The Virtual Vehicletransient Simulation in the diesel engine development processConcerning the topic “Motor Process Simulation in Realtime – Basics and Application Possibilities”, a dissertation was worked out at the Technische Universität Berlin, which received the “Hermann Appel Preis 2008” in the area powertrain development. This paper reports about the continuation of the scientific work in the current activities of modelling and simulation at IAV GmbH: Straight forward high-fidelity system simulation delivers solutions from module concept evaluation up to real-time application for controls testing ahead of the experiment on the engine dyno or vehicle.

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1 Introduction

IAV uses a modelling software environ-ment for the advanced diesel develop-ment. The environment consists of com-mercial and in-house developed hard- and software as well as model libraries in or-der to handle the growing complexity. The library is intensely supported by measure-ment data sources from engine and mod-ule test benches as well as by the controls development departments, Figure 1. The simulation activities are embedded in the advanced development infrastructure which consists of single cylinder and full-size engine test benches as well as vehicle and component test benches.

As in reality a virtual test cycle has to consider all effects that are emission rele-vant as cold start (friction, warm-up) in-cluding the appropriate control strate-gies. The modelling platform is flexible concerning degree of detail, calculation speed and connectivity. The various test data sources in conjunction with the controls development make a fast and ef-ficient simulation of a test cycle possible. Typical overall vehicle simulation stud-ies focus on:– exhaust raw emission reduction and ex-

haust gas aftertreatment system layout– turbocharger (TC) matching in con-

junction with different exhaust gas recirculation (EGR) systems [1]

– examination of energy flow and en-ergy recuperation systems [2].

The Authors

Dr.-Ing. Ingo Friedrichis team manager Ad­vanced diesel engines in the business area powertrain mecha­tronics at IAv GmbH in Berlin (Germany).

Dr.-Ing. Ralf Buchwaldis technical consultant thermodynamics Ad­vanced diesel engines in the business area powertrain mecha­tronics at IAv GmbH in Berlin (Germany).

Dipl.-Ing. Ansgar Sommeris head of department Advanced diesel engines in the busi­ness area powertrain mecha tronics at IAv GmbH in Berlin (Germany).

Figure 1: R&D environment supporting modelling and simulation

Dr.-Ing. Eckhard Stölting is team manager diesel engine Control design in the business area powertrain mecha­tronics at IAv GmbH in Gifhorn (Germany).

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Prerequisites here are a physics-based plant model and an appropriate model pa-rameterisation as well as the parameteri-sation of the necessary controls model.

2 Identification of Software Tools

The analysis of the overall vehicle behav-iour in conjunction with a high level of detail of single components causes unac-ceptable calculation times. Fast calculat-ing models allow a high number of pa-rameter variations in a limited time frame. Applications for the validation of ECU functionalities in a software in the loop (SiL) or hardware in the loop (HiL) environment have even higher demands on calculation speed up to real-time cap-

ability for the latter. Today physics based models replace more and more map-based models for these applications be-cause of their superiority in terms of mapping transient behaviour or causal relationships between in- and outputs. Increasing calculation power of today’s CPUs allows a fast execution of rather de-tailed engine models [3].

For a flexible simulation of the com-bustion engine in the overall vehicle en-vironment in terms of degree of detail, calculation performance and connectiv-ity a couple of questions concerning model depth and choice of simulation tools have to be answered. One possible way of model classification can be done with a level of abstraction. Early stages of engine development demand a high

degree of abstraction. Figure 2 assigns a model type for different vehicle subsys-tems to a degree of abstraction with one example of application. The degree of detail can individually be chosen for each subsystem depending on the project definition, the available model-ling data resources and the calculation time constraint. In that way the opti-mum simulation quality and efficiency can be reached. For example the com-bustion engine can be simulated in less than real-time using a 0D-gas exchange calculation combined with a mean- value model-based on an artificial neu-ral network for the in-cylinder process. If the energy and heat management simulation is the main scope, a high de-gree of detail can be chosen here – for example for subsystem specific concept or parametric studies – at minimised overall calculation time.

Figure 3 shows an exemplary segmen-tation into subsystems and the appropri-ate software tool in the chosen tool chain for an overall vehicle simulation. The software tools are linked together via the vehicle simulation software VeLoDyn (Ve-hicle Longitudinal Dynamics) based on Matlab/Simulink which serves as integra-tion platform since most of today’s off-the-shelf tools offer this interface. Fur-thermore Matlab/Simulink is the ade-quate software for controls development and for compiling hardware-independ-ent code with the real-time workshop.

Depending on the application GT- Power or Themos is used for the engine process, Dymola/Modelica for energy management, cooling system, powertrain and electric system and AxiSuite or KAT-Sim [4] for exhaust gas aftertreatment.

3 Engine Modelling

Engine modelling mainly focuses on the two following points:– the gas exchange including the turbo/

supercharging and EGR system– the combustion process with energy

flow analysis and engine out emission evaluation.

Both should be calculated with high ac-curacy for transient operation – ideally in real-time and three dimensional. Howev-er three-dimensional overall engine mod-elling is still far away from reasonable

Figure 2: Possible degrees of detail exemplary for five vehicle subsystems

Figure 3: Typical segmentation into subsystems for an overall vehicle simulation

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calculation time for overall vehicle simu-lation making a reduction of the degree of detail unavoidable. GT-Power is used here for engine modelling supplemented by zero-dimensional single and two-zone combustion and exhaust emission calcu-lation models developed and experimen-tally validated in-house. These models are also used for online as well as for real-time cylinder pressure data analysis on the test bench and moreover for control algorithm development or for real-time combustion model development.

Figure 4 shows the environment being used in typical projects. The single cylin-der engine delivers engine data at an early stage of development to IAV’s OpenTDA for detailed combustion analysis. The re-sults of the calculation and the compo-nent test bench data (that means turbo-charger, heat exchanger, fuel injector etc.) serve as input parameters for the GT-Pow-er engine and the AxiSuite exhaust gas aftertreatment model with heat release curves, exhaust emission data, valve dis-charge coefficients etc. After model ad-justment and verification the virtual full-size engine with exhaust gas aftertreat-ment model is ready for performing pa-rameter variations or concept studies.

For the development and calibration of advanced control functions the “Mod-ular Prototype Engine Controller” (IAV-MPEC) [5] is used. It can be run in rapid-prototyping or full-path mode. Its com-ponents are:– TRA (Thermodynamic Real-Time Anal-

ysis) for thermodynamic cylinder-pressure analysis

– MBCAP (Model-based Controlled Air Path) for the transient TC and EGR sys-tem control

– FI2RE (Flexible Injection and Ignition for Rapid Engineering) for all types of ignition and fuel injection system control

– AC3 (Advanced Closed Loop Combus-tion Control)

– HyWaCoS (Model-based Injection Con-trol)

– Model-based SCR control.To use synergetic effects IAV-MPEC is strongly linked to the IAV simulation tools TR-Sim (Thermodynamic Real-Time Simulation) and Themos (by IAV and Tesis) for combustion process simula-tion. Through the repeated interchange and comparison of simulated and meas-

ured data the engine simulation quality evolves step by step to a high level con-cerning precision, reusability and calcu-lation time. On this basis of system un-derstanding the extrapolation of the en-gine behaviour can be extended. For an overall vehicle simulation vehicle, driv-er, power train and auxiliary systems have to be modelled with adequate pre-cision, too.

4 Vehicle and Auxiliary Systems Modelling

The graphical user interface shown in Figure 5 represents the modular architec-ture of the integration platform VeLoDyn with its basic submodels and the signal

and control bus connections. The main parts are drive train (including engine, clutch and transmission), vehicle, driver and road profile supplemented by vehicle and control models (SoftECU). Within this system each submodel can be re-placed by another software tool via its Matlab simulation interface. Other fea-tures are the options to compile a real-time capable vehicle model with Matlab’s real-time workshop and/or to transfer the vehicle model to the engine test bench for emulating different drive cycles by controlling engine torque and speed with different parameter sets for gear ratios or gear shift strategies in order to optimize fuel consumption and emissions.

The aim of the SoftECU shown in Figure 6 is not to copy the functionality of

Figure 4: Modelling process

Figure 5: Modular structure of the Matlab-based integration tool VeLoDyn

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a real ECU or to display prototype func-tions, but rather to control all virtual ac-tuators as fuel injectors, VTG, EGR valves, intake throttle, VVA etc. during transient simulation in an optimal way, using all the information available from the simu-lation model. In order to obtain variables of interest as for example oxygen amount and peak pressure in the cylinder, EGR mass flow, or DPF loading the real ECU code has complex observers implemented since this information can not be meas-ured directly with sensors. Since the SoftECU has access to all this information directly, the complexity of the SoftECU is rather small in comparison to the real world ECU or even prototype functions.

5 Vehicle Model for Evaluating Euro-6 Diesel Car Concepts

In the following example NOx tailpipe emission and fuel consumption for a Die-sel-powered vehicle in the NEDC are com-puted and optimized. The target vari-ables are to be optimised with:– engine-based measures (single and

two-stage turbo charging combined with high/low pressure EGR, match-ing and warm-up optimisation) and

– emission aftertreatment measures (matching, calibration and control of the DeNOx (SCR) and oxidation cata-lyst system)

in order to find the optimal solution for the overall system.

In this case offline computation of the type described in Figure 7 can be seen as

the state-of-the-art methodology: The en-gine variations are simulated offline at steady-state operating points in the repre-sentative map area of the driving-cycle. The scope is the simulation of the input variables for the exhaust gas aftertreat-ment system: exhaust temperature and mass flow as well as exhaust gas composi-tion, particularly NOx. Here, NOx raw emission is computed using a semi-em-pirical model based on measured data. The resulting maps are implemented in VeLoDyn in order to perform the driving-cycle simulation in conjunction with the Dymola model for the parallel comput-ing of the transient engine warm-up process, including its gas-exchange com-ponents. The resulting time-based pro-files for exhaust gas temperature, mass flow and gas composition downstream of

the turbine now serve as input for the Ax-iSuite model which computes the ther-modynamics and reaction kinetics in the exhaust gas aftertreatment system with differing degree of detail. The AxiSuite results include the time-based curves for gas temperatures at relevant positions in the exhaust system as well as for NH3 and NOx mass flow. In an iterative process the GT-Power, VeLoDyn and AxiSuite model parameters have to be recalibrated until the optimal solution for engine, gear box and catalytic system is found.

Above the offline method there is the option of running all involved software tools synchronous in an adequate step size. This online simulation offers the ad-vantage that optimization procedures concerning engine parameters and after-treatment design parameters can be done during runtime. The realisation of an online simulation environment using the named software tools requires:– an adequate selection of the degree of

model detail– a subsystem specific choice of solver

step size in order to secure numeric stability

– a feedback control concept.The control concept is implemented in the SoftECU described before.

Figure 8 shows the simulation result for turbine outlet temperature, NOx con-version rate as well as urea and fuel con-sumption in the NEDC for the single- and two-stage TC engine. The latter is matched to a 20 % higher rated power compared to the single-stage engine both aiming at the same exhaust emission and fuel con-

Figure 6: SoftECU structure

Figure 7: Methodology for serial simulation of tailpipe exhaust emission in the driving cycle using GT-Power, VeLoDyn, Dymola and AxiSuite

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sumption level. For comparability the configuration of the exhaust gas after-treatment system is kept identical.

Immediately apparent in the case of the two-stage turbocharger system is the less steep temperature rise and the sig-nificantly lower temperature level down-stream of the turbine, resulting in less favourable conditions for the downstream exhaust gas aftertreatment components. The two-stage turbocharger system emits a significantly higher level of NOx be-cause of a much lower SCR conversion rate (65 % for single-stage, 39 % for two-stage) which doesn’t meet the develop-ment aim of 70 % of the Euro 6 level any-more. Fuel consumption for the single-stage turbocharger system is also about 8 % lower. Already at the simulation stage, the altogether lower level of NOx conversion, particularly with the two-stage turbocharger, demonstrates the problem of optimising the layout of the EGR-, turbocharger- and SCR system. This is because of the relatively low exhaust gas temperature level throughout the test cycle. Further optimisation is neces-sary here, for example by adapting en-gine calibration with the aim of shorten-ing the light-off phase and achieving a higher exhaust gas temperature.

Figure 9 depicts the simulation result for engine heat-up acceleration by means of a retarded start of fuel injection, a de-activation of the piston cooling and a post injection in order to raise engine-out gas temperature in the first 180 s of the test cycle. However, these measures

lead to a higher fuel consumption, which means that for this application the use of engine-based improvement measures must be weighted against improvement measures on the exhaust gas aftertreat-ment side.

6 Model-based Controls Algorithm Development Process

The development of automotive control algorithms is done according to the V-process, Figure 10. In the first step a con-cept has to be set up followed by the speci-fication and finally the realisation and

implementation respectively. In the right branch the implementation will be inte-grated into the overall system followed by the test against the specified target values from the left branch. The final calibration brings out whether the process can be verified. While working through the V-process complexity grows due to the steadily increasing degree of detail. If an error has been made at the beginning and has been taken all the way through the process, its localisation and correction re-quires a large amount of time.

For the purpose of early detection of errors simulation is advantageous. Phys-ics based models are well suited for the concept phase, since they are able to rep-resent the basic geometric attributes for an at least rough study of the system’s characteristics. Data driven black-box models mostly can’t be used in that early phase because generally no data source is yet available. The developed models can also be used for studying system be-haviour, which is of great importance for the definition of advanced solutions.

The engine model from the concept phase is also used in the specification phase for the layout of the control algo-rithms. This is done by continuous tests under pre-specified conditions against the engine model (MiL). In the phase of code generation the graphics-based speci-fication of the software will be ported to a basic computer language, for example C-code. Errors that may occur during this

Figure 8: Simulation results – comparison of turbine outlet temperatures for single and dual stage turbo charging

Figure 9: Simulation results – measures for system heat-up of exhaust-gas aftertreatment

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transformation will be corrected by itera-tive tests in short loops (SiL). In that phase the model based development is unique by making a testing against the reference test cases from the specification phase possible.

When the integration phase is reached it can be assured that the modules fits the specification due to the MiL and HiL tests. Accordingly the tests in the integration phase can be focused on the correct inter-action between the modules. A stimulus and monitoring of the modules can only be done via the I/O interface of the ECU. Therefore the ECU is driven in combina-tion with the engine model (HiL). For these tests the calibration from the specification phase as well as those test cases that are related to the overall system can be used.

Concerning the calibration phase mod-el-based development not only provides a higher degree of maturity but also a basic system calibration ready for the detailed optimisation. Through this the time to sys-tem launch can be remarkably shortened.

Although model-based system develop-ment shows significant advantages it is not (yet) completely accepted. The main obstacle is seen in the effort necessary to built and parameterize a suitable engine model. Providing the parameter set of an existing model combination from the en-gine and aftertreatment concept or de-sign phase (GT-Power and AxiSuite) to a physics based real-time capable engine

model environment (Themos and KAT-Sim) can be very efficient, since for both applications most of the necessary param-eters are compatible. The contradicting demands for real-time simulation con-cerning model stability and the numeri-cal error at large time stepping are still a challenge. Typically a compromise has to be found between step size and accuracy.

7 Summary and Conclusion

The increasing demands on Diesel com-bustion engines concerning exhaust emissions, fuel consumption and power density lead to a growing complexity of all involved systems. The contradictory need for further reducing the develop-ment time can therefore only be reached by an adaptation and improvement of the development methodology.

The IAV’s approach focuses on a phys-ics-based system view beginning at a very early state of development and attending to the series production. For this purpose the simulation environment has to be easily adaptable, scalable in terms of de-gree of detail and of high connectivity. The described hard- and software envi-ronment for the overall vehicle simula-tion has a heterogeneous structure based on off-the-shelf and in-house developed software. It can be adapted also to future requirements through the usage of an

integration platform based on Matlab/Simulink. With this software environ-ment it is additionally possible to find solutions for real-time applications with specialized tools as Themos and KATsim.

References[1] Birkner, C.; Jung, C.; nickel, J.; offer, t.; von Rüden,

K.: durchgängiger einsatz der Simulation beim mo­dellbasierten entwicklungsprozess am Beispiel des ladungswechselsystems – von der Bauteilausle­gung bis zur Kalibration der Regelalgorithmen. Hdt­tagung Simulation und Aufladung, Berlin, 2005

[2] Kitte J.; tietze, t.; Jänsch, d.; Bals, R.: model­lierung und Simulation in dymola/modelica als Basis zur entwicklung innovativer Wärmemanage­mentstrategien. In: Wärmemanagement des Kraft­fahrzeugs vI. S. 252 ff., ISBn 978­3­8169­2820­1, expert­verlag, Renningen, 2008

[3] Friedrich, I.: motorprozess­Simulation in echtzeit – Grundlagen und Anwendungsmöglichkeiten. dissertation, tU Berlin, Shaker­verlag 2008

[4] langeheinecke, K.; dusemund, S.; Schneider, H.; Bank, R.: KAtSim – ein Werkzeug zur numerischen Simulation von Abgaskatalysatoren. In: mtZ 70 (2009), nr. 11, S. 858 – 863

[5] Stölting, e.; Seebode, J., Gratzke, R.; Behnk, K.: emissionsgeführtes motormanagement für nutzfahrzeuganwendungen. In: mtZ 69 (2008), nr. 12, S. 1042 – 1049

Figure 10: Controls algorithm development process

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