Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Optimal calibration scheme for map-based control of diesel engines

  • 92 Accesses

  • 7 Citations


Map-based control has the advantage of a simple control structure. However, in the case of a complex plant, calibrating the map is difficult. Calibration methodology using plant model is increasing in engine calibration industries. However, the models are for steady-state operation and cannot simulate at transient operation well. This study introduces the dynamic empirical model and applies the transient corrective function of an electronic control unit to simulate more realistic behavior. The optimization problem for calibrating maps of diesel engines is constructed, and the formulation of cost function and constraints is discussed. Consequently, the proposed calibration scheme can find an optimal map in satisfied constraints. Finally, the optimized maps are validated using mass production diesel engines.

This is a preview of subscription content, log in to check access.


  1. 1

    Angridge S, Fessler H. Strategies for High EGR Rates in a Diesel Engine. SAE Technical Paper 2002–01-0961. 2002

  2. 2

    Nishio Y, Hasegawa M, Tsutsumi K, et al. Model Based Control for Dual EGR System with Intake Throttle in New Generation 1.6L Diesel Engine. SAE Technical Paper 2013–24-0133. 2013

  3. 3

    Neely G, Sasaki S, Huang Y, et al. New Diesel Emission Control Strategy to Meet US Tier 2 Emissions Regulations. SAE Technical Paper 2005–01-1091. 2005

  4. 4

    Ohata A. A desired modeling environment for automotive powertrain controls. In: Identification for Automotive Systems. London: Springer, 2012. 13–34

  5. 5

    Baumann W, Dreher T, Röpke K, et al. DoE for series production calibration. In: Proceedings of the 7th Conference on Design of Experiments (DoE) in Engine Development, Berlin, 2013

  6. 6

    Haukap C, Barzantny B, Röpke K. Model-based calibration with data-driven simulation models for non-DoE experts. In: Proceedings of the 6th Conference on Simulation and Testing for Automotive Electronics, Berlin, 2014

  7. 7

    Murata Y, Kato Y, Kanda T, et al. Application of model based calibration to mass production diesel engine development for indian market. In: Proceedings of the 8th Conference on Design of Experiments (DoE) in Engine Development, Berlin, 2015

  8. 8

    Brahma I, Rutland C. Optimization of Diesel Engine Operating Parameters Using Neural Networks. SAE Technical Paper 2003–01-3228. 2003

  9. 9

    Brahma I, Sharp M C, Frazier T R. Empirical modeling of transient emissions and transient response for transient optimization. SAE Int J Engines, 2009, 2: 1433–1443

  10. 10

    Berger B, Rauscher F, Lohmann B. Analysing Gaussian processes for stationary black-box combustion engine modelling. IFAC Proc Volumes, 2011, 44: 10633–10640

  11. 11

    Mrosek M, Sequenz H, Isermann R. Control oriented NOx and soot models for diesel engines. IFAC Proc Vol, 2010, 43: 234–239

  12. 12

    Atkinson C, Mott G. Dynamic Model-Based Calibration Optimization: an Introduction and Application to Diesel Engines. SAE Technical Paper 2005–01-0026. 2005

  13. 13

    Atkinson C, Allain M, Zhang H. Using Model-Based Rapid Transient Calibration to Reduce Fuel Consumption and Emissions in Diesel Engines. SAE Technical Paper 2008–01-1365. 2008

  14. 14

    Sakushima N, Wolf B, Karsten R, et al. Transient modeling of diesel engine emissions. Int J Autom Eng, 2013, 4: 63–68

  15. 15

    Berger B. Modeling and optimization for stationary base engine calibration. Dissertation for Ph.D. Degree. Munich: The Technical University of Munich, 2012

  16. 16

    Niedernolte H, Kloepper F, Mitterer A, et al. Workflow for data evaluation during basic calibration of combustion engines. In: Proceedings of IEEE Conference on Computer Aided Control System Design, IEEE International Conference on Control Applications, IEEE International Symposium on Intelligent Control, Munich, 2006. 2060–2065

  17. 17

    Fukuhara K, Murata Y, Nishio Y, et al. Dynamic MBC methodology for transient engine combustion optimization. In: Proceedings of International Calibration Conference Automotive Data Analytics, Methods, DoE, Berlin, 2017

  18. 18

    Shishido T, He J, Kaihatsu M, et al. Dynamic modeling for gasoline direct injection engines. In: Proceedings of International Calibration Conference Automotive Data Analytics, Methods, DoE, Berlin, 2017

  19. 19

    Bishop C M. Pattern Recognition and Machine Learning (Information Science and Statistics). New York: Springer, 2007. 138–147

  20. 20

    Behr L, Zimmermann U, Trinkert S, et al. Increased efficiency in the calibration process of automotive Li-ion battery systems. In: Proceedings of Internationales Stuttgarter Symposium, Wiesbaden, 2016. 101–115

  21. 21

    Baumann W, Schaum S, Ropke K, et al. Excitation signals for nonlinear dynamic modeling of combustion engines. In: Proceedings of the 17th World Congress the International Federation of Automatic Control, Seoul, 2008

Download references

Author information

Correspondence to Yui Nishio.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Nishio, Y., Murata, Y., Yamaya, Y. et al. Optimal calibration scheme for map-based control of diesel engines. Sci. China Inf. Sci. 61, 70205 (2018). https://doi.org/10.1007/s11432-017-9381-6

Download citation


  • optimization
  • Gaussian
  • SQP
  • Diesel engine
  • calibration
  • DoE