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A Hardware-in-the-Loop (HIL) Bench Test of a
GT-Power Fast Running Model for Rapid Control
Prototyping (RCP) Verification
2016-01-0549
Published 04/05/2016
Hai Wu and Meng-Feng Li
General Motors Co.
CITATION: Wu, H. and Li, M., "A Hardware-in-the-Loop (HIL) Bench Test of a GT-Power Fast Running Model for Rapid Control
Prototyping (RCP) Verification," SAE Technical Paper 2016-01-0549, 2016, doi:10.4271/2016-01-0549.
Copyright © 2016 SAE International
Abstract
A GT-Power Fast Run Model simplified from detail model for HIL is
verified with a bench test using the dSPACE Simulator. Firstly, the
conversion process from a detailed model to FRM model is briefly
described. Then, the spark timing, fuel pulse with control for FAR,
and torque level control are developed for proof of concept.
Moreover a series of FRM/Simulink co-simulation and HIL tests are
conducted. In the summary, the test results are presented and
compared with GT detailed model simulations. The test results show
that the FRM/dSPACE HIL stays consistent in most variables of
interest under 0.7-0.9 real-time factor condition between 1000 - 5000
RPM. The same steady-state can be reached by RCP controllers or
with GT-Power internal controllers. The transient states are close
using different control algorithm. The main purpose of HIL
application is achieved, despite inconsistencies in performance data
like fuel consumption. These inconsistencies are the result of
simplifications and discretization, and are not the focus of the HIL
application. The real-time capability of FRM/dSPACE along with
inheritance, reusability and conversion features make this process and
platform feasible from test-cell RCP development to production and
ECU verification.
Introduction
The automotive industry is one of the most competitive businesses in
the world. The complexity of automobiles increase as market
demands higher drivability and fuel economy, taking into account
stricter EPA regulations on emissions. This competition drives the
shortening of the development cycle from design to production. The
HIL is evolving from a method into a parallel technology, along with
the engine and ECU technology. It is forming its own software and
hardware market. Due to the fact that the HIL tool has become an
indispensable solution for Rapid Control Prototyping (RCP) controls,
Engine Control Unit (ECU) software development, and verification
cycles at research and production engineering phases.
HIL technology can be used in every aspect of automotive
development and verification, including combustion, engine control
and management, transmission, and body control. A HIL simulation
environment is used to validate a control-oriented model for the
SIHCCI hybrid combustion mode [1, 2]. The developed model is
capable of simulating the entire engine operating range, consisting of
SI and HCCI combustion. Another application uses double Wiebe
functions on a crank-angle based model on HIL to simulate the heat
release rate, temperature, and pressure reached during the
combustion. The HIL application showed good agreement with 57
engine tests in terms of ignition delay and cylinder pressure profiles
[3]. On engine simulation aspects, a comprehensive HIL test system
for engine controllers was presented in 2007[4]. Later, it developed
into an off-the-shelf product called Automotive Simulation Models
(ASM). This crank-angle based combustion simulation with physical
simulation on throttle, injector, and sensors, such as knock sensors;
cam phase sensors; crank sensors; ionization sensors; lambda probe,
oil and air temperature sensors; actuators including Electronic throttle
control (ETC); ignition systems; injectors for multiple injection;
current-controlled actuators, etc. The limitation on the method is that
the simulation is based on Ordinary Deferential Equation (ODE). In
the vehicle simulation area, a HIL simulation was used to simulate a
vehicle model through a highly responsive dynamometer to couple
with a real diesel engine. Such applications enable the verification of
performance and fuel economy prediction of different conventional
and hybrid powertrains [6]. In vehicle body electronics, a door
system electronic control model is implemented in HIL, converting
the model in real-time and interfacing it with actual vehicle hardware.
The system helped developers achieve aggressive new product
development timeline [7]. It is concluded from many applications that
HIL technology is a systematic process which improve the work
performance - it allows work to be done earlier, at a better level of
understanding, and reduces costs by decreasing dependence on
combustion testing, engine test cells, calibration runs, dynamometer
tests, vehicle tests and test driving.
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Physical models in PDEs, legacy mean value models, or control
oriented ODE models are mostly used for HIL implementation.
Among these, the one-dimensional PDE models give the most
detailed description but have the longest run times. On the other
hand, ODE models only provide input and output dynamic
description in few orders, and is implemented easily in real-time.
There have been many efforts to simplify the 1D model into fast-run
models for real-time HIL, while still retaining the accuracy of
detailed models. The Design of Experiments (DOE) and hybrid
Radial Basis Functions (RBF) can be used to approximate the
simulation results of detailed models for cylinder quantities: e.g., the
engine volumetric efficiency, indicated efficiency, and energy fraction
of the exhaust gas [8]. A statistic regression method is also applied to
1D simulation results, when the calibrated detailed model is available
[8]. Multiple nonlinear multi-state dynamic models are reported to
cover the entire range of speed and load with accuracy of (±2.0%)
[10]. An early version of GT-Power mean value model for HIL for
commercial Vehicle Applications was developed [11], the needed
fidelity and transient dynamic can be compensated by Neural
Networks (NN), which need a large amount of test data in the training
process. But the NN continuity and stability need to be verified
before applied into application.
There are series of products for the HIL plant model, such as ASM,
Boost-RT, and GT-FRM. When choosing from commercial offtheshelf tools, predictive capability, fidelity, complicity and
expandability are among the evaluating criteria for these models and
tools. In addition, ease of developing, calibration time, and better
reusability are also favorite factors that control engineers consider.
The process of inheriting from the upstream stage and integrating to
the downstream stage of whole production development is one of the
most important linkages between research, development, and
production departments.
The GT-Power model is utilized for engine development, performance
simulation, and product calibration based on its high fidelity, predictive
capability over different engine steady and transient conditions.
Recently, the GT-Power Fast Run Model (FRM) became available to
reuse the detailed 1D-enigne model for HIL implementation.
The feasibility of the GT-Power FRM is tested on dSPACE
Simulator™ hardware with DS1006 control boards, signal capture
functions, and a million volt output module. The simplification
approach from a detailed model to FRM, and real-time capabilities
are verified under this hardware environment. The interface design,
open-loop setting and closed loop testing are described in this paper.
The closed-loop tests, including fuel injection metering, throttle
control, and A/F ratio control are conducted to demonstrate the
capability of the FRM running on this HIL environment.
The Advantages of GT-Power Model for HIL
GT-Power is a comprehensive PDE based 1D engine modeling and
simulation tool at engine development stage. It is capable performing
predictive cylinder combustion simulations, including turbulence and
knocking predictions; thermodynamic simulations of cylinders and
valves between combustion fluid, engine body and coolant, and
environment; the fluid dynamic simulations from intake to exhaust
covering the engine geometry, predicting pressure drop and dynamic
over after-treatment components, mechanical parts and load
simulation with friction and lubrication factors; sonic and subsonic
simulation over valves, turbocharger or supercharger. It provides
physical torque level, torque oscillations and combustion
composition, so that it can be extended and connected to electrical
motor, transmission, and after-treatment system at vehicle level.
Another important factor that makes the GT-Power model a solid
starting point for HIL modeling beside the comprehensive capability,
is the model availability and reusability from the upstream
development stages. It could lead to a seamless transition, connection
and communication between engine development and control
implementation engineers.
A GT-Power engine model combines combustion, fluid dynamic,
thermals and mechanical subsystems. It may contain hundreds of
sub-volumes. This makes it possible to tailor the detailed model into
different directions for different areas of electrical implementation
and HIL application needs at varying levels and real-time strictness.
It is not economic and practical to have a super-computer to provide a
comprehensive and detailed real-time simulation for every HIL
applications. The GT model has many advantages over other
commercial off-the-shelf models, such as ODE models, mean-value
models, etc. on the fidelity, variable availability, tailoring flexibility
and extendibility. The user interface provides a detailed map with
zoom functionality, allowing users to view all parts of interest and
enabling feasibility for various HIL applications.
Co-simulation can be done seamlessly between the GT-Power and
Simulink, in which Inputs/outputs and controls are simulated modeling
feedback between the engine and ECU/RCP. The spark, injection,
EGR, and turbocharger control functions can be simulated, verified
step by step and incorporated using this HIL (Engine)/Simulink (ECU)/
RCP platform. This platform is important to reduce developing cost
and shorten the product development to production cycle.
Conversion from Detailed Model into FRM
The GT-Power engine model offers detailed information over
hundreds of sub-volumes in engine performance simulation, some of
which are not as critical for the HIL simulation. The simulation time
can take up to 20 - 30 times more than real time. In order to utilize
the GT-power model for real-time simulation, it is necessary to
reduce the complexity and increase the discretization size. Certain
detail wave dynamics might not be reflected in the results. This
sacrifice is inevitable using current hardware technologies, and the
final realistic verifications must be conducted in a test cell, in vehicles
and on the proving grounds.
The standard detailed GT-Power model is reduced to a simplified
model, called Fast Running Model (FRM). The conversion process
consists of steps of simplifications and verifications. To validate the
feasibility of this schematics, a baseline SI engine model from
GTSUITE software package examples is adopted and applied into the
process of conversion, implementation and hardware tests. The model
structure in GT-Power is shown in Figure 1, it is a 4-cylinder gasoline
direct injection engine equipped with turbocharger and intercooler.
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multiplier in the model can be tuned to match the temperature. The
orifice diameter can be used to match pressure loss after combining
air-path volume.
Discretization and Step Size
Figure 1. A 4-cylinder turbocharged gasoline direct injection engine with
turbocharger wastegate controller and semi-predictive intercooler in GTPower.
The control component in the model is the turbocharger wastegate
controller, WG_Controller as shown in Figure 1, and the BMEP is
sensed for control objectives at 18.75 bar when engine is running at
5000 RPM at WOT full load conditions. Fuel injection is restricted in
the SeqInjConn. The injector delivery rate is defined at 17 g/s, and
the fuel ratio is specified by FAR at 0.095 at 5000 RPM. The 5000
rpm case was investigated in this case study. It is the most
challenging for computation of time and resolution over the crank
angle; lower engine speed cases should perform more efficiently in
real-time simulations.
In Computational Fluid Dynamics (CFD), discretization is introduced
to solve governing partial differential equations by algebraic
expressions. The choice of proper discretization size (e.g. 200 mm for
intake and 300 mm for exhaust) in GT-FRM is key to balance
accuracy and computation time. Removing the default time step
restriction of 1 crank angle degree (CA) allows the model to run in
real time. Setting the maximum time step to 720 CA allows the tool
to take as large time-steps as possible without violating the CourantFriedrichs-Lewy (CFL) condition.
Key Variables and Real-time Factor
Assign Results (RLTs) of interest, such as BMEP, BSFC, Volumetric
efficiency, etc. as the accuracy check reference for each step during
the conversion. An example of key variables and accuracy checks are
listed in table 1. The real-time factor is the most important
consideration, and should have certain margin to guarantee that the
FRM achieves the HIL runtime target.
Table 1. An example of the key variable and RLTs need to check during the
conversion
Preparation
Before the beginning of the conversion process, some factors should
be considered, including the case setup, combustion model selection,
cylinder wall temperature model, controllers, and parameters. It is
necessary to cover the full engine operating range of simulation,
sweeping engine speed and load. Results should be validated with
available test data. The partial load test data may not be available
from engine performance or engine development, but it would be
good to have them for verification purposes. The combustion one can
be predictive for FRM, but it is limited to non-predictive for real-time
HIL application so far. The master-slave mode may be used for the
cylinder model to improve the runtime by ignoring the cylinder
difference. The controllers need to be disabled during the process, the
actuators can be set to a fix position for highest speed and maximum
fluid rate.
FRM Converter Tool
GT-ISE provides a useful model management tool with a
simplification and verification function. It is called the FRM
Converter Tool. It clearly organizes the tree for each simplification
step so that a new branch can be started at any time for introducing
improvement, as indicated in figure 2.
Volume Combination and Verification
It is important to identify the component that restricts time step the
most. Generally, models are simplified backwards, from the exhaust
tailpipe backward to the intake branch. GT-Power provides a useful
tool, Combine Flow Volume Wizard, to combine volume geometric
parameters into a single volume with consistent inherited properties
from a dedicated part of the subsystem. For example, two intake
valves parts can be converted into a single intake valve part with the
same effective flow area, heat transfer rate, and pressure drop. The air
path volumes are combined and the valve area multiplier is adjusted
to compensate for the two-valve system. In the process of conversion,
the volume size is conserved, but differences in surface area
introduce a temperature mismatch. At each step, verification is
necessary to match the original simulated results. The heat transfer
Figure 2. Conversion steps and results are saved in tree structure in a project.
The simplified FRM of the 4-cylinder gasoline model is shown in
Figure 3 below. Compared to the original detailed model, the FRM
has less parts modeling the intake and exhaust, the valve ports are
combined into one. As compared after 7 steps in Figure 4. Volumetric
efficiency, BMEP, and exhaust manifold pressure are consistently
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preserved from 1000 rpm to 5000 rpm along the simplification, and
the manifold temperatures among steps have 10 - 15 K difference.
The factor of real-time is substantially improved from about 30 to 0.8
times of real-time from original model to FRM.
Spark Timing
A predictive combustion model that is able to use spark timing as a
input is not implemented in this GT-Power model, but it is necessary
in RCP validation. In this test, the Wiebe function anchor is defined at
0.2% of burned fuel, with duration starting at 0.1% of burned fuel
until 99.99% total fuel burned. This method is used to simulate the
spark mechanism, even though the spark angle is different from the
initial heat release angle. The test data is necessary to model the
relationship between the sparking and Wiebe anchor angles by
utilizing either the lookup table or Neural Network.
Fuel-to-Air Ratio Control
Figure 3. The FRM model of the 4-cylinder turbocharged gasoline direct
injection engine
In the baseline model, fueling is calculated in ‘InjAFSqeConn’ based
on air charging, which is available in the GT-power simulation. In
HIL/RCP test, it needs to be replaced with a feedback controller. In
order to implement fuel metering control, the part is replaced with
‘InjPulseConn’ and the injection pulse width is controlled by a PI
controller to adjust the fuel pulse width to target FAR of 0.095. The
exhaust gas lambda is sensed as feedback from the exhaust manifold
split pipe, as shown in Figure 6.
Torque Control by BMEP and MAP
Torque-level control can be implemented by controlling BMEP or
manifold pressure. After the implementation of AFR control, BMEP
or intake manifold pressure can be controlled by adjusting the
wastegate position. The BMEP and manifold pressure signals need to
be filtered before they are used in control feedback. Both pressure
controls have a challenge due to the turbine dynamics and the time
delay characteristics. A PID controller is designed in co-simulation to
replace the Turbocharger Wastegate Controller in the original
GTPower model, as shown in Figure 6.
Figure 4. Comparison of key variables and results in GT-Post.
Co-Simulation between FRM and Simulink
The FRM can be utilized stand alone or FRM/Simulink co-simulation
for control design and tuning purposes. The FRM retains accuracy
and saves about a factor of 20 in runtime. GT has an interface called
Simulink Harness to exchange data with Simulink. The controllers in
GT-FRM are transferred to Simulink step by step. The co-simulation
is the first step for the FRM/RCP HIL test. The GT-Power/Simulink
simulation can still be used for verification purpose.
Figure 6. The Simulink part of co-simulation with fueling controller and PID
turbocharger controller.
Hardware-in-the-Loop (HIL) Simulation
Figure 5. Final simplified FRM model layout with GT fueling and
turbocharger controllers.
An interface between the HIL and RCP is necessary to accommodate
different engine setups: it includes HIL outputs, such as the digital
signals of encoder for engine test-cell, crank angle, and the analog
signals of manifold flow rates and pressures, the air-to-fuel ratio at
exhaust runner, the brake torque, and the milliamp signal for
Thermocouple simulation, etc. Meanwhile, the interface needs to
capture control input signals from the RCP, such as the spark angle
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and timing, single or multiple fuel injection angles and duration for
combustion control, the PWM signal for throttles, EGR ratings, and
turbine wastegate control. The Simulink design for IO interface and
dSPACE implementation is indicated in Figure 7.
Afterward, the closed-loop test is conducted to verify the real-time
capability of HIL and the control of RCP. AFR torque level controls
are developed for test bench purposes.
Another test adds the throttle angle step change from 30 degree to 90
degree beside the BMEP request, in which it simulates following
scenario: after the throttle reaches WOT, the wastegate is activated
for further power requests at 11s. The comparison is carried out after
matching the two throttle step requests and setting the same BMEP
request pulse between 14 and 18 bars. The top two plots shown in
Figure 9 demonstrate that the BMEP target is achieved by both of the
FRM/HIL and GT-Power/PC machines, with minor transient
differences. The controlling wastegates move simultaneously and
converge closely during steady state and transient states. The torque
values also show good agreement. All the computation in FRM/HIL
was completed within 90% of sampling time, shown in left plot of
bottom row.
Figure 7. The interface design for HIL/RCP and the controllers are moved to
RCP.
Results and Verification
To validate the real-time capacity and consistency in results of FRM
on HIL with GT-Power on PC, a series of tests are conducted. Results
of the BMEP manipulation test are shown in Figure 8. As indicated
on the right of first row, the BMEP is controlled to follow a step
change from 14 to 18 bar (dark dash-dot line) under WOT conditions.
Both the PID controller in RCP and the Turbocharger controller in the
original GT-Power model reach the same steady-state without
noticeable errors in BMEP, waste-gate position, intake manifold
pressure, MAF, and torque. The FRM running on HIL can simulate
the pumping pulse dynamic in flow, the dash curve from GT-Power is
an averaged value and centers the flow wave dynamic. Discrepancies
in the PID controller are observed during the transient process
because of the differences in algorithm. The bottom left figure
indicates that the computation turn-around time is less than sampling
time with about a 0.05ms margin.
Figure 9. The test results of step response to BMEP and throttle in HIL/RCP
comparing to simulation results with GT-Power detail model.
The fueling pulse width controls are also examined in figure 10. A
discrepancy exists in fueling steady states, even though the transient
dynamics are close. The difference in fuel injection arises from model
simplifications. To output the same amount of power and pressure,
they are at different fuel consumptions.
Figure 10. The test results of fuel rating in HIL/RCP comparing to simulation
results with GT-Power detail model.
In figure 11, spark timings are investigated for detailed and simplified
FRM models to demonstrate the proposed sparking strategic
approach. The spark timing from the -10 degree to 20 degree position
is simulated on an offline PC and tested online on a HIL machine.
From the results, firing at and after TDC are close in trend and shape.
Differences in accumulation and peak value exist, which might be the
results of sampling limitation caused by the hardware computation
power. The pressure resolution in crank angle is sacrificed in
sampling time.
Figure 8. The test results of step response to BMEP in HIL/RCP comparing to
simulation results with GT-Power detail model.
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In figure 12, the pressure trace of spark timing at CA 20 degree on
HIL (with big square marks) is compared to FRM-Simulink
Cosimulation results at different sampling rate of
(with marks in different shapes) and GT-Power detailed model (in red
solid line) simulation data. The simulation at higher sampling rates at
provide better output resolutions from RT solver of
FRM model. It is shown importantly that the HIL test date (Square
marks) can reproduce the FRM simulation (Triangle marks) at same
sampling rate, e.g. 1ms. So the solver provides enough capability to
in real-time simulation. Currently the sampling rate of hardware
limits the output resolution in this HIL bench test.
Consistency in the heat release trace, pressure trace, heat transfer etc.
in HIL with GT-Power plays an important role in real-time
simulation. Sampling rate is another key factor after the model
strategy is chosen for a certain hardware platform. The tradeoff
between model complexity and real-time capacity can only be
decided by control applications engineering practical criterion.
Conclusions
In this paper, a bench test is conducted to implement the GT-Power
FRM based engine plant model on dSPACE Simulator. After
reviewing previous work on HIL implementations, the advantages of
this method over the others are discussed for automotive applications.
A brief introduction of the conversion process from GT-Power
detailed engine model to FRM conversion is described, using a
4-cylinder SI engine model. Along the process of simplification of the
engine intake and exhaust system, key parameters and tools are
discussed. The important variables for the conversion checks are
chosen. The values are displayed for the step. The main features of
the FRM Converter Tool and the key criteria are discussed. During
the conversion, real-time factors are validated for HIL
implementation purposes. After replacing the injection and torque
level controller, a series of bench tests of the FRM running on
dSPACE DS 1006 based Simulator has been conducted to verify the
real-time capability of this software and hardware structure.
At the GT-Power and Simulink co-simulation stage, the methods used
to implement spark timing and fuel injection strategies are discussed.
The typical functions of ECU, such as spark timing, injection control,
throttle control and AFR control are designed for test and verification
purposes. The case study shows that the detailed GT-Power model
can be simplified into an FRM that runs in real-time on HIL platform.
The test results are compared to GT-Power detailed model simulation
results. From the comparison, the controlled real-time FRM model
shows good agreement for both steady and transient states compared
to the GT-Power detailed model. The FRM inherits the characteristics
of the detailed models, the features like the converter tool, and
real-time capabilities on HIL show great potential for the automotive
design and development applications.
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Figure 11. The test results of spark timing on cylinder pressure trace in HIL/
RCP comparing to simulation results with GT-Power detail model.
Figure 12. Comparison among the cylinder pressure traces from HIL (1ms)
test, FRM model (1ms, 1/3, ½, 2/3ms) simulation results and GT-Power detail
model simulation result of spark timing at 20 CA ATDC.
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Contact Information
Hai Wu, test system engineering of Research and Development,
General motors, at Warren Technical Center
hai.wu@gm.com
Acknowledgments
Abbreviations
AFR - Air-fuel ratio.
bmep - Brake mean effective pressure
bsfc - Brake specific fuel consumption
CA - Crank angle
CFD - Computational fluid dynamics
CFL - Courant-Friedrichs-Lewy
DOE - Design of experiment
ETC - Electronic throttle Control
ECU - Engine Control Unit
FAR - Fuel-air ratio
FRM - Fast Running Model
HIL - Hardware-in-the-loop.
NI - National Instrument
NN - Neural Network
ode - Ordinary differential equation
pde - Partial differential equation
RBF - Radial basis functions
RCP - Rapid Control Prototype
RLT - Result
WOT - Wide open throttle
The authors would like to express their appreciations to the supports
from Miles Melka and Kevin Roggendorf of Gamma Technology and
suggestive discussions with Orgun A Guralp, Jonathan Dawson, and
Xiaofeng Yang of GM R&D.
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