Concurrent WAVE/Matlab Simulink Simulation Applied to HSDI Diesel ECU Calibration
Richard Osborne
Ford Motor Company (Diesel Engineering)
Dunton Engineering Centre
Basildon
UK
Abstract
This paper describes an application of an engine cycle simulation code (WAVE) in co-simulation with a control and
dynamics modelling package (Matlab Simulink) to provide total vehicle system modelling capability. The models
presented form the basis of an Analytical Calibration CAE toolset allowing the definition of many electronic control
unit (ECU) calibration parameters in the CAE environment. Results are presented for a medium capacity,
automotive high speed direct injection (HSDI) turbo-charged diesel, fitted with a variable geometry turbine (VGT).
These show a high predictive accuracy for engine mass air flow (MAF) and intake manifold pressure (MAP) during
transient conditions. The aim of the technique is to reduce the amount of dynamometer-based and vehicle calibration
and this is demonstrated by analytically calibrating the ECU to improve vehicle performance.
Introduction
Total vehicle system modelling including engine, vehicle and ECU components has been available as a design and
development tool to Ford Diesel Engineers for some time. Applications have included exhaust gas re-circulation
(EGR) control strategy and hardware definition [1], VGT/EGR controller design [2,3] and vehicle driveability (i.e.
performance feel and drive line oscillation). These models have traditionally been implemented in a control system
modelling environment such as Matlab Simulink. The numerical solvers in these tools are not optimized for engine
cycle simulation and therefore the representation of the engine models usually contain minimal physical content and
are heavily reliant on empirical data in either interpolation tables or regression format. A typical model may include
a ’filling and emptying’ or mass and energy conservation approach to capture the dynamic behaviour of the intake and
exhaust systems with cycle-averaged cylinder mass fluxes defined by experimentally obtained volumetric efficiency
data [1]. These models are often referred to as ’mean value’ types as they resolve cycle averaged rather than crank
angle based quantities. Mean value models have the advantage of close to ’real time’ execution speeds but the
disadvantage of requiring re-mapping of engine parameters whenever significant hardware changes are made during
the engine development process. The empirical content is usually based on steady-state engine data which may be
erroneous in the heavily transient situations which these models are required to simulate. This is particularly
apparent for turbo-charged engines where engine inlet and exhaust conditions differ widely from transient to steady
state conditions due to turbo-lag.
Engine simulation packages such as WAVE are widely used in industry to aid engine design and development,
having the advantage of being able to capture engine flow through intake and exhaust systems, with user input of the
system geometry and flow data for valves and ports. Primarily this is due to the 1-D compressible flow formulation
being able to simulate the gas-dynamic or wave action present in the system. State and velocity throughout the
system are also predicted with crank angle based resolution rather than mean cycle values. Because of the reduced
empiricism required to characterize the mass flow through the engine system, cycle simulation models such as
WAVE have higher ability to capture transient engine behaviour than their mean-value counterparts.
The following is an outline description of a total vehicle system model using WAVE as the engine model. This is
followed by comparison of simulation output for MAF and MAP against data obtained from in-vehicle sensor
outputs. Comparative results for transient torque behaviour are presented as a ’before and after’ using the model as a
controller design and re-calibration tool.
Analytical Calibration Model
The ability to couple control and vehicle simulation with engine cycle simulation has been enabled by the
Matlab/WAVE link. The link follows standard Matlab S-function protocol and usage is detailed in the WAVE user
manual.
Figure 1. shows the top-level view of the model as implemented in Simulink with three major systems i.e. vehicle
drive-line, engine model (WAVE) and controller (ECU). The WAVE model is signified by the ’W’ icon, entitled
HSDI Diesel. The Simulink environment not only allows logical, hierarchical structuring of the model but also
offers a user-friendly graphical user interface (GUI) in which the various parts of the system can be viewed. The
simulation is executed in the normal manner by choosing ’start’ from the pull-down menu system. During simulation
data is exchanged at appropriate execution time steps to and from WAVE.
Data exchange is categorized into two main groups i.e. ’sensors’ where data is transferred from WAVE to Simulink
and ’actuators’ where data is transferred from Simulink to WAVE. The ECU outputs in figure 1. are shown connected
to the Unit Conversion and Actuator Dynamics block and allow control of the EGR valve, injected fuel quantity,
VGT and start of injection. The unit Conversion and Sensor Dynamics block shows sensor output to Simulink and
includes mass air flow, intake manifold absolute pressure and engine speed. In addition an ’actuator’ is provided to
allow instantaneous engine torque as calculated in WAVE to be fed to the vehicle/drive line model. The engine
speed output of the drive line model is connected as a ’sensor’ to define engine speed in the WAVE model.
Figure 2. Shows the WAVEBUILD representation of the engine model used in this study. The basic configuration
of the engine is a 4 cylinder, 4 valve per cylinder HSDI unit. The full vehicle intake system is modelled, including
air cleaner, inter-cooler, compressor, intake manifold and associated ducting. The exhaust system includes the
manifold and variable geometry turbine. EGR is modelled by interconnection of intake and exhaust systems. Inlet
and exhaust valve effective flow areas and maps specifying turbine and compressor data were prepared as an input to
WAVE as described in the user manuals. The DI diesel Wiebe combustion block was specified and calibrated to
provide the correct engine brake specific fuel consumption (BSFC) and pre-turbine temperatures against steady-state
dyno data.
The ECU model is a Simulink representation of the appropriate control strategy and includes fuel quantity, start of
injection, EGR and VGT features. The proprietary nature of the ECU control strategy precludes further description
in this paper.
The vehicle/drive line model shown in figure 1. uses a lumped parameter approach in which vehicle sprung and
unsprung masses are defined separately. Drive line stiffness and losses are characterized as well as tyre slip. This
type of model has a proven accuracy within Ford as a vehicle performance indicator and is able to predict
driveability attributes such as drive line tip-in/tip-out oscillation.
Results
Due to time constraints not allowing an appropriate calibration of the vehicle model, the following results do not
include simulations with the vehicle model enabled. In all cases measured engine speed was defined as an input to
the simulation. Two vehicle drive test cases were chosen to provide validation data for the model.
Test 1
0% pedal position deceleration to 1250 rpm engine speed followed by 100% pedal position tip-in from 1250rpm
engine speed accelerating to 4000 rpm engine speed in second gear (clutch engaged throughout).
Test 2
0% pedal position deceleration to 1250 rpm engine speed followed by 100% pedal position tip-in from 1250 rpm
engine speed to 4200 rpm engine speed, followed by 0% pedal position tip-out and deceleration in third gear (clutch
engaged throughout).
Test 1 results are presented in figure 3a and 3b. The lower graph in figure 3a plots engine speed and measured and
predicted controller fuel demand against time. The upper graph shows measured and predicted MAP and MAF and
predicted VGT actuator rod position. MAP and MAF measured values are taken directly from signal-conditioned
output of the in-vehicle sensors which were already present as part of the engine ECU implementation. Figure 3b
includes plots of measured and predicted ECU signals from a PID (proportional + integral + derivative) controller
within the ECU strategy. Test 2 results are given in figures 4a and 4b and follow the same format as the Test 1
results.
Figure 5 shows the ’before and after’ result of re-calibrating the ECU in the CAE model on predicted engine brake
torque output during an in-gear acceleration.
Discussion
The validation results in figures 3a and 4a show excellent overall agreement between predicted and measured
quantities. The discrepancies for MAF and MAP in the period 0 to 0.5 seconds are a result of model ’settling time’
from initial conditions. Predictive errors in fuel demand during the tip-in period can be explained by incomplete
implementation of the fuel quantity strategy in the ECU model. At this stage of model development, small errors in
MAF and MAP prediction have to be interpreted with care. This is because no special attempt has been made to
model the hysterisis associated with in-vehicle MAP and MAF sensors. Future work is planned to correct this
omission.
The ECU signal results (figures 3b and 4b) also prove the model has a high degree of predictive accuracy. As for the
MAF and MAP results model ’settling time’ is evident during the period 0 to 0.7 seconds. Although some steadystate error is noticeable in the proportional and integral terms of the controller, the dynamic response is accurately
captured. This is demonstrated by the high accuracy of prediction for the derivative terms.
The noticeable degradation in predictive accuracy from Test 1 (2nd gear) to Test 2 (3rd gear) is primarily due to the
non-linearity of the actuator used to drive VGT vane position (The dynamic response of the actuator was calibrated
against Test1 and left unchanged for Test 2). Future models will incorporate a higher level of physical interpretation
of the VGT actuator.
A major advantage of CAE based ECU calibration compared to dynamometer/vehicle methods is the ability to
analyze many of the inter-dependent engine system parameters that are difficult to measure. This is particularly true
for temperatures and instantaneous gas composition (e.g. EGR percentage) under transient conditions. The ECU recalibration carried out as part of this study was a result of using the model to understand the differences between
transient and steady-state operating conditions. The ECU re-calibration produced up to a 32 % improvement in
predicted torque output with a considerable increase in torque rise rate from 1600 rpm to 2300 rpm (see figure 5).
The irregularity in engine torque output between 1250 rpm and 1750 rpm is a result of drive line oscillation. The
ECU re-calibration has since been implemented in-vehicle and has delivered a maximum of a 34% improvement in
single gear acceleration tests.
A disadvantage of the use of WAVE as an engine model compared to mean value models is the computational runtime overhead. The simulations used to provide the validation results in this paper executed at approximately a
factor of 200 over ’real time’ on an SGI Indigo2 workstation. Although in some applications it may be possible to
trade-off run-time against predictive accuracy the current performance is considered inadequate for tasks such as
certification drive cycle calibration work. Future studies will involve reducing the complexity of the WAVE engine
model until an acceptable balance between accuracy and execution time is realized. WAVE will also be used to
provide high accuracy data for input to mean value models.
Although considerable CAE effort was invested in model validation, the ECU re-calibration task including CAE
analysis and vehicle tests were carried out within 15 working days. This represents a considerable time and cost
saving over test-based calibration methods.
Conclusions
WAVE and Matlab Simulink in concurrent simulation has provided a powerful analytical calibration tool for Ford
Diesel Engineering.
Initial validation has shown a high degree of predictive accuracy for MAF and MAP under transient conditions
without resorting to the normal level of empiricism associated with total vehicle system models.
The analytical ECU calibration process has successfully been applied and has delivered real-world performance
benefits in vehicle tests.
References
1. Lancefield T., Cooper L. and French B., ’Designing the Control and Simulation of EGR’, Automotive Engineer,
February/March 1996.
2. van Nieuwstadt M., Moraal, P., Kolmanovsky, I., Stefanopoulou, A., Criddle, M. and Wood, P.,’ A Comparison
of SISO and MIMO Designs for EGR-VGT Control of a High-Speed Diesel Engine.’, IFAC Workshop on
Adavances in Automotive Control, Mohican State Park, Ohio, U.S.A., February 1998.
3. Kolmanovsky, I., Moraal, P., van Nieuwstadt M. and Stefanopoulou, A., ’Issues in Modeling and Control of
Variable Turbocharged Diesel Engines.’, 18th IFIP Conference, 1997.
Figure 1. Top Level View of SIMULINK Model
Figure 2. WAVEBUILD Representation of Engine Model.