Models for the Prediction of Performance and Emissions in a Spark Ignition Engine - A Sequentially Structured Approach

Paper #:
  • 980779

Published:
  • 1998-02-23
Citation:
Arsie, I., Pianese, C., and Rizzo, G., "Models for the Prediction of Performance and Emissions in a Spark Ignition Engine - A Sequentially Structured Approach," SAE Technical Paper 980779, 1998, https://doi.org/10.4271/980779.
Pages:
17
Abstract:
A thermodynamic model for the simulation of performance and emissions in a spark ignition engine is presented. The model is part of an integrated system of models with a hierarchical structure developed for the study and the optimal design of engine control strategies. In order to reduce the uncertainty due to the mutual interference during the validation phase, the model has been developed accordingly with a hierarchical and sequential structure.The main thermodynamic model is based on the classical two zone approach. A multi-zone model is then derived form the two zone calculation, for a proper evaluation of temperature gradients in the burned gas region. The emissions of HC, CO and NOx are then predicted by three sub-models.In order to make the precision of emission models suitable for engine control design, an identification technique based on decomposition approach has been developed, for the definition of optimal model structure with a minimum number of parameters.The results of the thermodynamic cycle model validation, performed over more than 300 engine operating conditions, show a satisfactory level of agreement between measured and predicted data cycles. Afterward, the two step identification procedure has been applied for the emission models parameters identification. From this analysis, it has been found that the model precision achieved can be comparable with that obtained via conventional mapping procedures using black-box models, but with a drastic reduction of the experimental effort. Moreover, the proposed approach allows substantial computational time saving with respect to conventional identification techniques.
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