One of the main issues in the design, modeling, and control of innovative automotive engines is to reduce energy consumption and emission of pollutants and, at the same time, to guarantee a high level of performance indices. In particular, enhanced model-based control of the injection process has been a hot research topic in recent years to increase the combustion efficiency in common rail (CR) diesel engines and to meet strict legislations. This paper focuses on the development of a more accurate model for the electro-injector in CR diesel engines. The model takes into account the mechanical deformation of relevant parts of the electro-injector and the nonlinear fuel flow. Model parameters are then optimized by an evolutionary strategy. Simulation results confirm that the optimized model can be helpful for predicting the real trend of the injected fuel flow rate, evidenced by experimental data, thus can be helpful for injection control of CR diesel engines.

Issue Section:
Research Papers
Keywords:
Automotive controls, Modeling and simulation, Dynamic systems
Topics:
Ejectors, Flow (Dynamics), Needles, Optimization, Pressure, Fuels, Diesel engines, Common rail fuel injectors, Deformation

References

1.
Guzzella
,
L.
,
2009
, “
The Automobile of the Future—Options for Efficient Individual Mobility
,”
3rd IEEE Multiconference on Systems and Control, Saint Petersburg
,
Russia
, July 8–10, pp.
i
–i.
2.
Dyntar
,
D.
,
Onder
,
C.
, and
Guzzella
,
L.
,
2002
, “
Modeling and Control of CNG Engines
,” SAE Paper No. 2002-01-1295.
3.
Lino
,
P.
,
Maione
,
B.
, and
Amorese
,
C.
,
2008
, “
Modeling and Predictive Control of a New Injection System for Compressed Natural Gas Engines
,”
Control Eng. Pract.
,
16
(
10
), pp.
1216
–
1230
.
Google Scholar
Crossref
Search ADS
 
4.
Yan
,
F.
,
Chen
,
S.
,
Zeng
,
X.
,
Zhao
,
J.
, and
Wang
,
J.
,
2013
, “
Modeling and Analysis of Fuel Injection Split for Diesel Engine Active Fueling Control
,”
ASME J. Dyn. Syst., Meas., Control
,
135
(
6
), p.
061016
.
Google Scholar
Crossref
Search ADS
 
5.
Lino
,
P.
, and
Maione
,
G.
,
2013
, “
Design and Simulation of Fractional-Order Controllers of Injection in CNG Engines
,”
7th IFAC Symposium on Advances in Automotive Control (AAC 2013
),
Tokyo
, Sept. 4–9, pp.
582
–
587
.
6.
Lino
,
P.
, and
Maione
,
G.
,
2003
, “
Fractional Order Control of the Injection System in a CNG Engine
,”
12th European Control Conference (ECC 2013)
,
Zurich, Switzerland
, July 17–19, pp.
3997
–
4002
.
7.
Lino
,
P.
, and
Maione
,
G.
,
2014
, “
Switching Fractional-Order Controllers of Common Rail Pressure in Compressed Natural Gas Engines
,”
19th IFAC World Congress
,
Cape Town, South Africa
, Aug. 25–29, pp.
2915
–
2920
.
8.
Saponaro
,
F.
,
Lino
,
P.
, and
Maione
,
G.
,
2014
, “
A Dynamical Model of Electro-Injectors for Common Rail Diesel Engines
,”
22nd Mediterranean Conference on Control and Automation (MED)
,
Palermo, Italy
, June 16–19, pp.
207
–
211
.
9.
Lino
,
P.
,
Maione
,
G.
, and
Saponaro
,
F.
,
2015
, “
Fractional-Order Modeling of High-Pressure Fluid-Dynamic Flows: An Automotive Application
,”
8th Vienna International Conference on Mathematical Modelling
,
Vienna, Austria
, Feb. 18–20, pp.
386
–
391
.
10.
Dongiovanni
,
C.
, and
Coppo
,
M.
,
2010
, “
Accurate Modeling of an Injector for Common Rail Systems
,”
Fuel Injection
,
D.
Siano
, ed.,
Sciyo (HRV)
,
Rejeka
, pp.
95
–
119
.
11.
Lino
,
P.
,
Maione
,
G.
,
Saponaro
,
F.
, and
Li
,
K.
,
2014
, “
Evolutionary Parameter Optimization of Electro-Injector Models for Common Rail Diesel Engines
,”
Intelligent Computing in Smart Grid and Electrical Vehicles
(Communications in Computer and Information Science, Vol.
463
),
K.
Li
,
Y.
Xue
,
S.
Cui
, and
Q.
Niu
, eds.,
Springer-Verlag
,
Berlin
, pp.
539
–
551
.
12.
Lino
,
P.
,
Maione
,
B.
, and
Rizzo
,
A.
,
2005
, “
A Control-Oriented Model of a Common Rail Injection System for Diesel Engines
,”
10th IEEE Conference on Emerging Technologies and Factory Automation (ETFA 2005)
,
Catania, Italy
, Sept. 19–22, Vol.
1
, pp.
557
–
563
.
13.
Streeter
,
V.
,
Wylie
,
K.
, and
Bedford
,
E.
,
1988
,
Fluid Mechanics
, 9th ed.,
McGraw-Hill
,
New York
.
14.
Dellino
,
G.
,
Lino
,
P.
,
Meloni
,
C.
, and
Rizzo
,
A.
,
2007
, “
Enhanced Evolutionary Algorithms for Multidisciplinary Design Optimization: A Control Engineering Perspective
,”
Hybrid Evolutionary Algorithms
(Studies in Computational Intelligence, Vol.
79
),
C.
Grosan
,
A.
Abraham
, and
H.
Ishibuchi
, eds.,
Springer
,
Heidelberg
, pp.
39
–
76
.
15.
Storn
,
R.
, and
Price
,
K.
,
1997
, “
Differential Evolution: A Simple and Efficient Heuristic for Global Optimization Over Continuous Spaces
,”
J. Global Optim.
,
11
(
4
), pp.
341
–
359
.
Google Scholar
Crossref
Search ADS
 
16.
Vesterstrøm
,
J.
, and
Thomsen
,
R.
,
2004
, “
A Comparative Study of Differential Evolution, Particle Swarm Optimization, and Evolutionary Algorithms on Numerical Benchmark Problems
,”
Congress on Evolutionary Computation (CEC2004)
,
Portland, OR
, June 19–23, Vol.
2
, pp.
1980
–
1987
.
17.
Price
,
K.
,
Storn
,
R.
, and
Lampinen
,
J.
,
2005
,
Differential Evolution—A Practical Approach to Global Optimization
,
Springer
,
Berlin
.
18.
Das
,
S.
,
Konar
,
A.
, and
Chakraborty
,
U.
,
2005
, “
Two Improved Differential Evolution Schemes for Faster Global Search
,”
7th Annual Conference on Genetic and Evolutionary Computation
,
H.
Beyer
, ed., Washington, DC, June 25–29,
ACM-SIGEVO
, pp.
991
–
998
.
19.
Chakraborty
,
U.
,
2008
,
Advances in Differential Evolution
,
Springer-Verlag
,
Berlin
.
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