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Chang Y.-J.,Shandong University | Lu X.-Z.,Jinan Automobile Test Center | Wang S.-L.,Jinan Automobile Test Center | Tian H.-Y.,Shandong University | Wang Z.-M.,Shandong University
Neiranji Gongcheng/Chinese Internal Combustion Engine Engineering | Year: 2012

Prediction models of NOx emissions and in-cylinder peak pressure for HP common rail TCI diesel engine at speed of 1600 r/min were established based on partial least squares(PLS)regression, which are more clear in physical meaning to explain NOx emission process. Taking torque, common rail pressure, injection advance angle, injected fuel amount per cycle, fuel consumption rate and air excess factor and its square and interaction terms as prediction variables, root mean squares predication error(Rh) of calibration samples and root mean squares of validation samples(Rt) of NOx models can reach 14.0×10-6 and 15×10-6 respectively and those of peak pressure model can reach 0.144 MPa and 0.173 MPa respectively, meeting demands of engine NOx and peak pressure calibration and reducing the calibration work load significantly. The presented method of prediction variable selection and model optimization is practical and reliable, and it can be used to optimize the PLS model.

Chang Y.-J.,Shandong University | Lu X.-Z.,Jinan Automobile Test Center | Wang S.-L.,Jinan Automobile Test Center | Xie Z.-F.,Shandong University | Wang Z.-M.,Shandong University
Neiranji Xuebao/Transactions of CSICE (Chinese Society for Internal Combustion Engines) | Year: 2012

NO x prediction model of a EGR TCI diesel engine were established based on partial least squares regression(PLS). Using half of the samples as the calibration samples, the root mean squares error of calibration samples with cross validation method(R h) and test samples(R t) of NO x prediction error are 20.0×10 -6 and 18.7×10 -6 respectively with EGR, and 22.6×10 -6 and 26.5×10 -6 respectively without EGR. As the prediction accuracy of calibration samples and test samples is approximate, it was shown that the models are reliable and the PLS method can be applied to the NO x calibration to reduce calibration time. When the PLS model cannot reach the prediction accuracy, interactions and squares of prediction variables can be added as the prediction variables. The R h and R t values of PLS models with interactions and squares prediction variables are 22.1%-38.5% and 9.5%-23.0% lower than that using the models without interactions and squares variables. The results showed that the methods for prediction variables selection and model optimization proposed are practical and reliable, which can be applied to the optimization of PLS model.

Chang Y.,Shandong University | Lu X.,Jinan Automobile Test Center | Wang S.,Jinan Automobile Test Center | Wang Z.,Shandong University
Jixie Gongcheng Xuebao/Journal of Mechanical Engineering | Year: 2011

The main pollutants from automobile engines or off-road engines emission are NOx, THC, CO, PM and CO2 et al. The analyzers for measuring these exhaust pollutants need to be linearized for their non linearization characteristics. The linearization model based on polynomial stepwise regression linearization method is undesirable in respect of stability and prediction performance. The valid linearization forecast models of CO2 and CO analyzers, which are more clear in physical meaning, are presented on the basis of partial least squares regression, whose prediction accuracies are 29.1%~35.1% and 23.5%~39.3% higher than the model with least squares regression and the model with Chebyshev polynomial regression respectively. A general way to calculate the uncertainties of regression coefficients based on full cross validation and a principle of determining the model prediction accuracy with RMSE based on full cross validation and a principle of validating the significances of variables with the method whether the uncertain scope of regression coefficient is cross 0 are presented. These methods are simple, useful and effective, which can be applied to models not only with PLS method but with LS method and other regression methods as well. Engine exhaust emission analyzers can be linearized with this modeling progress to improve their measuring accuracies, especially of great advantage to a poor linearization and/or complex characteristic analyzer. © 2011 Journal of Mechanical Engineering.

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