November 5, 2012

A Quick Running Model of a Turbocharged Diesel Engine

for engine calibration and development application on HiL

GT-SUITE NORTH AMERICAN CONFERENCE

P. Sashidharan, M. Farsodia, H. Nanjundaswamy, D. Tomazic, C. Hayes, FEV Inc.

C. Schernus, FEV GmbH

Photo Credit :

Wikimedia

Commons

Overview: Incentive for Quick Running Model

It is of interest to FEV to develop GT-POWER quick-running models of engines that

will undergo calibration development for various customers.

Traditionally, these engines are transient tested in dynamometer cells along with the

real-time hardware.

There is a cost incentive, for partially or fully replacing the physical engine with a real

time (plant) model for Hardware in the Loop (HiL) testing.

FEV expects a quick-running model to be sufficient for the present application. Hence

an application of GT-SUITE-RT on a HiL simulator was not necessary, yet.

The term “quick-running model” as used in this presentation refers to a GT-POWER

model that runs faster than a 1-d detailed model, by using methods such as lumped-

volumes or neural-network assisted mean-value objects.

The quick-running model connects to the Simulink model via the “Simulink Harness”

part, as shown in the schematic (next slide).

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Control Schematics: Micro Hardware in the Loop (HiL) Testing

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Virtual Test and Simulation Environment for Development Calibration & Optimization

Signal

Conditioner/Simulator

ECU Calibration

Interface ECU/ETK

GT

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EN

GIN

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Source: SAE Papers; 2011-01-0703 , 2012-01-0928

Approach and Premise: Using Test Data as a Model Short-Cut

The traditional approach to building a quick-running mean-value model is to start out

with a detailed 1-d model and generating several thousand points of predicted data by

varying input parameters. This large data-set is then used to train neural-networks.

This method works well. However for the predicted data to be accurate, requires the

detailed model to be correctly calibrated to test data. The time and effort required to

calibrate the detailed model to all test data points is considerable.

A different approach is used in this study with the following premise and incentives :

– Dynamometer data already available for some engines.

– Dynamometer data-set, if sufficiently comprehensive, may be used to train the

neural-network directly.

– Time savings in model build (by not having to build and calibrate a detailed model).

– Dataset size vs. neural-net RMS error vs. detailed model calibration.

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Detour: Why Use Neural Networks?

Any parameter-to-be-predicted that is a function of just 2-variables such as RPM and load can be

modeled as a simple 2-d look-up table.

If the parameter-to-be-predicted is a function of more than 3-variables, then a look up table

becomes problematic. Artificial neural networks are one way to simulate a hyper-dimensional

look-up table.

Artificial neural networks, inspired by biological neural networks, employ algebraic units called

neurons that accept any number of inputs. These inputs are multiplied by a weighing factor called

a weight.

Training the neural network involves determining the appropriate weights to correctly map the

input variables to output variable(s).

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Dynamometer Dataset

Test data was taken from a 4-cylinder turbocharged Diesel passenger van application. A

dynamometer dataset of about 180 data points were used for this study.

The following (6) data fields were selected as input parameters

Engine Int. Man. Int. Man. Int. Man. fuel injected Exh. Upstream

Speed Pressure Temp. EGR /cylinder/cycle turbine pressure

(RPM) (mbar) (°C) (%) (mg) (mbar)

The following (3) data fields were selected as output parameters for the mean-value cylinder

Vol. Eff. IMEP Exh. Upstream

Ref. Int. Man. 720° turbine Temp.

(fraction) (bar) (°C)

The following (2) data fields were also used

BMEP FMEP

(IMEP – BMEP)

(bar) (bar)

The FMEP in the dynamometer dataset equals the difference between the reported IMEP and

BMEP. Therefore, the IMEP in the dataset is assumed to be Net IMEP (720°) i.e. NMEP.

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Dynamometer Dataset: Input Parameters

The GT-POWER mean value cylinder replaces the empirical combustion cylinder with estimated

mean values for the (3) output parameters Volumetric Efficiency, IMEP and Exhaust Gas

Temperature.

The mean value cylinder imposes the above (3) estimated parameters on the rest of the model

and forces the turbocharger behaviour, etc. to be consistent with these imposed values.

The user does not have much choice in selecting the (3) imposed output parameters. However

the user must carefully consider and select the input parameters that will affect the (3) imposed

mean value parameters.

(5) Input parameters for Volumetric Efficiency

– Engine RPM wave dynamics

– Intake Manifold Pressure

– Intake Manifold Temperature

– Intake Manifold EGR displaces fresh air

– Exhaust Manifold Back Pressure in-cylinder end-of-exhaust-stroke pressure

The fuel mass injected is not expected to affect volumetric efficiency for a direct-injected diesel.

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charge air density

Dynamometer Dataset: Volumetric Efficiency

Volumetric Efficiency

– In this study is always stated with reference to intake manifold conditions.

– Can be reported on a fresh-air basis such that the volumetric efficiency that is imposed on the

cylinder will include only fresh air, OR on an all-gas basis such that the volumetric efficiency will

include fresh air and EGR gases.

– When volumetric efficiency from the dynamometer dataset is plotted against EGR level the data

points are within the regions marked below. This strong negative linear correlation to EGR

implies that the volumetric efficiency is reported on a fresh-air basis; and selected accordingly.

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Dynamometer Dataset: IMEP & Exhaust Temperature

(6) Input parameters for IMEP [Indicated Mean Effective Pressure] and Exhaust Temperature

– (5) factors affecting volumetric efficiency mass of charge air

– mass of fuel injected heat release & work done

The mass of charge air affects the in-cylinder pressure at the end of compression-stroke. The

mass of air also sets an upper limit to the injected fuel mass, within smoke-limited lambda.

In fact, assuming appropriate lambda, the mass of fuel should be the predominant parameter

controlling IMEP in a diesel engine. Plotting the IMEP (net) in the dynamometer dataset against

injected fuel mass shows a strong positive linear correlation, as shown below.

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Fuel injection timing (SOI) can also affect the

IMEP. However, for the purpose of this study, the

injection timing is not included as an input

parameter.

Variable Valve Timing can also affect output. This

engine does not use VVT.

Dynamometer Dataset: FMEP

The default FMEP empirical model provided in GT-POWER was used during current stages of

model development; by roughly correlating the predicted FMEP to dynamometer dataset.

The FMEP empirical model uses a peak cylinder pressure factor. Since the mean value cylinder

uses IMEP values that are much lower than peak cylinder pressure, the FMEP empirical model

will not be accurate. There are a couple of options:

– The FMEP dynamometer dataset can be used to train a neural network that will impose FMEP

on the model; similar to Vol. Eff., IMEP and exhaust temperature.

– The FMEP dynamometer dataset can be used to generate a 2-d table to look-up FMEP as a

function of RPM and load.

– The mean value cylinder contains an option for a mean value combustion object that can

generate pressure traces. The peak cylinder pressure thus estimated can be used for

modeling FMEP.

The above options will be considered during next stage of model refinement.

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Training the Neural Networks: Post-Processing Regression

The regression plots of the optimized neural networks, from this study, for (3) output parameters are shown:

Volumetric efficiency IMEP Exhaust Temperature

The above results are from the automatic training mode. GT-POWER sets aside 10% of the data to use for testing after training is complete.

The scatter of testing points about the zero-error line appears consistent with the scatter of training data.

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Schematics of Mean Value Model

12 Ambient

LUMPED

VOLUME

Intercooler/Ducts

MEAN VALUE

Intercooler Temperature

LUMPED

VOLUME

Intake

Manifold

Mean

Value

Cylinder

LUMPED

VOLUME

Exhaust

Manifold

Crank Train

MEAN VALUE

EGR Temperature

LUMPED

VOLUME

EGR Cooler EGR

Throttle

FUEL

EGR

sensor

COMP. TURBINE

VGT

(Boost)

Controller

RPM

P T

P

Fast Running Model Using Lumped Volumes

Run time can be significantly reduced by lumping together the volumes of all the ducts upstream

and downstream of engine and turbocharger. In this study, the flow circuit was lumped into 3

main volumes (flow-splits): intake manifold, exhaust manifold, and intercooler system.

One of the major computational bottlenecks in a detailed 1-d boosted-engine model is the

intercooler and EGR cooler objects. The detailed intercooler is usually modeled by a bundle of

identical pipes that simulate the intercooler restriction and large heat transfer surface; with flow-

splits at each end. This potentially creates small discretized volumes that restrict the time step.

In the lumped-volume concept the intercooler and EGR cooler Mean Value temperatures are

imposed with special “Heat Exchanger Connections” shown in the previous slide. This method of

imposing the temperature does not unnecessarily limit volume sizes.

For example, the 1000 RPM_4.8 bar IMEP point was run with detailed intercooler, EGR cooler

and pipe objects, with the following constraints in Speed Mode.

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Fast Running Model Using Lumped Volumes

At boost-pressure convergence, the run-time was 06 minutes 45

seconds; comprising 129 periods . As shown on the output screen,

the time-step was partly restricted by the CAC (charge air

intercooler) object.

In contrast, the same point run with the same constraints with

lumped-volumes reached boost-pressure convergence in 01 minute

23 seconds of run-time; comprising 174 periods.

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Steady-State Check of the Model

The fast-running model was run at the following speed-load points from the dynamometer dataset. The % error between predicted and measured values are listed below:

Only column # 3 has significant level of EGR (in this sample set). The rest are near-zero EGR.

The IMEP and Vol. Eff. neural-net output error % is minimal, with a slightly higher error on the exhaust temperature neural net. The neural-net accuracy appears acceptable, at least when given points from the dynamometer dataset.

At any other hyper-dimensional data-point, the neural-nets should predict intermediate values that are assumed to be of sufficient accuracy.

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Summary & Conclusions

A 180-points dynamometer test data was directly used to train neural networks to predict output

parameters: volumetric efficiency, IMEP and exhaust gas temperature. These outputs are

imposed on the rest of the turbocharger flow circuit.

This method is suggested as a labor-saving alternative to building and calibrating detailed 1-d

models for time-sensitive projects. At the same time, this method is expected to be less accurate

compared to generating thousands of synthetic data points. The study reported in the GT-

POWER tutorial implies a RMS error of roughly 3 times with ~200-points compared to >1000-

points. However, unless the detailed model is scrupulously calibrated to the full test data-set

before generating synthetic data, the real-world loss in accuracy is difficult to quantify.

A random sample of test data points were run on the steady-state model and successfully

confirmed that the model provides realistic results on trained output parameters.

If a valid input data point is not from the test data, the neural-nets interpolate “reasonable”

results. However, the accuracy of these interpolated results can only be gauged with a detailed

model simulation/calculation or with more testing.

The lumped-volume neural-net model runs significantly faster than a more detailed model.

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Next Steps

Update the rough FMEP model with either neural network OR 2-d look-up table.

Develop transient model for selected duty-cycle.

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