Mathematical Modeling
Vol. 5 (2021), Issue 1, pg(s) 21-26

MATHEMATICAL MODELLING OF TECHNOLOGICAL PROCESSES AND SYSTEMS

Engine model for control concepts and e-fuel identification for recreational vehicles and hand-held tools

  • 1 University of Applied Sciences Upper Austria, Austria

Abstract

Mean value models are essential for developing control engineering methods, e.g., speed control concepts, failure monitoring features. This work briefly summarizes the main parts of such a second-order mean value model, suitable for control engineering purposes. The nonlinear model is linearized around one operating point, and a linear state controller is designed, both for single-input and multipleinput, without and with an integral component to eliminate steady-state deviations. Limitations of the proposed linear control concepts, potential advantages of nonlinear flatness-based control concepts, and possible extensions of the model for fuel identification are discussed in the outlook.

Keywords

References

  1. ] E. Hendricks, “A Compact, Comprehensive Model of Large Turbocharged, Two-Stroke Diesel Engines,” SAE Technical Paper 861190, 1986. [Online]. Available: https://www.sae.org/publications/technical-papers/content/861190/
  2. E. E. Streit and G. L. Borman, “Mathematical Simulation of a Large Turbocharged Two-Stroke Diesel Engine,” SAE Technical Paper 710176, 1971. [Online]. Available: https://www.sae.org/publications/technical-papers/content/710176/
  3. J. Cook, J. Grizzle, and J. Sun, “Engine Control,” in The Control Handbook. [Online]. Available: http://worldcatlibraries.org/wcpa/oclc/246910471
  4. J. D. Powell, “A Review of IC Engine Models for Control System Design,” IFAC Proceedings Volumes, vol. 20, no. 5, pp. 235–240, 1987, doi: 10.1016/S1474-6670(17)55378-1.
  5. K. H. Ahn, “Estimation of Ethanol Content and Control of Air-to-Fuel Ratio in Flex Fuel Vehic les,” Dissertation, Mechanical Engineering, University of Michigan, Michigan, USA, 2011.
  6. T. Coppin and N. Maamri, “Fuel estimation and air-to-fuel ratio control for Flexfuel spark-ignition engines,” in 2010 IEEE International Conference on Control Applications, Yokohama, Japan, 2010, pp. 555–560.
  7. K. Ahn, A. G. Stefanopoulou, and M. Jankovic, “AFR-Based Fuel Ethanol Content Estimation in Flex-Fuel Engines Tolerant to MAF Sensor Drifts,” IEEE Trans. Contr. Syst. Technol., vol. 21, no. 3, pp. 590–603, 2013, doi: 10.1109/TCST.2012.2187786.
  8. L. Guzzella and C. H. Onder, Introduction to Modeling and Control of Internal Combustion Engine Systems, 2nd ed. s.l.: Springer-Verlag, 2010. [Online]. Available: http://site.ebrary.com/lib/alltitles/docDetail.action?docID=10 359800
  9. G. P. Rao and H. Unbehauen, “Identification of continuous-time systems,” IEE Proceedings - Control Theory and Applications, vol. 153, no. 2, pp. 185–220, 2006, doi: 10.1049/ip-cta:20045250.
  10. S. Mayr, G. Grabmair, and J. Reger, “Input design and online system identification based on Poisson moment functions for system outputs with quantization noise,” in 2017 25th Mediterranean Conference on Control and Automation (MED), 2017, pp. 23–29.
  11. J. Lunze, Regelungstechnik 2: Mehrgrößensysteme, Digitale Regelung, 9th ed. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016.
  12. G. Grabmair and R. Gahleitner, “PI-Zustandsregler - eine methodische Neubetrachtung,” (in de), at - Automatisierungstechnik, vol. 67, no. 9, pp. 727–738, 2019, doi: 10.1515/auto-2019-0037.
  13. K. Ahn, A. G. Stefanopoulou, and M. Jankovic, “Puddle Dynamics and Air-to-Fuel Ratio Compensation for Gasoline- Ethanol Blends in Flex-Fuel Engines,” IEEE Trans. Contr. Syst. Technol., vol. 18, no. 6, pp. 1241–1253, 2010, doi: 10.1109/TCST.2009.2035441.

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