Comparison of a turbocharger model based on isentropic efficiency maps with a parametric approach based on Euler's turbo-machinery equation (original) (raw)

Abstract

Faults in the intake and exhaust path of turbocharged common rail Diesel engines can lead to an increase of emissions and performance losses. Application of turbocharger models can help to detect and diagnose more faults as standard fault detection methods. The modeling of the turbocharger for onboard fault diagnosis can be obtained by different models. The differences between an approach based on the isentropic efficiencies and an approach based on Euler's turbo-machinery equation are investigated in this paper. The two models for a GT1749MV turbocharger are parameterized with data from the engine testbed. The comparison is applied by issues of measured model inputs, number of intern parameters, parameterization effort and model accuracy. Both models are compared regarding the application for onboard diagnosis.

Figures (10)

Fig. 1. Compression process a) and expansion process b) in a schematic h-s diagramm The compressor- and turbine massflow models are given according to Zahn (2007), Zahn and Isermann (2008) by

Fig. 1. Compression process a) and expansion process b) in a schematic h-s diagramm The compressor- and turbine massflow models are given according to Zahn (2007), Zahn and Isermann (2008) by

Fig. 2. Impeller velocity triangles according to Zahn and Isermann (2008)

Fig. 2. Impeller velocity triangles according to Zahn and Isermann (2008)

Table 1. Compressor model parameters as polynomial function of 4th order dependent on the guide vanes actuator position Sy¢¢:

Table 1. Compressor model parameters as polynomial function of 4th order dependent on the guide vanes actuator position Sy¢¢:

Fig. 3. Turbine velocity triangles according to Zahn and Isermann (2008)

Fig. 3. Turbine velocity triangles according to Zahn and Isermann (2008)

Table 3. LOLIMOT models of 7, and 1 iddynamic model contains only constant parameters which have to be estimated. Summing up, the 74 parameters of the thermodynamic approach and the circumstance of training the neuronal nets in every iteration step show a higher parameterizing effort then the 10 parameters of the fluiddynamic model. The thermodynamic model contains, in addition to con- stant parameters, two neuronal nets 17 (Ntc,corr; Me,corr) and 7: (Cu, Sugt) of type LOLIMOT, which have to be pa- rameterized in every iteration step. These models compose of several local linear models, which are interpolated by Gaussians, see Nelles (97). The amount of parameters 0 for a LOLIMOT net with v inputs and ¥ local linear models is defined by 6 = y(v+1)+2-y-v. The number of parameters of isentropic efficiencies in thermodynamic model is shown in table 3. Contrary to the thermodynamic model, the flu-

Table 3. LOLIMOT models of 7, and 1 iddynamic model contains only constant parameters which have to be estimated. Summing up, the 74 parameters of the thermodynamic approach and the circumstance of training the neuronal nets in every iteration step show a higher parameterizing effort then the 10 parameters of the fluiddynamic model. The thermodynamic model contains, in addition to con- stant parameters, two neuronal nets 17 (Ntc,corr; Me,corr) and 7: (Cu, Sugt) of type LOLIMOT, which have to be pa- rameterized in every iteration step. These models compose of several local linear models, which are interpolated by Gaussians, see Nelles (97). The amount of parameters 0 for a LOLIMOT net with v inputs and ¥ local linear models is defined by 6 = y(v+1)+2-y-v. The number of parameters of isentropic efficiencies in thermodynamic model is shown in table 3. Contrary to the thermodynamic model, the flu-

Fig. 4. Compressor efficiency map with corresponding engine test bench measurement points

Fig. 4. Compressor efficiency map with corresponding engine test bench measurement points

Fig. 6. Comparison of both modeling approaches concern- ing measured model inputs

Fig. 6. Comparison of both modeling approaches concern- ing measured model inputs

Fig. 7. Excitation signals for testbench measurement

Fig. 7. Excitation signals for testbench measurement

Fig. 8. Model comparison: turbocharger speed ntc

Fig. 8. Model comparison: turbocharger speed ntc

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