A comparison of dynamic models of various complexity for diesel engines

Y. H. Zweiri, J. F. Whidborne, L. D. Seneviratne, K. Althoefer

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Diesel engines are widely used in many application domains. The recent drive to reduce emissions, improve efficiency and implement on board diagnostic tools has resulted in a real need for improved models for the simulation of Diesel engines. This paper evaluates three different Diesel engine dynamic models of varying levels of complexity. The assumptions for the models and the equations are presented. The equations are implemented in a computer simulation environment. The models describe the dynamic relationship between indicated pressure and engine speed. The first model is a detailed analytical non-linear dynamic model, including dynamometer dynamics, instantaneous friction components, viscosity variations with temperature and inertia variations with piston pin offset. In-cycle calculations are performed at each crank-angle. The second model is a nonlinear dynamic model which includes a mean friction model for the engine components and dynamometer dynamics, but does not include the piston pin offset as well as the inertia variations. The third model is obtained by an identification procedure to find a low-order linear transfer function between the engine input and output. The three models are used to predict the behaviour of a Diesel engine. The models are compared using experimentally measured engine speeds, under steady state and transient operating conditions. The paper discusses the suitability of the models for various control applications such as engine simulation, fuelling control system design and fault diagnostics.

Original languageBritish English
Pages (from-to)273-289
Number of pages17
JournalMathematical and Computer Modelling of Dynamical Systems
Volume8
Issue number3
DOIs
StatePublished - Sep 2002

Keywords

  • Diesel engine modelling
  • Dynamic modelling
  • Engine diagnostics
  • Fuelling control
  • Identification
  • Simulation

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