The document describes a new method developed by Renault called ORME to optimize engine control parameters during the warm-up period of emissions testing. ORME uses a high dynamic test bed to collect engine response data across multiple tests with small variations in control parameters. It builds a linear model of the engine's transient responses during warm-up and then uses this model to compute the optimal control parameters at each second to minimize emissions while meeting constraints on NOx, PM, fuel consumption and noise. This approach reduces the number of required tests compared to traditional methods and provides a good first suggestion of optimal warm-up calibrations.

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MOTOR & UMWELT 2003
ENGINE & ENVIRONMENT 2003
COLD TRANSIENT EMISSIONS OPTIMISATION FOR
DI DIESEL ENGINE ON HIGH DYNAMIC TESTBED
Rémi Bastien
Didier Gilbert
Véran Van-Den-Berghe
Dominique Maignan
Renault S.A.

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SUMMARY OF
PRESENTATION
Generally, the engine emissions calibration process
is divided in two main stages : the first step consists
in optimising the engine control parameters at hot
conditions (90°C water temperature), and the
second step consists in setting the engine control
parameters for the transient warm-up period of the
NEDC cycle (the time the water temperature takes to
increase from 20°C to 90°C). A possible translation
of this second step into a mathematical framework
is the following constrained optimisation problem:
minimise HC/CO emissions with constrained
NOx/PM emissions (typically at the level obtained
after hot calibration), fuel consumption and noise.
Usual current warm-up calibration methodologies
rely on iterative changes on a chassis dyno. In order
to improve this process, Renault has developed a
new analytic method based on the use of a High
Dynamic Test Bed (test bed with driver, vehicle and
cycle simulation). Such a test bed was chosen for
its very small test-to-test variability (ie. a very high
consistency of repeated points which is essential
for an automated optimisation tool).
The principle of our approach is to characterise the
engine responses in transient conditions: a first
emission cycle is performed with some initial
(reference) engine control parameters coming from
the hot calibration, and then, several additional
emissions cycles are performed with modified
control parameters: the new parameters are some
offsets of the reference parameters. Only one
parameter is modified at a time, so that it leads to
one emissions cycle per control parameter under
consideration (plus the reference cycle). With this
data base, the tool is able to build a basic linear
model of the dynamic engine responses (every
second) during all the warm-up period. This model
is then used for computing every second the
optimal control parameters with respect to the
optimisation target (emissions trade-off). Finally,
the result must be fitted into the engine control
strategy maps. Finally, the calibrations are
validated with the vehicle on a chassis dyno. This
approach turns out to produce a good first
suggestion of optimal warm-up calibrations
This method allows to reduce dramatically (by 2 at
least) the number of tests and therefore to save
time in the optimal and robust calibration process
of control parameters. The approach requires a high
level of knowledge (calibration engineering and
computing).
INTRODUCTION
Mastering the engine emissions in order to comply
with the legal standard is very time-consuming in
the engine development process. The complexity of
engine responses and the number of controllable
parameters in engines have been increasing
dramatically in recent years. Reducing the
emissions usually involves many trade-offs so that
other engine performances may be affected (like for
example fuel consumption). Hence, it becomes very
difficult to optimise all the control parameters
without efficient analytic methods. Today, it is
commonly agreed that Design Of Experiments
(DOE) based methods provide a very efficient and
almost unavoidable way to optimise the engine hot
calibrations. These DOE based methods have
become very widely used for optimising hot
calibrations but they do not allow so far to optimise
efficiently the warm-up calibrations. Nonetheless, a
significant part of the emissions are produced on
the ECE, especially for HC and CO. This warm-up
period is part of the drive cycle and often critical for
reaching the emissions legal targets. In order to
obtain optimal engine calibrations, it is necessary
to adjust the engine control parameters also in the
cold phase. Different ways can be investigated
regarding this topic. Two approaches are usually
considered. The first approach consists in using a
similar technique to that used for optimising the
hot calibrations. The DOE model procedure based
on steady-state mapping can be carried over to the
engine at a number of different coolant
temperatures. The coolant is force-cooled and the
temperature held constant throughout each test. A
disadvantage of such an approach is that a constant
coolant temperature may not be representative of
the actual engine conditions encountered during a
natural warm-up. The second approach is to model
the engine emissions with a DOE method based on
drive cycle measurements. The goal is to build a
reliable model of the engine transient responses.
This model then allows an optimisation of the
engine control parameters during the whole
emissions cycle. While this second approach is
under investigation at Renault, such a model is not
yet available. In order to fill the gap between the
two approaches, Renault has developed a third way.
It combines an iterative approach based on drive
cycle measurements with an analytic optimisation
procedure. It provides an efficient methodology for
optimising warm-up transient emissions of DI
diesel engines on high dynamic test beds.

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PROBLEM STATEMENT
Reducing the emissions is one of the main task of
the engine calibration process. The objective of
calibration is to provide optimum ECU maps during
the full emission cycle (warm-up period and hot
conditions). The final calibration must not only
guarantee that the emissions comply with the legal
levels; the driveability and noise levels must also be
optimised. While the legislation has become more
and more stringent, the number of controllable
parameters for DI engines has been increasing: the
number of parameters to be optimised is now up to
8 to 10. In recent years, many automotive
companies have turned to DOE based methods for
engine calibration. DOE based approaches are
commonly applied to determine the hot ECU maps
from engine steady-state tests. Some statistical
models are usually built at a number of key
operating points (speed and load operating
conditions). These points are chosen to be
representative of the emissions cycle. The models
are then combined with optimisation procedures for
finding optimal control parameters. The advantage
of the above approach is that a large number of
ECU parameters can be optimised with a
reasonable number of tests. However, a
disadvantage is that the rough approximation of the
emissions cycle by a sequence of steady states may
not be representative enough of the real vehicle
emissions. In order to improve this method, a global
model, including engine load and speed as
parameters, can be built. This allows a further
reduction of the required number of test points and
a better approximation of the cycle emissions.
However, the typical limits of a steady-state
approach remain: it can only be safely used for the
optimisation of hot calibrations.
The legislation drive cycle starts with a cold engine
and after the first couple of seconds, the engine
enters the warm-up phase. During this period, the
engine hot calibrations determined by the above
DOE based approach are unsuited. Most engine
management systems correct these ECU maps
according to the values of the coolant temperature,
and the engine speed and load. These correction
maps must be optimised taking into account
several targets on emissions, noise level, fuel
consumption and driveability. In fact, these targets
(either objectives or constraints) are divided in two
types: cumulative quantities (emissions, fuel
consumption) and instant quantities (noise level,
driveability). In a mathematical framework, the
warm-up transient optimisation consists in finding
optimum control parameters to fulfil above
cumulative and instantaneous objectives and
constraints. In addition, the predictions must be as
close as possible to the real vehicle responses.
Since, as explained above, a DOE based steady
state mapping procedure cannot be safely used and
a transient model of the emissions is not yet
available, Renault has developed a specific warm-
up calibration tool called ORME.
Fig. 1: Global view of ORME approach

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TRANSIENT
OPTIMISATION METHOD
In order to address the previous warm-up
calibration challenge, Renault has developed a new
method based on high dynamic test beds. The
corresponding in-house tool is called ORME. This
method helps completing a first step towards
optimising warm-up emissions (part of the engine
management calibration). In the calibration
process, the optimisation of the transient
calibrations is carried out after the DOE based
optimisation of the hot calibrations. The hot
calibrations are usually used as a starting point for
determining the best corrections to be applied
during the engine warm-up.
ORME tool is based on a combination of an iterative
approach and a multi-objective optimisation. The
iterative approach was chosen for its simplicity. A
global view of the approach is proposed on figure 1.
A first emission cycle is performed with some initial
(reference) engine control parameters coming from
the hot calibration, and then, several additional
emissions cycles are performed with modified
control parameters: the new parameters are some
offsets of the reference parameters. Only one
parameter is modified at a time, so that it leads to
one emissions cycle per control parameter under
consideration. For example, for 5 engine control
Fig. 2: Test-to-test variation of the high dynamic test bed (consistency of repeated points)
parameters, 6 emission tests are required (1
reference + 5 perturbed).
Then, the gradients of the different responses can
be computed: for each sampling time, the change
in the engine responses is compared with the
change in the parameters to obtain gradient
information. This linear model is used by an
optimisation solver to determine the optimum
engine control parameters in order to comply with
the selected targets (objectives and constraints).
ORME handles both cumulative (mass of fuel and
pollutants) and instant (combustion noise level,
driveability, smoke) quantities
In order to use such a tool, a low test-to-test
variability is essential (for computing gradient
information every second). This is why Renault has
based its method on a high dynamic test bed. The
tests can be repeated without driver variability. The
test-to-test variability has been assessed and
results are presented in figure 2. Each plot is
associated to one response: the x-axis represents
the value of this response and the y-axis the
corresponding relative variability (in %) of repeated
points. For 95 % of the repeated points, the
variation of the response is lower than 15% for all
main pollutants (HC, CO, NOx, CO2) and lower than
30% for particulate measurements based on an
opacimeter signal). This last point should be
improved.

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TOOL ARCHITECTURE
Renault ORME tool has a specific architecture with
two types of data inputs. The first type includes the
vehicle or test bed measurement data, while the
second type includes some expert knowledge data.
It may involve different accuracy levels: either
rough tendencies or complete and accurate
simulations of the engine. The two types of data
(measurements and expert knowledge) are mixed by
the optimisation agent. This tool architecture is
illustrated in figure 3. In this figure, one can notice
that measurement results can be used as a
reference test for a new optimisation loop. Such
iterations may be necessary to converge to an
optimum (at most 1 or 2 loops may be required).
The expert knowledge helps the convergence of the
tool, sometimes avoiding local optima (figure 4).
Fig. 3: Tool architecture
Fig. 4: Gradient computation

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A LOCAL LINEAR
APPROXIMATION
The algorithm is based on a linear model of the
engine responses. In fact, for each pollutant, ORME
computes a gradient, linear relationship of the
engine responses to the parameters. These
gradients are computed for each sample time
(1Hz). Since the gradient is issued from the
difference of engine responses between two
emission cycles divided by the difference of engine
control parameters for the same emission cycles, a
very small test-to-test variability is necessary. This
is why this method is first used on a high dynamic
test bed. Moreover, specific accuracy checks are
carried out: if the change in engine responses is
lower than the variability level, the gradient for this
sample time is set to zero; the same is done if the
change in the control parameters is too small. The
gradients are then averaged for each operating
point (engine speed & load and water temperature).
All these algorithm specificities make ORME very
robust. Figure 5 illustrates the validity domain of
the gradient information. Most of engine responses
have a quadratic behaviour, but locally they can be
approximated by a linear model. The algorithm
seeks a solution in the interpolation area (between
the two values of each control parameter) but also
in an extrapolation area (the limits of which are set
to an extra half of the interpolation area width).
Fig. 5: Validity domain of the gradient

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EXAMPLE:
DIESEL APPLICATION
To illustrate the proposed procedure, we consider
the case of a light duty common rail diesel engine.
A reference hot calibration was available (steady
state DOE approach with 15 key points). Assessed
on the vehicle, this calibration produced too high
values of HC and CO emissions, especially during
the warm-up. To solve this problem, it was decided
to use ORME with the engine on a high dynamic
test bed. A first optimisation loop was performed for
the first 600 seconds of the emissions cycle (warm-
up). The optimisation of 5 control parameters was
done (Main injection timing, Rail pressure, EGR
rate, Pilot injection timing and pilot injection
quantity). This first loop therefore required six
emissions cycle. The following target was tried:
reduce HC and CO emissions with the same level of
fuel consumption, noise, NOx and particulate
emission as after hot calibration. Figure 6 shows
the results. Bottom charts detail the gains in
emissions. With ORME methodology, a gain of
around 20% in HC and CO with iso-level of the
other engine responses was attained. These results
are an average of three realisations of the emission
cycles: bottom left chart shows these three
emission results compared to the reference values.
In one optimisation loop (ten emission cycles
including the validation cycles), the target was
reached. This reduction represents a division by two
at least of the usual required number of tests.
Fig. 6: Results for a Diesel application
Emission result
0
0.2
0.4
0.6
0.8
1
1.2
1.4
HC CO NOx 10*PM
Emission(g/km)
Reference test
ORME test 1
ORME test 2
ORME test 3
Gain in percent between
reference cycles on ORME optimisation
20.6
18
3.3
-5.3
0.8
-10
-5
0
5
10
15
20
25
HC CO NOx PM CO2
Gain(%)

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EXAMPLE:
GASOLINE APPLICATION
ORME is a generic tool and it can also be used for
gasoline application. In the considered example,
ORME was applied to the first 200 seconds of the
emissions cycle. The engine control parameter
modifications concerned spark advance, idle engine
speed and richness distribution. The challenge was
to attain the emissions targets with a lower number
of tests as well as reducing the fuel consumption.
The values given in figure 7 are the results of two
loops of ORME, the first loop is only made by a
simulation with an emission model. The result of
this first loop becomes the reference for the second
loop based on real tests. The results are compared
with the calibration obtained by the classical
method carried out on the vehicle. The results
obtained with only seven emission tests would have
CONCLUSION
ORME completes the warm-up calibration process
by means of an efficient analytic method. ORME do
not replace DOE based methods for optimising hot
calibrations. Based on a local linear model, it gives
very accurate results. This tool is a first step
towards dynamic optimisation (thermal and load
transient).
Of course, this method needs some improvements:
noise measurements in transient, continuous
particulate measurements, thermal representativity
of the high dynamic test bed. All these items are
under investigation for different optimisation
methods. The use of ORME methodology already
reduces by two at least the number of tests required
to reach the emissions targets.
Fig. 7: Results for a gasoline application
Fig. 8: Catyst temperature
required twenty tests with a classical approach.
Figure 8 shows the catalyst temperature
optimisation obtained with ORME compared to that
obtained with a classical method.