subsystems can be used to the calibration process.

This paper gives an overview on available engine modeling depths, from purely data driven empirical models to physics based crank angle resolved models to be applied from the concept phase up to testing. Compared to empirical models physical models are more accurate and are significantly more robust when operated outside “default” operating conditions.

It often can be observed that in the concept and calibration phase models are developed independent of each other. This inconsistency typically sums up in double work for base data collection, model design and model tuning. With the goal of frontloading and moving the calibration work earlier in the development process a horizontal consistency of the models is becoming even more important.

INTRODUCTION

Demands for more functions in an automobile are continuously growing and at the same time complexity of the powertrain. Stringent economic and environmental constrains are challenging the development of modern IC engines. Simulation supports the engineer to manage these new functions and variants and it’s subsystem interactions with a reasonable effort to fulfill the consumers request for low fuel consumption and fun to drive. System level simulations are required to incorporate different domains like engine, drive train, cooling, exhaust aftertreatment and engine control, in a multi-physical manner. In the early concept phase, when lacking measurements, models need to rely on first order principles and empirical approaches with a sound knowledge basis of model parameters. In the late control design and calibration phase real-time capable models are mandatory

REAL-TIME ENGINE MODELS

This section aims at comprising a compact description of the different approaches developed in the framework of AVL CRUISE M Engine. Following terms are used to specify the gas path consideration:

• 1D Gas Dynamics: Pressure waves and it’s propagation in Crank-Angel resolution

• 0D Gas Dynamics: Pressure waves in Crank-Angel resolution

• 0D Mean Value Gas Path: Cycle average pressure (no pressure waves)

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1D GAS-DYNAMICS IN CRANK-ANGLE RESOLUTION

In state-of-the-art engine simulators, the engine models are flexibly assembled out of base elements such as cylinders, pipes, turbochargers, catalytic converters, and so on. The solution of the 1D Euler equations (balances for mass,

momentum, energy and species) returns the essential information of pressure wave motion in pipes and their impact on volumetric efficiency, compressor, turbine efficiency etc. The numerical solution of the transient 1D pipe dynamics typically applies finite volume discretization combined with dedicated shock-capturing techniques

The cylinder is modeled by 0D or multizone approaches. The models range from explicit combustion descriptions (ROHR tables, Vibe) to quasi-dimensional combustion models. Pollutant formation is typically modeled by (semi)empirical models but also by comprehensive kinetically driven reaction mechanisms (see Wanker et al. (1)).

0D GAS DYNAMICS IN CRANK-ANGLE RESOLUTION (CRA4, CRA4M)

Recently 0D models have been gaining interest again because of their capability of resolving pulsations in the volume elements and of running in real-time. These models are suitable from concept phase - to support base layout design decisions- up to real-time applications in the testing and calibration environment

0D gas exchange models rely on the cylinder models featuring equal modeling depth like those applied in the 1D approach (Figure 1)

0D MEAN VALUE GAS PATH AND CRANK-ANGLE RESOLVED REPRESENTATIVE CYLINDER (CRA1)

An innovative approach combining the characteristics of crank-angle resolved cylinder models and of mean value gas path models was proposed in Wurzenberger et al. (2) to ensure efficient computational efforts while preserving a high level of model predictability.

The applied cylinder approach is identical to the crank-angle resolved cylinder modeling approaches presented above. (Figure 2)

0D MEAN VALUE GAS PATH AND SURROGATE CYLINDER BLOCK MODEL

The combination of a 0D mean value gas path model with a surrogate description of the cylinder block is well known since many years. The key idea is to condense the physically complex and therefore numerically expensive cylinder domains (i.e. cylinder block) into fast running surrogate. The physical based description of the cylinder block is replaced by the data

Figure 1: Model Specifics for 0D Gase Dynamics

and CRA Resolved Cylinder Figure 2: Model Specifics for Mean Value Gas Path

and CRA Resolved Cylinder

driven surrogate functions. Different surrogate approaches are available, like Relevance Vector Machines (RVM) Support Vector Machines (SVM), Intelligent Neural Network (INN) and more.

The gas path model is assembled out of the same engine base elements (volumes, compressor, turbine, air cleaner, inter-cooler etc.) as discussed above. (Figure 3)

Figure 3: Model Specifics for Mean Value Gas Path and Surrogate Cylinder

TRANSIENT SURROGATE ENGINE MODEL (RVM, INN, dINN)

In contrast to the above discussed approaches, where the engine is described by a network of base elements, it is also possible to condense the entire engine characteristic into one single data driven element/model. When applying surrogate models to describe the entire engine behavior, transient effects like turbo-lagging need to be reflected by the approach. Also transient surrogate engine models are not sensitive any longer to changes in hardware configuration or ECU calibration when not specifically trained in advance.

The potential application ranges for transient engine surrogate models comprise rather late phases of the engine development process where all required input data are available.

STEADY-STATE MAP BASED ENGINE MODEL (MAP)

The application of steady state engine maps can be seen as the simplest approach to describe the entire engine in a data driven manner. In the present work, 2D maps for torque and fuel consumption are used depending on BMEP (i.e. load signal) and engine speed. More complex dependencies needed to describe the transient ramp up of temperatures during cold start or the transient build-up of boost pressure during accelerations can be covered by two kinds of extensions. Either multi-dimensional maps can be populated with data or the existing 2D maps can be corrected by physical and semi-physical correlations. This is an example of a Main Heading section. This section will include sub-sections.

SIMULATION RESULTS

The results presented in this paper comprise steady-state validation simulations, simulation results revealing the specific characteristics of different modeling depths, computational performance comparisons and transient results from simulating a real-life drive cycle.

The engine model used in the present study describes a turbocharged direct injection gasoline (TGDI) engine with four cylinders and an engine displacement of 1.6 l. The engine is equipped with a waste-gate turbocharger and a three-way

catalyst. The engine is controlled by a simple software ECU model. The target boost pressure is closed/open loop controlled by throttle, waste gate opening and intake valve phasing depending on operating conditions. The fuel injection angle, injection duration and spark advance are open loop controlled.

For the performance of transient simulations the TGDI engine is connected to a compact class vehicle of 1.2 tons gross weight with a manual 6-speed gear box drivetrain model, which is another module of the multi-disciplinary simulation tool CRUISE M. Figure 4 shows the modules of CRUISE M.

Figure 4: CRUISE M multi-disciplinary modules

STEADY-STATE ENGINE CALIBRATION

The models are calibrated with the help of steady-state measurements available at a series of load signal, engine speed and valve timing variations.

Figure 5 compares measured and simulated engine characteristics using two different engine model approaches discussed above to preserve readability of the figures. All comparisons are performed at the same engine speed and the same load signal, i.e. the same fuelling in the crank-angle based cylinder models. Figure 5 confirms that it is possible at steady-state points to accurately calibrate engine models featuring different modeling depths. Therefore transient results will be analyzed more thoroughly afterwards. However a short analysis of the steady-state simulations in Figure 5 reveals some cause and effect relations that are inherent to the different modeling approaches

DRIVE CYCLE SIMULATION

The transient drive cycle simulations are performed following a real-life cycle. For this purpose a velocity profile from a dedicated driving event (measured using AVL M.O.V.E.) was taken from a dynamically highly transient driving profile. This

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choice was made in order to compare the capabilities of the different engine models in highly transient operating conditions as they may not always be given by legislation cycles. The simulated results of the entire drive cycle are shown in Figure 8, followed by several zooms into specific segments (Figure 7 to Figure 10) for a more detailed analysis of the different model approaches.

Figure 8a shows on the overall scale of the drive cycle, that all modeling approaches are capable of following the target vehicle velocity trace with reasonable accuracy. This also implies a good agreement in engine speeds as given in Figure 8b. Both agreements (vehicle velocity and engine speeds) confirm the capability of the driver and ECU model to adequately control the engine power output in the overall forward facing model. The consistent prediction of engine power in the overall scale is also given by the BMEP plots in Figure 8c. In order to expose model specific differences it is thus necessary to zoom-in into particular drive cycle segments.

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Figure 7 shows the same data as given in Figure 8 for a selected velocity trace segment covering different accelerations (Figure 7a). It is discernible from Figure 7a that during a first moderate acceleration period all modeling approaches follow the target velocity with high fidelity. This also implies a very good agreement in engine speeds (Figure 7b) and a relatively good agreement of BMEPs (Figure 7c). The BMEP values do not match at all individual time instances but rather show an appropriate mean agreement over longer time spans. This fact is in agreement with the forward-facing

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Figure 5: Comparison of measured and simulated engine at steady-state points

Figure 6: Exhaust manifold pressure (a) and effective torque (b) for different model approaches at 6000rpm full load

modeling approach, where driver actions depend on the difference between current and target velocity and thus small differences in actual velocity might result in different driver actions. Moreover, different modeling depths feature different delay times between load signal variation and torque response.

For example, physical gas path models inherently feature delays between throttle position variation and a variation of the in-cylinder mass, since the aspired mass flow is mostly influenced by the intake manifold pressure that responds with delay due to its finite capacity.

It can be seen from Figure 7 that differences between modeling approaches are much more pronounced for the subsequent severe acceleration period taking place between second 86 and 92. It is discernible from Figure 7a that no engine modeling approach is capable of following the target velocity trace as the engine is not capable of providing sufficient power output to ensure sufficiently fast vehicle acceleration. This is due to the fact that the selected real-life cycle velocities were recorded with a different vehicle. The pure map based model (MAP) responds differently than all other models. It produces much higher torque output compared to other modeling approaches. This can be attributed to

Figure 8: Vehicle Velocity (a), Engine Speed (b) and Engine Performance (c) for different engine models in transient drive cycle.

Figure 7: Vehicle Velocity (a), Engine Speed (b) and Engine Performance (c) for different engine models in transient drive cycle. Zoomed into segment between 80 and 105 seconds

the fact that the MAP model is based on the simple engine performance map. The engine responds instantaneously without considering any delays, where certainly the delay of accelerating the turbocharger features the largest time scale.

The engine torque output of the stoichiometrically operated homogeneous charge spark ignition engines is directly influenced by the in-cylinder charge mass. The cylinder charge mass strongly depends on the pressure in the intake manifold (Figure 9a) that is mainly influenced by the throttle opening (Figure 9d). Both these parameters coincide very well for all engine modeling approaches. However, besides the pressure in the intake manifold, the in-cylinder mass of fresh air depends also on the intake manifold temperature and species composition. Figure 9b and Figure 9c indicate that both properties (intake manifold temperature and combustion product concentration) differ significantly between the crank-angle resolved cylinder approaches (CRA4, CRA4M, CRA1) and surrogate cylinder block models (RVM INN). The latter models do not cover the effects that cylinder charge can be swept back into the intake system leading to significant amounts of combustion products and higher intake manifold temperatures

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Figure 10 shows a zoom into a short transient sequence of the drive cycle that is used to analyze phenomena outlined in the previous paragraph. It can be seen that the intake manifold pressure (Figure 10a) and the throttle position (Figure 10d) of all engine modeling approaches coincide well. It is also discernible from the figure that the intake temperature (Figure 10b) and the combustion product concentration (Figure 10d) of all modeling approaches coincide well up to second 86.5. In this period, the throttle is partially opened and the intake manifold pressure is slightly lower than 1bar. In this operating regime there is no significant backflow of exhaust products through the intake valve. Thus the temperature in the intake manifold is slightly above the ambient temperature caused by the compression in the compressor. This rather flat period is followed by a severe vehicle deceleration where the throttle is nearly closed. As a consequence of the closed throttle a severe and abrupt drop of the pressure in the intake manifold can be observed. All models that rely on the crank-angle based cylinder description (CRA4, CRA4M, CRA1) predict a significant increase of combustion product concentration (Figure 8c) in the intake manifold. This is driven by an intense backflow of the exhaust products through the intake valve. This backflow is governed by the large pressure difference between the intake and exhaust manifold. The backflow of the exhaust gasses also significantly increases the temperature in the exhaust manifold (Figure 8c). These phenomena, as they also can be observed in the real engines, are appropriately captured by the models relying on crank-

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Figure 10: Intake plenum pressure (a), temperature (b), combustion product concentration (c) and dimensionless throttle opening angle (d) for five different engine models. Zoom into segment of transient drive cycle

Figure 9: Intake plenum pressure (a), temperature (b), combustion product concentration (c) and dimensionless throttle opening angle (d) for five different engine models. Zoom into segment of transient drive cycle

angle based cylinder approaches, whereas the exact resolution of the results certainly also corresponds to the 0D modeling depth. Contrary to that, the surrogate cylinder block approaches (RVM , INN) are not capable of modeling back-flows. Thus combustion product concentration is always zero in the intake manifold as the engine does not have an external EGR line. The absence of backflow and the sudden expansion for a pressure ratio of more than ten also result in a severe drop in the intake manifold temperature (Figure 10b). Here heat transfer from the walls is not capable of compensating the temperature drop due to the short period of time. The temperature thus drops to an unrealistically low value, which still is modeled physically plausible. However, it originates from the inability of the surrogate models to capture backflow. The above results indicate that this deficiency of the surrogate cylinder block models does not necessarily result in drawbacks when modeling engine torque. The torque surrogate can be trained based on the pressure in the intake manifold. However, this modeling depth certainly cannot be used to provide reasonable inputs to emission models due to its inability of adequately predicting temperature and gas composition in the intake manifold.

CONCLUSION

This study shows a comprehensive comparison of different engine modeling depths of AVL CRUISE M assessed during both steady-state and transient drive cycle simulations. The models comprise 1D and 0D crank-angle resolved approaches in the gas path and in the cylinders, mean value gas path approaches combined with crank-angle resolved cylinders blocks, mean value gas path approaches combined with surrogate cylinder blocks, transient engine surrogate models and steady-state map-based engine descriptions.

The comparison of different approaches shows that crank-angle resolved cylinder block approaches are favorable for MiL and SiL applications in early phases of the engine development process as they are capable of responding physically plausible to changed inputs in hardware or controls.

Contrary to that, surrogate modeling approaches feature superior computational speed what makes them suitable for HiL, but they require a large database of reference data.

Two main conclusions can be drawn from the present study. First, different model depths are needed and used for different simulation tasks at different stages of the engine development process. Second, physical based engine models with crank-angle resolved cylinder block descriptions are the most promising approach.

REFERENCES

1. Wanker R., Wurzenberger J. C. and Schuemie H. A., „Advanced 0D/1D modeling of gasoline combustion pollutant

formation and aftertreatment systems“, SIA Conference:, Strasbourg 2007.

2. Wurzenberger J. C., Heinzle R., Schuemie H. A. and Katrašnik T.., “Crank-Angle Resolved Real-Time Engine

Simulation –Integrated Simulation Tool Chain from Office to Testbed”, SAE Technical Paper 2009-01-0589, 2009.

DEFINITIONS, ACRONYMS, ABBREVIATIONS

BMEP Brake Mean Effective Pressure

CFL Courrant Friedrich Levy

CRA Crank-Angle

DI Direct Injection

dINN Dynamic Intelligent Neural Network

DoE Design of Experiment

ECU Engine Control Unit

EGR Exhaust Gas Recirculation

HiL Hardware in the Loop

HSDI Turbocharged High Speed Diesel

Engine

IC Internal Combustion

ID Injection Duration

INN Intelligent Neural Network

MCC Mixture Controlled Combustion

MiL Model in the Loop

MVEM Mean Value Engine Model

PID Proportional Integral Differential

PT1 Low pass filter

ROHR Rate of Heat Release

RT Real-Time

RVM Relevance Vector Machine

SOI Start of Injection

SVM Support Vector Machine

TGDI Turbocharged Gasoline Direct Injection

VTG Variable Turbine Geometry

VVT Variable Valve Timing

xD x Dimensional