The Identification of Model Parameters for a Semi-empirical Model of Working Process in the Compression-ignition Engine

The paper concerns the identification process applied for a semi-empirical model of working process in the CI engine. The identification is based on pressure courses in the cylinder recorded during the experimental measurements on the test stand and is performed for estimation of values of the model parameters. Appropriate estimated values of the model parameters ensure minimization of the difference between measured and modelled pressure courses in the cylinder. The identification process can be divided into two stages. The first stage concerns identification of discrete values of the model parameters for a set of discrete engine operating conditions. The task of discrete identification is formulated as a dynamic optimisation task which is solved using a genetic algorithm. The accuracy of the identification process is evaluated by comparison of measured and calculated values of main parameters which characterize the working cycle such as: the mean indicated pressure, thermal efficiency, the mean indicated pressure in working part of the cycle, maximal pressure of the cycle, the mass of the medium in the cylinder and the crankshaft angle for which the maximal pressure occurred. The second stage concerns generalization of the results for any technically possible engine operating conditions which is solved by means of approximation. Feedforward multilayer artificial neural networks are used for the approximation. The accuracy of the identification and some examples of verification of the model predictions are presented as well.