A Predictive Model of Pmax and IMEP for Intra-Cycle Control Loughborough Univ

Format:

Conference/Event

Author/Creator:

Yang, Yang, author.

Contributor:

Steffen, Thomas

Stobart, Richard

Winward, Edward

Conference Name:

SAE 2014 World Congress & Exhibition (2014-04-08 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2014

Summary:

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 (Pmax) and Indicated Mean Effective Pressure (IMEP). By applying correlation analysis, proper inputs were found for a linear predictive model of Pmax and IMEP respectively. The prediction performance of this linear model is excellent with a 30% fit number for both Pmax 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

Notes:

Vendor supplied data

Publisher Number:

2014-01-1344

Access Restriction:

Restricted for use by site license