Concept Studies of SI Engines 2030+ Including RDE: Challenging Tasks for the Powertrain Simulation

  • Michael GrillEmail author
  • Mahir Tim Keskin
  • Michael Bargende
Conference paper
Part of the Proceedings book series (PROCEE)


Nowadays 0D/1D simulations are being widely used in the engine development process. Thanks to the high prediction quality of the models and the low computational times, they are powerful tools to investigate engine and powertrain concepts for the years 2030+.

Nearly all SI-powertrains will have at least a 48 V hybridization in 2030+. As the electric drivetrain will be active above all in the low-load range, typical part-load efficiency measures will become less relevant, whereas efficiency improvements in the area of 40–80% load will become crucial. Hence, new concepts are needed to guarantee the efficient SI-engine operation in a very wide range of engine operating conditions. In this context, technologies such as lean engine operation, high load EGR or long stroke engines emerge as new, challenging tasks in the 0D/1D simulation.

In order to achieve high model prediction accuracy, 0D/1D models used for the simulation of future SI engine concepts do not only have to consider thermodynamic effects, but also need to account for the in-cylinder chemical processes in detail. Kinetic reaction mechanisms for different fuel types that have been intensively developed in the recent years can be used to perform simulations at in-cylinder conditions that provide better understanding of the chemical processes during the combustion. More importantly, they enable the development and validation of simplified approaches for the 0D/1D simulation that reproduce the real chemistry behavior very accurately.

In this way, correlations yielding exact values for the laminar flame speed of different fuels have been developed at FKFS, making the reliable predictive simulation of lean combustion possible. A CCV model can then be used to estimate the possible engine operation region and the changes in fuel consumption resulting from the cycle-to-cycle variations. Furthermore, the reaction kinetics simulations at in-cylinder conditions proved that, at specific boundary conditions, the auto-ignition of the unburnt mixture resulting in knock happens in two stages. This phenomenon significantly influences the ignition delay of the mixture and characterizes gasoline fuels. Based on these findings, a new knock modeling approach was developed.

For SI-engine concepts based on mixture dilution in the years 2030+, one of the most important challenges will be the management of the exhaust gas temperature during RDE to avoid light-out of exhaust aftertreatment components. The expected increase in the engine’s thermal efficiency will lower the exhaust gas temperatures, making the aftertreatment components even more prone to light-out. It is thus crucial for future engine concepts to simulate this behavior in RDE situations correctly, which is a challenging task for a 1D simulation. The combination of a 1D flow path model and a quasi-dimensional burn rate model leads either to inacceptable simulation times or to very inaccurate results. A new approach introduced by FKFS and Bosch in June 2018 with a combination of a physics-based modelling of the gas exchange and a data-based model of the high-pressure part, aiming to resolve this dilemma, will be discussed in this context.


Powertrain simulation 0D/1D modelling RDE 


  1. 1.
    Grill M., Keskin M.T., Bargende M., Fasse S., Hann S. (2019) Concept Studies 2025+: Challenging Tasks in 0D/1D Engine Simulation. In: Liebl J. (eds) Ladungswechsel und Emissionierung 2018. Proceedings. Springer Vieweg, Wiesbaden.Google Scholar
  2. 2.
    Malcher, S., Bargende, M., Grill, M., Baretzky, U., Diel, H., Wohlgemuth, S., & Röttger, G. (2018). Investigation of Flame Propagation Description in Quasi-Dimensional Spark Ignition Engine Modeling (No. 2018-01-1655). SAE Technical Paper.Google Scholar
  3. 3.
    Hann, Sebastian, Michael Grill, and Michael Bargende. “Laminare Flammengeschwindigkeit Abmagerung und Hochlast-Abgasrückführung in der Motorsimulation.” MTZ-Motortechnische Zeitschrift 79.4 (2018): 28–35.CrossRefGoogle Scholar
  4. 4.
    Hann, Sebastian, Michael Grill, and Michael Bargende. Reaction Kinetics Calculations and Modeling of the Laminar Flame Speeds of Gasoline Fuels. No. 2018-01-0857. SAE Technical Paper, 2018.Google Scholar
  5. 5.
    J. B. Heywood, Internal combustion Engine Fundamentals, New York: McGraw-Hill, 1988.Google Scholar
  6. 6.
    Ö. Gülder, „Correlations of Laminar Combustion data for Alternative S.I. Engine Fuels,“ SAE Technical Paper Series, Nr. 841000, 1984.Google Scholar
  7. 7.
    C. K. Law, A. Makino und T. Lu, „On the Off-Stoichiometric Peaking of Adiabatic Flame Temperature,“ Combustion and Flame, Nr. 145, pp. 808–819, 2006.CrossRefGoogle Scholar
  8. 8.
    S. Hann, L. Urban, M. Grill und M. Bargende, „Influence of Binary CNG Substitute Composition on the Prediction of Burn Rate, Engine Knock and Cycle-to-Cycle Variations,“ SAE Int. J. Engines, Bd. 10, Nr. 2, pp. 501–511, 2017.CrossRefGoogle Scholar
  9. 9.
    J. Ewald, A Level Set Based Flamelet Model for the Prediction of Combustion in Homogeneous Charge and Direct Injection Spark Ignition Engines, Aachen: Ph.D. thesis, Rheinisch-westfälische Technische Hochschule, 2006.Google Scholar
  10. 10.
    M. Grill und M. Bargende, „The Development of an Highly Modular Designed Zero-Dimensional Engine Process Calculation Code,“ SAE Paper, Bde. %1 von %22010-01-0149, 2010.Google Scholar
  11. 11.
    C. Bossung, M. Bargende, O. Dingel und M. Grill, „A quasi-dimensional charge motion and turbulence model for engine process calculations,“ 15. Internationales Stuttgarter Symposium, 17./18 März 2015.Google Scholar
  12. 12.
    S. Hann, L. Urban, M. Grill und M. Bargende, „Prediction of burn rate, knocking and cycle-to-cycle variations of binary compressed natural gas substitutes in consideration of reaction kinetics influences,“ IJER, 2017.CrossRefGoogle Scholar
  13. 13.
    M. Bargende und M. Grill, „Zukunft der Motorprozessrechnung und 1D-Simulation,“ Motortechnische Zeitschrift, Nr. Jubiläumsausgabe, 2014.Google Scholar
  14. 14.
    M. Wenig, M. Grill und M. Bargende, „A New Approach for Modeling Cycle-to-Cycle Variations Within the Framework of a Real Working-Process Simulation,“ SAE International Journal of Engines, Bde. %1 von %22013-01-1315, Nr. 6, pp. 1099–1115, 2013.Google Scholar
  15. 15.
  16. 16.
    European Commission, „Regulation (EU) 2017/1151,“ Official Journal of the European Union, 7th July 2017.Google Scholar
  17. 17.
    European Commission, „Regulation (EU) 2017/1154,“ Official Journal of the European Union, 7th May 2017.Google Scholar
  18. 18.
    G. Fraidl und M. Westerhoff, „Addressing the statistically relevant operation modes,“ MTZ worldwide, pp. 24–26, June 2017.Google Scholar
  19. 19.
    H. Baumgarten, J. Scharf, A. Balazs und al., „Simulation-Based Development Methodology for Future Emission Legislation,“ 37th International Vienna Motor Symposium, 2016.Google Scholar

Copyright information

© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2020

Authors and Affiliations

  • Michael Grill
    • 1
    Email author
  • Mahir Tim Keskin
    • 1
  • Michael Bargende
    • 2
  1. 1.FKFS Forschungsinstitut für Kraftfahrwesen und Fahrzeugmotoren StuttgartStuttgartGermany
  2. 2.Institut für Verbrennungsmotoren und Kraftfahrwesen (IVK)University of StuttgartStuttgartGermany

Personalised recommendations