Skip to main content
SpringerLink
Log in

Experimental Modeling of Engines

  • Chapter
  • First Online:
Engine Modeling and Control

  • 5707 Accesses

Abstract

Several parts of engines cannot be modeled in a theoretical way only, because the mathematical formulation of parts of the processes is not precisely known or the computational expense is too large with regard to control and diagnosis applications. This holds for example for the flame propagation, pressure and temperature development of gasoline engines and for the spray development and combustion of diesel engines, both under the influence of valve-induced turbulent flow, swirl and tumble motion. Also precise and computational simple theoretical models for the emissions depending on several manipulated variables are not available.

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

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Amann H, Escher J (2008) Analysis III, 2nd edn. Birkhäuser, Basel

    Google Scholar 

  • Ayoubi M (1996) Nonlinear system identification based on neural networks with locally distributed dynamics and application to technical processes. Fortschr.-Ber. VDI Reihe 8, 591. VDI Verlag, Düsseldorf

    Google Scholar 

  • Babuska R, Verbruggen H (1996) An overview of fuzzy modeling for control. Control Engineering Practice – CEP 4(11):1593–1606

    Google Scholar 

  • Ballé P (1998) Fuzzy-model-based parity equations for fault isolation. Control Engineering Practice – CEP 7:261–270

    Google Scholar 

  • Bänfer O, Nelles O (2009) Polynomial model tree (polymot) – a new training algorithm for local model networks with higher degree polynomials. In: IEEE International Conference of Control and Automation, Christchurch, New Zealand, pp 1571–1576

    Google Scholar 

  • Bishop C (1995) Neural networks for pattern recognition. Oxford University Press, Oxford

    Google Scholar 

  • Blessing P (1979) Identification of input/output and noise dynamics of linear multivariable systems. In: 5th IFAC Symposium on Identification, Darmstadt, Germany

    Google Scholar 

  • Böhme D (2013) Methoden zur effizienten Basisapplikation für Luftpfad- und Abgastemperaturmodelle von Ottomotoren. Fortschr.-Ber. VDI Reihe 12, 767. VDI Verlag, Düsseldorf

    Google Scholar 

  • Böhme D, Isermann R, Martiny M, Kern J, Kerkau M (2009) Quasi-stationary measurement method for the ecu-functions of the cylinder charge determination and the torque model. In: 3rd International Symposium on Development Methodology, Wiesbaden, Germany

    Google Scholar 

  • Büchel M, Thomas M (2009) Roll-out of a fast calibration approach for engine base calibration. In: 3rd Internationales Symposium on Development Methodology, AVL, Wiesbaden

    Google Scholar 

  • Drewelow W (1990) Parameterschätzung nach der Ausgangsfehlermethode. Messen, Steuern, Regeln – MSR 33(1):15–23

    Google Scholar 

  • Ernst S (1998) Hinging hyperplane trees for approximation and identification. In: 37th IEEE Conference on Decision and Control, Tampa, FL, USA

    Google Scholar 

  • Eykhoff P (1974) System identification. John Wiley, London

    Google Scholar 

  • Fehl G (1992) Entwicklung einer Meßstrategie zur kontinuierlichen Erfassung von Kennlinien auf Motorenprüfständen. PhD thesis, Technische Hochschule Darmstadt, Darmstadt

    Google Scholar 

  • Fink A (2006) Nonlinear control based on local linear neuro-fuzzy models. Dissertation Technische Universität Darmstadt. Fortschr.-Ber. VDI Reihe 8, 1096. VDI Verlag, Düsseldorf

    Google Scholar 

  • Gschweitl K, Pfluegl H, Fortuna T, Leithgoeb R (2001) Steigerung der Effizienz der modellbasierten Motorenapplikation durch die neue CAMEO Online DoEToolbox. ATZ - Automobiltechnische Zeitschrift 103(7):636–643

    Google Scholar 

  • Hafner M (2002) Modellbasierte station¨are und dynamische Optimierung von Verbrennungsmotoren am Motorenprüfstand unter Verwendung neuronaler Netze. Dissertation Technische Universität Darmstadt. Fortschr.-Ber. VDI Reihe 12, 482. VDI Verlag, Düsseldorf

    Google Scholar 

  • Hafner M, Müller N, Semmler I S and Skranjk (2000a) Incorporation of neuro-fuzzy knowledge for fast measurement of combustion engine maps. In: The 2000 ASME International Mechanical Engineering Congress and Exposition, Orlando, Florida, vol 1, pp 585–590

    Google Scholar 

  • Hafner M, Schüler M, Nelles O, Isermann R (2000b) Fast neural networks for diesel engine control design. Control Engineering Practice – CEP 8:1211–1221

    Google Scholar 

  • Hafner S, Geiger H, Kreßel U (1992) Anwendung künstlicher neuronaler Netze in der Automatisierungstechnik. Teil 1: Eine Einführung. Automatisierungstechnische Praxis – atp 34(10):592–645

    Google Scholar 

  • Haykin S (1994) Neural networks. Macmillan College Publishing Company, Inc., New York

    Google Scholar 

  • Hecht-Nielson R (1990) Neurocomputing. Addison-Wesley, Reading, MA

    Google Scholar 

  • Holzmann H, Halfmann C, Germann S, Würtenberger M, Isermann R (1997) Longitudinal and lateral control and supervision of autonomous intelligent vehicles. Control Engineering Practice – CEP 5(11):1599–1605

    Google Scholar 

  • Hoschek J, Lasser D (1993) Fundamentals of computer-aided geometric design. AK Peters, Wellesley, MA

    Google Scholar 

  • Isermann R (1992) Identifikation dynamischer Systeme. Springer, Berlin

    Google Scholar 

  • Isermann R, Münchhof M (2011) Identification of dynamic systems. Springer, Berlin

    Google Scholar 

  • Isermann R, Töpfer S, Nelles O (1997) Identification with dynamic neural networks - architecture, comparisons, applications -. In: Proc. IFAC Symposium on System Identification, Fukuoka, Japan

    Google Scholar 

  • Kleppmann W (2003) Versuchsplanung, 3rd edn. Hanser Verlag, München

    Google Scholar 

  • Kötter H (2007) Kennfeldoptimierung. Rechnergestützte Kennfeldoptimierung. FVV Heft 831. FVV, Frankfurt

    Google Scholar 

  • Kötter H (2008) Innovative Motorvermessung. Schnelle Motorvermessung mit optimierten Testsignalen. FVV Heft 853. FVV, Frankfurt

    Google Scholar 

  • Kowalczyk M (2013) Online-Methoden – Optimierte schnelle Motorvermessung mit Online-Methoden zur Bestimmung von statischen und dynamischen Modellen. FVV Heft 987. FVV, Frankfurt

    Google Scholar 

  • Kowalczyk M (2014) Online-Methoden II – Erweiterung der Online-Vermessung um aktiv lernende Optimierungsmethoden zur Emissions- und Verbrauchsreduktion. Heft 1043-2014. FVV, Frankfurt

    Google Scholar 

  • Kowalczyk M, Isermann R (2014) Online and active learning measurement of engines. FVV conference. Report R566-2014. Madgeburg

    Google Scholar 

  • Kowalczyk M, Isermann R, Lindner S (2014) Online-Methoden II – Erweiterung der Online-Vermessung um aktiv lernende Optimierungsmethoden zur Emissionsund Verbrauchsreduktion (Extended online-identification with active learning methods). FVV Report 1043-2014. FVV, Frankfurt

    Google Scholar 

  • Kruse T, Kurz S, Lang T (2010) Modern statistical modeling and evolutionary optimization methods for the use in ECU calibration. In: IFAC Symposium Advances in Automotive Control, Munich, Germany

    Google Scholar 

  • Kruse T, Kreuzinger T, Ulmer H, Lang T (2012) Use of new data-based methods for the ecu calibration on a HiL system. In: 5th Conference on Simulation and Testing for Automotive Electronics, Berlin, Germany

    Google Scholar 

  • Leithgöb R, Bollig BM M, Henzinger F (2005) Methodology for efficient calibration of model-based ECU structures. In: 1st Internationales Symposium on Development Methodology, AVL, Wiesbaden

    Google Scholar 

  • Ljung L (1999) System identification – theory for the user, 2nd edn. Prentice Hall, Upper Saddle River

    Google Scholar 

  • Mallows C (1973) Some comments on cp. Technometrics 15(4):661–675

    Google Scholar 

  • Martini E, Voß H, Töpfer S, Isermann R (2003) Effiziente Motorapplikation mit lokal linearen neuronalen Netzen. Motortechnische Zeitschrift - MTZ 64:406–413

    Google Scholar 

  • McCulloch W, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5:115–133

    Google Scholar 

  • Mitchell J (2000) An algorithm for the construction of D-optimal experimental designs. Technometrics 42(2):48–54

    Google Scholar 

  • Montgomery D (2005) Design and analysis of experiments, 6th edn. JohnWiley and Sons, New York

    Google Scholar 

  • Müller N, Hafner M, Isermann R (2000) A neuro-fuzzy based method for the design of combustion engine dynanometer experiments. SAE 2000World Congress, Detroit, Michigan, USA, March 6-9, 2000 SP-1511, pp 331-337 (12)

    Google Scholar 

  • Murray-Smith R, Johansen T (1997) Multiple model approaches to modelling and control. Taylor and Francis, London

    Google Scholar 

  • Myers R (1990) Classical and modern regression with applications. PWS-Kent Publishing Company, Boston

    Google Scholar 

  • Nelles O (1997) LOLIMOT – Lokale, lineare Modelle zur Identifikation nichtlinearer, dynamischer Systeme. Automatisierungstechnik – at 45(4):163–174

    Google Scholar 

  • Nelles O (2001) Nonlinear system identification. Springer, Heidelberg

    Google Scholar 

  • Nelles O, Isermann R (1995) Identification of nonlinear dynamic systems – classical methods versus radial basis function networks. In: Proc. American Control Conference (ACC), Seattle, USA

    Google Scholar 

  • Nelles O, Hecker O, Isermann R (1991) Automatic model selection in local linear model trees (LOLIMOT) for nonlinear system identification of a transport delay process. In: Proc. 11th IFAC Symposium on System Identification (SYSID), Kitakyushu, Fukuoka, Japan

    Google Scholar 

  • Pfeufer T (1999) Modellgestützte Fehlererkennung und Diagnose am Beispiel eines Fahrzeugaktors. Fortschr.-Ber. VDI Reihe 8, 749. VDI Verlag, Düsseldorf

    Google Scholar 

  • Poland J (2002) Modellgest¨utzte und Evolutionäre Optinierungsverfahren für die Motorentwiclkung. Logos, Berlin

    Google Scholar 

  • Preuß HP, Tresp V (1994) Neuro-Fuzzy. Automatisierungstechnische Praxis – at 36(5):10–24

    Google Scholar 

  • Renninger P (2004) Schnelle Variationsraumbestimmung – Vorraussetzung für eine effektive Versuchsplanung. In: Motorenentwicklung auf dynamischen

    Google Scholar 

  • Prüfständen, Haus der Technik, Wiesbaden, Germany Rosenblatt F (1958) The perceptron: a probabilistic model for information storage and organization in the brain. Psychological Review 65:386–408

    Google Scholar 

  • Santner J, Williams B, Notz W (2003) The design and analysis of computer experiments. Springer, New York

    Google Scholar 

  • Schmitt M (1995) Untersuchungen zur Realisierung mehrdimensionaler lernfähiger Kennfelder in Großserien-Steuergeräten. Dissertation Technische Universit¨at Darmstadt. Fortschr.-Ber. VDI Reihe 12, 246. VDI Verlag, Düsseldorf

    Google Scholar 

  • Schreiber A (2010) Dynamische Motorvermessung mit verschiedenen Methoden und Modellen. In: Isermann R (ed) Elektronisches Management motorischer Fahrzeugantriebe, Vieweg+Teubner, Wiesbaden

    Google Scholar 

  • Schreiber A, Isermann R (2007) Identification methods for experimental nonlinear modeling of combustion engines. In: 5th IFAC Symposium on Advances in Automotive Control, Aptos, California, USA

    Google Scholar 

  • Schreiber A, Isermann R (2009) Integrated methodology for stationary and dynamic measurement and modeling of combustion engines. In: 3rd Internationales Symposium on Development Methodology, AVL, Wiesbaden

    Google Scholar 

  • Schreiber A, Isermann R, Vogels MS (2007) Dynamische Vermessung und Modellbildung hochdimensionaler Zusammenhänge in Verbrennungsmotoren mit dynamischen Motorenprüfständen. In: 2. Internationales Symposium für Entwicklungsmethodik, Wiesbaden, Germany

    Google Scholar 

  • Schüler M (2001) Stationäre Optimierung der Motorsteuerung von PKWDieselmotoren mit Abgasturbolader durch Einsatz schneller neuronaler Netze. Fortschr.-Ber. VDI Reihe 12, 461. VDI Verlag, Düsseldorf

    Google Scholar 

  • Schwarte A, Hack L, Isermann R, Nitzke HG, Jeschke J, Piewek J (2004) Automatisierte Applikation von Motorsteuergeräten mit kontinuierlicher Motorvermessung. In: AUTOREG 2004, VDI, Wiesloch, Germany, vol VDI-Berichte 1828

    Google Scholar 

  • Sequenz H (2013) Emission Modelling and Model-Based Optimisation of the Engine Control. Dissertation Technische Universität Darmstadt. Fortschr.-Ber. VDI Reihe 12, 1222. VDI Verlag, Düsseldorf

    Google Scholar 

  • Sequenz H, Schreiber A, Isermann R (2009) Identification of nonlinear static processes with local polynomial regression and subset selection. In: 15th Symposium on System Identification, SYSID 2009, Saint-Malo, France

    Google Scholar 

  • Töpfer S (2002) Hierarchische neuronale Modelle für die Identifikation nichtlinearer Systeme. Fortschr.-Ber. VDI Reihe 10, 705. VDI Verlag, Düsseldorf

    Google Scholar 

  • Töpfer S (2003) Statistische Versuchsplanung. In: Isermann R (ed) Modellgestützte Steuerung, Regelung und Diagnose von Verbrennungsmotoren, Springer, Berlin

    Google Scholar 

  • Töpfer S,Wolfram A, Isermann R (2002) Semi-physical modelling of nonlinear processes by means of local model approaches. In: Proc. 15th IFAC World Congress, Barcelona, Spain

    Google Scholar 

  • Verification and validation (2012) en.wikipedia.org/wiki; visited on May 21st 2012

    Google Scholar 

  • Ward M, Brace CJ, Vaughan ND, Ceen R, Hale T, Kennedy G (2002) Investigation of sweep mapping approach on engine testbed. In: SAE paper number 2002-01-0615, SAE, Detroit, Michigan

    Google Scholar 

  • Weber M, Kötter H, Schreiber A, Isermann R (2005) Model-based design of experiments for static and dynamic measurements of combustion engines. In: 3. Tagung Design of Experiments (DoE) in der Motorenentwicklung, Berlin, Germany

    Google Scholar 

  • Widrow B, Hoff M (1960) Adaptive switching circuits. IRE WESCON Conv Rec pp 96–104

    Google Scholar 

  • Zimmerschied R, Isermann R (2009) Nonlinear system identification of blockoriented systems using local affine models. In: 15th Symposium on System Identification, SYSID 2009, Saint-Melo, France

    Google Scholar 

  • Zimmerschied R,Weber M, Isermann R (2005) Statische und dynamische Motorvermessung zur Auslegung von Steuerkennfeldern - eine kurze Ãœbersicht. Automatisierungstechnik – at 53(2):87–94

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. TU Darmstadt, Darmstadt, Germany

    Rolf Isermann

Authors
  1. Rolf Isermann
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rolf Isermann .

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Isermann, R. (2014). Experimental Modeling of Engines. In: Engine Modeling and Control. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39934-3_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-39934-3_3

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39933-6

  • Online ISBN: 978-3-642-39934-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation