A Predictive Model of P<sub>max</sub> and IMEP for Intra-Cycle Control

Zhijia Yang; Thomas Steffen; Richard Stobart; Edward Winward

doi:10.4271/2014-01-1344

In order to identify predictive models for a diesel engine combustion process, combustion cylinder pressure together with other fuel path variables such as rail pressure, injector current and sleeve pressure of 1000 continuous cycles were sampled and collected at high resolution.

Using these engine steady state test data, three types of modeling approach have been studied. The first is the Auto-Regressive-Moving-Average (ARMA) model which had limited prediction ability for both peak combustion pressure (P_max) and Indicated Mean Effective Pressure (IMEP). By applying correlation analysis, proper inputs were found for a linear predictive model of P_max and IMEP respectively. The prediction performance of this linear model is excellent with a 30% fit number for both P_max and IMEP. Further nonlinear modeling work shows that even a nonlinear Neural Network (NN) model does not have improved prediction performance compared to the linear predictive model.

These predictive models have great potential to be implemented online in an adaptive and recursive form for intra-cycle control applications.

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A Predictive Model of P _max and IMEP for Intra-Cycle Control 2014-01-1344

SAE MOBILUS

A Predictive Model of P max and IMEP for Intra-Cycle Control 2014-01-1344

SAE MOBILUS

A Predictive Model of P _max and IMEP for Intra-Cycle Control 2014-01-1344