Advances in Automotive Control 1995. A Postprint Volume from the IFAC Workshop, Ascona, Switzerland, 13–17 March 1995

229 Pages·1995·24.582 MB·English

by  L. Guzzella, U. Kiencke

ADVANCES IN AUTOMOTIVE
CONTROL
A Postprint volume from the IF AC Workshop,
Ascona, Switzerland, 13 -17 March 1995
Edited by
U. KEENCKE
University of Karlsruhe, Karlsruhe, Germany
and
L. GUZZELLA
ΕΤΗ, Zurich, Switzerland
Published for the
INTERNATIONAL FEDERATION OF AUTOMATIC CONTROL
by
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I F AC W O R K S H OP ON A D V A N C ES IN A U T O M O T I VE
C O N T R OL
Sponsored by
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- Technical Committee on Automotive Control
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Organized by
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IFAC Technical Committee on Automotive Control
International Programme Committee (IPC)
U. Kiencke (D) (Chairman)
J. Ackermann (D)
H.P. Geering (CH)
L. Guzzella (CH)
J.K. Hedrick (USA)
L. Nielsen (S)
National Organizing Committee (NOC)
L. Guzzella (Chairman)
C. Wittwer
ChWittmer
Copyright © IFAC Advances in Automotive Control,
Ascona, Switzerland, 1995
ANALYSIS & VALIDATION OF MEAN VALUE MODELS FOR SI
IC-ENGINES
C. SIVIERO\ R. SCATTOLINl\ A. GELMETTI*
L. POGGIO1, G. SERRA*
*Dipartimento Informatica e Sistemistica, Université di Pavia, 27100 Pavia, Italy. E-mail:
siviero@ipvaimed3. unipv. it
fMagneti Marelli, Divisione Controllo Motore, Sviluppo Sistema, 40134 Bologna, Italy. E-mail:
LPoggio@bologna. maraut. it
Abstract A Mean Value Model is developed for engine control design. The model structure is de-
scribed by physical considerations and the model parameters are estimated from steady-state data
obtained in cell tests on a FIAT engine. The proposed model is suitable for optimisation of engine
control calibration.
Key Words. Modelling, Parameter estimation, Automobiles, Engine control.
1. INTRODUCTION CCo throttle plate when tightly closed against the
throttle bore [°]; 2
Mean Value Models (MVM) describe the dynamics throttle open area [m ];
Ath
of the main engine variables, measurable and un- θ spark advance angle [°];
measurable, without considering cycle variations.
brake thermal efficiency;
Ob
These models are derived from a physical analysis of
n* volumetric efficiency (based on intake
the main phenomena occurring inside the engine,
conditions);
while the model parameters are obtained by fitting.
n crank shaft speed [rpm],
It is generally recognised that MVM have good pre-
I tota3l moment2 of in2ertia loading engine
dictive properties without being too complex. For
[10(27i/60) Kgm];
this reason they are of particular interest in engine
control (see e.g. Dobner, 1980; Aquino, 1981; Hen- Tb brake torque [Nm];
Pb brake power [KW],
dricks and Sorenson, 1990).
H fuel heating value [KJ/K3g],
The aim of this paper is to present some preliminary u
v engine displacement [m ]; 3
results on the analysis and validation of a MVM d
V manifold-port passage volume [m ];
applied to a FIAT engine. The model is constituted
r compression ratio;
by two differential and a set of algebraic equations.
R gas constant [J/Kg°K];
These equations can be derived by physical consid-
k ratio of specific heats (Cp/Cvi 1.4 for the air);
erations or by means of a "black-box approach" and
Cf flow coefficient of throttle body throat;
contain a number of unknown parameters to be de-
D throttle bore diameter [m];
termined from experimental data.
d throttle shaft diameter [m];
M inlet air molecular weight [g/mole];
a
2. SYMBOLS
M working fluid molecular weight [g/mole] ;
HC hydrocarbon emissions [g/KJ];
m air mass flow rate past the throttle plate
at NO nitric oxide emissions [g/KJ];
x
[Kg/sec]; CO carbon monoxide emissions [g/KJ].
m injected fuel mass flow [Kg/sec];
f
λ relative air/fuel ratio; 3. EXPERIMENTAL SET-UP
ρ man intake manifold air pressure [Pa] ;
Pamb ambient pressure [Pa], To validate the engine model a set of about 450
Pexh exhaust gas pressure [Pa]; steady-state measurements has been taken on a 1.6
Pth pressure at the throat [Pa] ; litres, 16 valves FIAT 4 cylinder engine equipped
Tm an intake manifold air temperature [°K]; with a Magneti Marelli multi-point injection system.
Tomb ambient temperature [°K], The engine is mounted on an AVL PUMA4
/ISAC200 automatic dynamic test bench. This con-
a throttle plate angle [°] ;
1
trol system is equipped with an optical pick-up, on a statistical analysis of data. Finally, the pollut-
giving a signal of 600 pulse/revolution from which ants are assumed to depend, through some algebraic
the engine speed is derived, and controls the engine equations, on η, θ, Λ T. In these cases too a "black-
b
torque with a frequency of 14Hz. Throttle valve box" modelling approach is adopted, since a signifi-
position and engine speed are controlled in closed- cant physical description of the pollutants formation
loop by a transputer which allows the reproduction is complex and not necessary for the goals of the
of standard or arbitrary engine or vehicle test cycles, MVM here considered.
with an excellent control of engine speed and torque.
To modify the engine control variables (e.g. θ and λ) 4.2. Model parameter estimation, complexity selec-
the Engine Control Unit (ECU) has been coupled tion and validation
with an AVL-MCS3 (an EPROM emulator) system
for remote control of all the ECU variables. The For simplicity the dependency of η» EC, CO, NO
AVL-MCS3 is connected with the PUMA4; hence on the primary variables n, θ, λ, T, p is assumed
b mam
this system gives the possibility of acquiring simul- to be static. Furthermore physical considerations can
taneously engine parameters and ECU variables too. be of great help in providing guidelines for the
Exhaust gases are measured by a five gases (C02, structural selection of these dependencies. However,
CO, NOx, HC, 02) analyser with an AVL730 fuel the form of the functions to be estimated and their
consumption equipment. The measurements have parameters have to be identified from data. To this
been conducted with λ control active (λ=1). The test regard, increasing the model complexity, that is the
bench keeps the engine operating point at fixed number of parameters to be estimated, in general
values of speed and torque during variation of spark leads to a more and more precise fitting of data. On
advance, ranging from a minimum value of about 0 the other hand, a natural requirement is to determine
degree to a value of incipient knocking. models with a limited number of parameters. Among
the criteria for the model complexity selection pre-
4. THE MEAN VALUE MODEL sented in the literature, the Final Prediction Error
(FPE) Criterion has been used in this work (see e.g.
4.1. Model description Soderstrom and Stoica, 1989). Then denoting by Ν
the total number of data, b2y q the number of parame-
Since in the performed cell tests the FIAT engine ters to be estimated, by μ the loss function, the FPE
operated under λ control, the MVM here considered criterion consists of determining the model structure
is constituted only by the two submodels describing which minimises the following performance index:
the air mass flow in the intake manifold and the
crank shaft speed dynamics, while the fuel film dy-
N-q
namics can be neglected.
Moreover, a prediction error whiteness test
Furthermore, some algebraic equations describe all
(Soderstrom and Stoica, 1989) is performed to assess
the phenomena that exhaust in few motor cycles.
the prediction capability of the identified models.
Since the engine temperature is assumed to be at the
For any given model structure, the parameter esti-
steady-state, the model is not suitable to describe the
mation procedure has been performed by minimising
warm-up phase.
the mean square prediction error with different
The tw o di fferential eVqunation s are: m 1 techniques, namely the Maximum Likelihood
ή - * « , RTman ^ /n method and a Gauss Newton technique. The values
Pman ~ ~ Vν Ρ man + at γ V )
of the parameters have been determined together
with their relative standard deviation (r.s.d.). It has
also been decided to reject models with estimated
The model is completed by a number of algebraic parameters characterised by a r.s.d. beyond 10%.
equations describing ma t as function of pth i Tomb and
a. Moreover, η and 77* depend on primary vari- 4.3. Air mass flow rate through the throttle plate
ν
ables like η, Θ, p „. These dependencies have to be
ma
determined either by means of physical considera- Air flow in a pipe. The air mass flow through the
tions or starting from a "black-box" approach based throttle plate can be viewed as a particular case of
the isentropic flow of a compressible gas in a pipe.
Under these assumptions, and with reference to the
symbols of Fig. 1 , the air flow m through sections
Ai
Α ι and A with pressure pi and p respectively, is
2 2
A2 ) given by:
• _ P\ A I Ριλ
r
ρι.Τι
Fig. 1. Isentropic flow of a compressible gas in a pipe:
for^> = -±- (3.a)
symbols. rcR
2
for^-^r (3.b) Q
OT
where \
) < 3 x
" » - l H * - " *) ^ "
u u
and C, nominally equal to 1, is to be determined 3 F i e m o vdd al lf vi e
f
experimentally in order to tarke into account struc- ê ™* P™^
tural defects or neglected physical factors (e.g. fric- , , . -,. - . ,.~ , ~,
χ « ° . ' . u u modified as shown m Fig. 3. This modified profile
tions). Furthermore, tcr IS the pression ratio which „ 1_ . ν u
nconrrrroecsnpnonnHdcs *toΛ tλhλe c,Ιri^tοiΐc al flow. a,ll ows on..e. . to *ge t so,m. e i,mp*ort an,t .benefits, such as
τ« , . the possibility to achievAe better dnv eabihty charac- u
In pηaοr^tiΛc,u,lΐaοr, , assumi·n g t ha+ t A. ·i s -thΛe throttlΛe plate - ^ , , . ~
*\ ' , .. . '2. , . Λ , tenstics through a much more precise control of the
open area^lrt and section 1 mΛ Fig. 1 is the ambient, ·Γ A
so that i4^4i=0, expression (3) coincides with that ™f * rf o ,ls e s a , A
t f
given in Heywood (1988) and Hendricks (1990), ***** « ^ *
\nthp2=pth Pi=Pamt» Tj^Tamb. almost linearly with a. In turn, thigh control of the
air flow is a fundamental step in the development of
Conventional throttle plates. In traditional throttle efficient idle speed control techniques.
valves, an ellipsoidal throttle plate with throttle I ,
angle a is used, see Fig. 2. \
^ j/^^ \ \ «ι ;
"\ Fig. 4. Throttie opra area calc^
I The computation of the area A is made difficult in
th
view of the dissymmetry of the profile. In this work
Fig. 2. A traditional throttle valve. three different views corresponding to the three cases
. j/r^ 4 ν reported in Fig. 4 have been taken. In section I, A is
TLettmga=d^D,^4rtisgivenby: + * ^ * a . *u th
Λ
computed as a function of a according to the view
r 0Α\ 0 r5 2FL j/ normal to the throttle plate. In section Π, A is com-
Δ th
2 - i- +:?. (CQS _αα2 ^2 JA/4 pUedt according to the view normal to the straight
Ώπ { cos a ) π[ cos a line between the edge of the plate (point M) and the
0
, χ 22 y 1 τ modified profile (point P). Finally, in section III, the
+ —siiTM a — \-a\l-a) - sin' a view is parallel to the straight line tangent to the
cos a V cos a J J throttle shaft and passing through the edge of the
0
_i ! 1 5
for a<a<>a where a = cos {acosa ) (4.a) | ! i \ j \ ' >—
0 0
2/2 l
-4Α^Λ- = 1, 2 Γα \1Λ-α )γ /2 2+s.i n_!a 1 «1 t4rf 1s «- >; i i Ι / !
for α < α <-2 (4.b) I s_ ; ; ! Y..:, ]
* ! ι ι çy
Expression (4) coincides with Eq. (7.18) in Heywood ] ^ ^ -^
(1988), save for the sign of two terms. ο ~~ίό ώ ώ—«δ—as—eb—λ—as—ά
T>irottt* ang^c ptate Π
Throttle valves with modified profile. In the la st . . ^. . ^ , , . r
r A A
generation of^ th« rottl.e b. od«ie.s , th.eβ i n.te rnal p.rofil1e .is Λ Fig. 5. Throttle plate open area versus throttle angle plate,
3
plate (point M). unknown parameters of (5) have been estimated
With reference to the dimensions of the throttle body together with their r.s.d., leading to: = 0.133
ηνηο
considered in this paper, the procedure above leads (r.s.d. = 29.9 %), = 0.391e-3 (r.s.d. = 8.2 %),
ηνηΐ
to compute the value of Ah reported in Fig. 5 as = -0.0636e-6 (r.s.d. = 9.3 %) and = 0.202e-
t Tivn2 ηνρΐ
function of a. Note the substantial variation in the 5 (r.s.d. = 9.8 %). Note that, although the model has
shape ofAt h which occurs at about 24°. good prediction capabilities, the estimated parame-
ters have unacceptable r.s.d..
Expérimentai results. Eq. (4) has been used together
A second approach is to determine η by means of
ν
with the computed throttle plate open area of Fig. 5,
physical considerations. To this end, Heywood
to fit the data experimentally observed in the test cell
(1988) presents the following relation:
for the FIAT engine. By considering in Eq. (4) C/ =
0.83, the results reported in Fig. 6 have been ob-
tained. These results clearly illustrate that the rh=——è—\- M^M*-1)T
adopted model provides excellent prediction capa-
bilities for values of a<50° (the mean prediction Me\ y+u [i rx-\ k(r-l){ Pm m Ί
error is about the 4.3%). On the contrary, for o>50° (6)
It is worth noting that, for a wide range of λ
a significant departure of the predicted values of the
M 1
air flow from the observed data is found. This can be
explained as follows: in Eq. (3) p should coincide
2
with pt,h while in the performed experiments pman Then Eq. (6) reduces to
has been used. Since /w, >p , an error is introduced
th
which can be only partially corrected by the selection r-l k(r-\){ )
Pman
of Cy. In fact, the shape of P(pman) is found to be which is the form given in Taylor (1977), Boam et
constant up to critical flow; then in this region,
al, Matthews et al. (1991). According to (7), the
which corresponds to small values of a, the use of
physical model we have adopted takes the form:
Pman instead of pth does not lead to any error. On the
contrary, for high values of a, an error in the pres-
[r-l k(r-l){ )_
sure measure induces through fflpman) a significant Pman
error in the computed air mass flow rate. In view of The identification procedure leads to c=0.86 with
these considerations better results in all the operat- r.s.d. 0.31%, while the prediction capabilities of the
ing range could only be achieved by modifying the model are equivalent to those of model (5).
pressure sensor position in the manifold.
0.9^ , , , ,
! : ο
4.4. The volumetric efficiency \
0.85 1 \ \
Many models for the volumetric efficiency η have ? o.8 ! I I* i L!
ν
been proposed in the literature. A "black-box" ap- 1 * s s
^ 0.75 aï.....®. i L ί
proach has been adopted in Hendricks and Sorenson
(1990) where the dependence of η upon η and p^n I % ι *
ν > 0.7 & i j. : ;
is assumed to be given by the following model:
o *
2
065 1 1 j ! I
ην= ηνηΟ + ηνηΐ η + Τ|νη2 η + ηνρΐ Pman. (5) 6
°'1555 55δδ 25075 55Ô75 350U iooo
By means of the maximum likelihood technique, the CCrraannkk sshhaafftt ssppeeeedd [(rrppmm]]
Fig. 7. Values of ην measured (o), and predicted (*) by the
<j) ο ό ο c model (8), as functions of n.
: Ο :
i Φ ! i
\ \ 0 \ \ \ Note however that model is characterised by a single
- \ \
para5m eter, whose value can be estimated with great
I
«S ; *ο i \ accuracy. The experimental data (o) are compared to
the predicted ones (*) in Fig. 7. A prediction error
I
ΙΛ whiteness test has also been performed to assess the
S ο.
Ε model accuracy. This test has shown a significant
* bias with respect to n. A possible interpretation is
that Eq. (7) takes into account quasi-static effects
only, while dynamic effects are neglected. Among
these, the reverse flow can be significant at low
Throttle angle plate Π
values of n, while resonance and wave effects can
Fig. 6. Air mass flow rate through the throttle valve meas- increase the value of η at high engine speed. For
ν
ured (o) and predicted (*) as functions of a. these reasons it has been considered a different
model of the form:
4
With reference to this model, the predicted values of
JL L-ÎAÉ_+(*-I) (9) /fa (*) are compared to the measured ones (0) in Fig.
Γ-1 k(r-\){
Pman 9, as functions of n.
The estimation procedure has been repeated for two
different sets of data. In the first data set only ex- 4.6. The brake torque
perimental data with /K2400 rpm have been used to
determine Ci = 0.55 (r.s.d. 1%) and c2 = 64.84e-9 A criticism which could be made to the model here
(r.s.d. 1.8%). In the second one, data corresponding presented is due to the dependence of some quanti-
to /7>2400 lead to estimate Ci = 0.76 (r.s.d. 0.6%) ties (η„ /fa, HC, NO, CO) upon p^h and T, while for
b
and c2 = 11.2e-9 (r.s.d. 3.9%). This model is charac- control design purposes, it could be undoubtedly
terised by a very low prediction error and excellent r.
better to describe them as functions of the state vari-
s.d. of the estimated parameters. Experimental (o)
ables n, pman or the manipulated variables λ an Θ. As
and predicted data (*) are reported in Fig. 8.
for h, Pexexperimental evaluation has shown that
taking constant its value produces only a slight deg-
0.9 , , , ,
radation of the performance of models (9) and (11).
0.85 \ \ L Ϊ Things are more involved for Tb\ however, it has to
be recalled here that, in the near future, the measure
of T will be available at low costs. Moreover, it is
I " f - ti [ b
also possible to follow a black box approach where
S 0.75 5....» ί •: i
! 8
80 , , , ,
I
0.7 £ ] i I i
70 •• °
065 1 ] f ι : 60 ! i g I
06
ι50 · * u ι i
40
15W 307X5 55075 3DTJ0 35075 TOOO
CCrraannkk sshhaafftt ssopeeeedd f[rrpomm]l 1 »---8- i ι I
1»-
Fig. 8. Values of η* measured (o) and predicted (*) by the » ι j ( ι
model (9), as functions of n. 20 _ k. i \ \
# :
4.5. The brake thermal efficiency 10 _ t \ ς j [ \
ΐ°5δδ 3δ&5 25675 3507) 35&S ίδοο
In the literature, 175 is usually represented as a Crank shaft speed (rpm]
"black-box" function of n, λ, θ, . However, being
P m a n Fig. 10. Values of Tb measured (o) and predicted (*) by
the FIAT engine only weakly sensitive to variations
(12), as functions of n.
in ft a physical model for % has been derived as
follows. From the speed-density law one gets: T is estimated from w, , Α, Θ. For example, the
b P m a n
following regression model has been considered:
LFL/ = 1 X (10)
120.RT .{X14.7)
mm
On substituting this expression in (2) and by recall- Tb = Co + Ci.n +c2. ηθ + c3.0 (12)
ing the form of (7), at the steady-state it results:
which leads to the results of Fig. 10, with Co = 0.16e-
(11) 2 (r.s.d. 1%), CI = -2.15e-2 (r.s.d. 2.7%), c = -1.9
2
Pman+Wexh (r.s.d. 1.4%) and c = 0.1e-2 (2.2%).
3
The unknown parameters of model (11) have been
estimated as follows: Co = 0.8101 (r.s.d. 0.9 %) and
4.7. Exhaust emissions models
d=-0.0861 (r.s.d 4.2%).
The physical phenomena producing HC, CO, NO
x
0.35^ , , , , are clearly and extensively illustrated in Heywood
3 0 (1988), and in the references there reported. How-
i *
ever, being these phenomena highly involved, a
* - i i i ""o
common approach in the literature, see e.g. Tennant
I . '
et al. (1983) and Pianese and Rizzo (1992), is to
I 0 25 _ 4^....| ; ; :
determine empirical (static) relations of these pollut-
I « \
ants with engine variables; the unknown parameters
I $ ; ι ; ι
0.2 of these models can then be estimated as described in
0.15 _ [ i I i _ Paragraph 4.2. This procedure leads to more tracta-
[ ι ; :
01 ble models suitable for control. Since the FIAT en-
8 gine operated under λ control, another set of data
i5W 20075 25073 3ubrj 357» *000
Crank shaft speed [rpm] obtained from tests on an ALFA ROMEO Boxer
Fig. 9. Brake thermal efficiency measured (o) and pre- engine was used to select the model structures. Then,
dicted (*) by model (11), as function of n. these models were validated also on the FIAT en-
5
gine. For space limitations, the results concerning 5. CONCLUSIONS
the Boxer engine are not reported here.
In this paper a MVM for spark ignition engines has
Hydrocarbon emission. The model structure selected been presented and validated. Future work is aimed
takes the form: at verifying the predictive capabilities of the model
2 in dynamic conditions. Moreover, the film fluid
HC=c+^+X X +e 0+T iTb (13) phenomenon can be included together with the de-
2 1 b
pendencies of the model on the intake manifold and
With respect to previous results (Tennant et al., the cylinder wall temperatures in order to cope with
1983) no significant dependence of HC upon η was the warm-up phase.
found. The estimated parameters for the FIAT en-
gine (recall that λ=1) are: c=0.128e-2 (r.s.d. 7.6%), Acknowledgements. The authors gratefully ac-
0,=0.52e-^ (r.s.d. 5.9%), T =-0.2e-4 (r.s.d. 3.8%). knowledge the help of Prof. G. Rizzo and Ing. C.
bl
In order to illustrate the performance of this model, Pianese in providing the data of the ALFA ROMEO
Boxer engine.
in Fig. 11a number of collected (o) and predicted (*)
data are reported as function of Θ.
6. REFERENCES
Nitric Oxide. According to Heywood (1988), the
engine variables which mainly influence NO emis- Aquino, CF. (1981). Transient A/F Control Charac-
teristics of the 5 Litre Central Fuel Injection En-
sions are λ and θ. Furthermore, the performed
gine. SAE Paper 810494.
analysis has shown a dependence on T. This de-
b
Boam, D.J., I.C. Finlay and J.J.G. Martins (1989). A
pendence has also been experimentally found in
Model for Predicting Engine Torque Response
Tennant et al. (1983). Roughly the dependence of
during Rapid Throttle Transients in Port-injected
NO upon λ can be interpοreted aοs quadratic, while in
Spark Ignition Engines. SAE Paper 890565.
a wide range (about 10 ^β<40) NO grows linearly
Dobner, D.J. (1980). A Mathematical Engine Model
with θ. This justifies that the best model structure,
for Development of Dynamic Engine Control.
among the considered ones, takes the form:
SAE Paper 800054.
2
Hendricks, E. and SC. Sorenson (1990). Mean
ΝΟ=ο+λιλ+λ2λ 4θ,θ+ΤΜ^ (14) Value Modelling of Spark Ignition Engines. SAE
Paper 900616.
The estimated parameters are: c=-.26e-2 (r.s.d.
Heywood, J.B. (1988). Internal Combustion Engine
10.9%), 0,=0.13e-4 (r.s.d. 6.9%), T =0.91e-4
bl Fundamental. McGraw-Hill, New York.
(r.s.d. 2.5%).
Matthews, R.D., S.K. Dongre and JJ. Beaman
(1991). Intake and ECM Submodel Improvements
Carbon monoxide. It is well known that the produc-
for Dynamic SI Engine Models: Examination of
tion of CO mainly depends on λ\ in the analysis a
Tip-IN/Tip-Out. SAE Paper 910074.
quite weak but statistically significant dependence
Pianese, C. and G. Rizzo (1992). A Dynamic Model
on T has been found; then, the model here adopted
b for Control Strategy Optimisation in Spark Igni-
is.
tion Engines. Proc. of Third ASME Symp. on
Trasp. System, 44, 253-267.
Soderstrôm, T. and P. Stoica (1989). System Identi-
CO = e^(T +T T ) (15)
b 0 b lb fication. Prantice Hall, Cambridge.
Taylor, CF. (1977). The Internal-Combustion En-
The estimated model parameters are: Tbo=0.23e-l
gine in Theory and Practice. The MIT Press,
(r.s.d. 1.9%). T =-0.2e-3 (r.s.d. 5.1%).
bl Cambridge.
Tennant, J.A., AI. Cohen, H.S. Rao and ID. Powell
1'9| ! ! ! ! 1 1 (1983). Computer-aided procedures for optimisa-
ι.β _ ι j i i i 9 tion of engine controls. In: Application of Control
Theory in the Automotive Industry (R. Fruechte,
υ L 1 1 1 Ed), pp. 1-12. Inderscience Enterprises Ltd.,
=r ; i Genève.
f x
1_ 1.6 \ i. 1 \ \
υ
; * :
15 f t * t \ ι
ο
1 3 -
2δ—"—ή -22** 52fδc 3ήT 3b 52
Spark advance angle [*]
Fig. 11. HC emissions of the Fiat engine measured (o) and
predicted (*), as functions of Θ.
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Copyright © IFAC Advances in Automotive Control,
Ascona, Switzerland, 1995
MULTI-VARIABLE EXPERIMENT DESIGN TO IMPROVE ENGINE MAPPING
Justin Seabrook, B.Eng, AMIMechE
Chris Nightingale, MA, PhD, CEng, MIMechE, MSAE
University College London, Department of Mechanical Engineering,
Torrington Place, London, JVC IE 7JE, England
Abstract: The cost and time involved in mapping a modern spark-ignition engine
management system (EMS) is high because of the large number of variables that must be
accommodated. The situation is exacerbated when some of the variables interact.
Identifying the interactions can be problematic, particularly where exhaust emissions are
concerned.
The research described utilises statistically designed experiments to determine the nature
and magnitude of the main effects and interactions occurring in a prototype engine with a
number of features that directly affect emissions, including variable valve timing (inlet and
exhaust), variable intake geometry and exhaust gas re-circulation.
The implications for EMS control strategies are discussed.
Keywords: Automotive emissions, variable valve timing control, regression analysis,
engine modelling, optimal experiment design, interaction mechanisms
1. INTRODUCTION The large number of variables and responses of
interest make the research ideally suited to
One of the single largest costs in the development of statistical experiment design techniques. Such
a new engine is that of mapping the engine techniques have applications in many fields. This
management system (EMS). Determining the right paper provides an overview of the sort of problems
combinations of several variables for optimum that can be encountered during the mapping
levels of numerous output parameters is time- procedure, details how statistics can aid the
consuming and expensive. mapping process, and discusses the implications for
control strategies.
After a basic engine design has been selected there
are a large number of variables on a modern engine
that must be controlled. Particularly relevant to 2. DESCRIPTION OF ENGINE AND
exhaust emissions control are air-fuel ratio, ignition INSTRUMENTATION
timing, fuel injection timing, valve timing and
exhaust gas re-circulation (EGR). In addition to The engine is a prototype produced by Jaguar Cars
emissions, the engine designer is naturally Ltd in 1991. It is a straight-six three-litre unit with
concerned with power, fuel consumption, idle pent-roof combustion chamber design and four
stability, noise and vibration (NVH), driveability valves per cylinder. The engine has drive-by-wire
(transient response), reliability and cost. throttle control, is fitted with independent,
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