Transcript Sample Title Slide - Missouri University of Science and Technology

Thermodynamic Based Modeling for
Nonlinear Control of Combustion Phasing
in HCCI Engines
J.B. Bettis, J.A. Massey and J.A. Drallmeier
Department of Mechanical and Aerospace Engineering
Missouri University of Science and Technology
J. Sarangapani
Department of Electrical and Computer Engineering
Missouri University of Science and Technology
Oak Ridge National Laboratories
June 23, 2010
Outline
– Background
– State-Space Methods
– Specific Objective
– Experimental Setup
– Model Development
– Results
– Extension to Other Fuels
– Conclusions
Background
– Unlike SI or CI engines, HCCI requires advanced control
strategies for proper control of combustion phasing.
– A model which captures the key dynamics of the HCCI
process is essential to achieve this control.
– Modeling the onset of combustion is difficult due to its
dependence on chemical kinetics through reactant
concentrations and temperature (Yao et al.)
– Control-oriented models must remain as simplistic as
possible while still capturing the key dynamics of the
process.
– Previous models of this nature have been developed, most
of which utilize linearization in order to achieve effective
control (Shaver, Roelle et al).
State-Space Methods for Control
• Strategy used for advanced control
– Can handle multiple input-multiple output nonlinear systems
• Represented by a discrete system of the form:
–
–
–
–
xk+1 = F(xk,uk) (i.e. T1,k+1 = F(T1,k, αi,k, θ23,k, Tin,k, αe,k, αe,k-1))
Current state is determined by previous state and input
F is typically nonlinear
Control input is inside the nonlinearity
• Linearization essentially simplifies the control problem, but
comes with a price
– F becomes linear
– xk+1 = Axk + Buk
– Reduced operating range and loss of some nonlinearities
• This model developed with nonlinear control in mind
– Significant challenges in control, but more accurate
– Motivation for a discrete state engine model
Specific Objectives
The specific objective of this work is to
develop a nonlinear control-oriented model of
the HCCI process which provides a platform for
nonlinear controller development.
Methodology:
1. Use a five state ideal thermodynamic cycle to develop a
discrete model.
2. Investigate the engine cycle to determine where it
should begin for control.
3. Investigate HCCI combustion timing models to
determine which one best balances accuracy and
simplicity.
Experimental Setup
• Hatz 1D50Z CI engine operating in HCCI mode
• Existing preliminary data taken by Scott Eaton and Jeff Massey at
ORNL.
• Varied intake temperature using resistance heater to vary timing
• Run using a 96RON Unleaded Test Gasoline (UTG96)
• Sister engine being set up at MS&T
Model Development
• Five state thermodynamic cycle
– Adiabatic, constant pressure induction
– Isentropic compression
– Constant volume combustion
– Isentropic expansion
– Isentropic blowdown to an adiabatic, constant
pressure exhaust
• Stoichiometry modeled using C/H = 7/16
– Gasoline-type fuels (exhibit little low temperature
heat release (LTHR))
– Extension to other fuels possible
Model Development
Engine cycle begins with compression.
Model Development (Combustion Timing)
• Integrated Arrhenius Rate model (Shaver)
– Relates timing to reactant concentrations and
temperature
– Evaluate integrand at TDC
– Modified integration limits
Fdes 
 2 3  

IVC
  Ea 
a
b
C7 H16 TDC O2 TDC
A exp 
 RTTDC 
d

Threshold calculated at one setpoint and held constant at all others.
Model Development (Combustion Timing)
• Variable Δθ
– Real combustion event is not
instantaneous
– Exp. data shows relation to SOC
– Developed correlation from
exp. data
• Related to chemical kinetics
(Chiang, Stefanopoulou)
• Residual fraction
– Effects combustion through
temperature and dilution.
– Introduces cyclic coupling
– Utilized correlation from the
literature (Waero)
Model Development (Control)
• Definitions for control
– State variables
• Temperature at IVC
• Residual fraction
– Inputs
• Intake temperature
• External EGR fraction
• Fueling rate
– Outputs
• Peak pressure
• θ23
Results - Validation
Pressure Evolution
6 gpm UTG96
9
Tin=495K
Tin=463K
• Simple model
captures pressure
evolution
• Single threshold
captures drop in
peak pressure
Results - Validation
SOC Tracking
Fdes evaluated using 9 gpm
fueling rate at Tin=463K.
9 gpm UTG96
Trends are most important
for control.
6 gpm UTG96
Results - Validation
Model θ23 vs. Exp. CA50
Variable Δθ
has significant impact.
9 gpm UTG96
Constant Δθ representative
of linearization.
6 gpm UTG96
Results
Pressure Rise Rates
Efficiency
PW3 ig Psoc
 
PRR
Fuel Energy

UTG96
UTG96
Heat transfer effects.
Unburned Hydrocarbons.
Results
HCCI Operating Range
Excessive pressure
rise rates
Limits effectively captured
by model.
9 gpm fueling rate
UTG96
Late combustion
results in significant
cyclic variations
Results
HCCI Operating Range
9 gpm fueling rate
UTG96
Late combustion
resulting in significant
cyclic variations
Excessive pressure
rise rates
Future control objective: Maximize efficiency
while minimizing PRR.
Preliminary Results
Closed-Loop Control
•
Optimal Neural Network controller tracks a desired θ23.
– Possible future control objectives are also shown
Preliminary Results
Closed-Loop Control
• Controller based on
previous work done at
MS&T for lean SI engines.
• Nonlinear NN controller.
• Controller learns how the
model behaves and then
tracks a desired θ23.
• Noise added to states
Extension to Other Fuels
Experimental data reveals that
all fuels behave similarly as intake
temperature is varied.
How can we explain the shift
seen in the experimental data as
the fuel is varied?
Similarities between HCCI auto-ignition
and SI knock suggest that RON and
MON may be responsible.
Extension to Other Fuels
• Due to differences in engine
operating conditions, RON and
MON alone cannot fully describe
HCCI ignition (Kalghatghi)
• It turns out Octane Index (OI)
does a good job of predicting
HCCI auto-ignition (Kalghatghi)
 Combines RON and MON values
 Accounts for engine operating
conditions
Gasoline-Type
TRF
UTG
PG
TRF
UTG
Oxygenates
E50
OI  RON  KS
1
where K  0.00497Tcomp15  0.135   3.67
 
Hydrocarbons and alcohols
exhibit different behavior.
E85
E50
Extension to Other Fuels
• Physical relationship between OI and Ea
• Resistance to auto-ignition vs. energy required for reactions to
occur
• Developed an experimental correlation between OI and
activation temperature (Ea/Ru)
• Based on experimental combustion timing
TRF
UTG96
PG
E50
E85
Extension to Other Fuels
• Using the same threshold value, the activation temperature was
modified to account for different fuels
• Separate model developed for alcohols based on E85 stoichiometry
• Same general form as previous model
• Accounting for OI allows the model to predict ignition timing for various
fuels
Gasoline-Type
Oxygenates
Conclusions
– A control-oriented model of the HCCI process was
developed in the form of a nonlinear discrete time
system for state space control.
– This model was validated against experimental HCCI
data and was able to accurately predict trends.
– The model displays an operating range similar to that
seen in experiment.
– The model displays high sensitivity to intake
temperature, similar to what is seen in the literature
(Yao et al.)
– Extension to other gasoline-type fuels is possible by
modifying the activation temperature to reflect
changes in OI.
Acknowledgments
Funding for this project was provided by the
National Science Foundation under grant ECCS0901562. Also, thanks to Dr. Bruce Bunting of
ORNL for allowing data collection from the Hatz
engine.
Questions?
State Space Methods
• Model results in a non-affine nonlinear
discrete system.
– xk+1 = F(xk,uk, uk-1)
– yk = H(xk,uk-1)
• Control input is inside the nonlinearity
• F and H are not accurately known, so how can
we linearize it?
• Significant challenges in control due to MIMO
non-affine system.
Results
9 gpm fueling rate
No EGR
Output Sensitivity
• Gave random 1% perturbations to Tin
• Observed effects on control outputs
Peak Pressure Return Map for Varying Intake Temperatures (alphae = 0)(9 gpm fuel rate)
(Tegr = 300)(Variable dtheta)
Theta23 Return Map for Varying Intake Temperatures (alphae = 0)(9 gpm fuel rate)
(Tegr = 300)(Variable dtheta)
65
470
460
55
Theta23 (i+1), (CAD)
Peak Pressure (i+1), (bar)
60
50
45
40
35
30
25
20
15
Temp =
Temp =
Temp =
Temp =
Temp =
10
5
0
0
5
10
15
20
25
30
35
40
45
Peak Pressure (i), (bar)
50
55
410
430
450
470
490
60
450
440
430
420
410
400
390
380
Temp = 410
Temp = 430
Temp = 450
Temp = 470
Temp = 490
370
360
65
350
350
360
370
380
390
400
410
420
430
440
450
460
Theta23 (i), (CAD)
Ignition does not occur for intake temperatures less than 430 K,
which is also seen in the experimental data.
Sensitivity increases as temperature decreases.
Outputs much more sensitive to temperature than dilution.
470
Preliminary Results
Closed-Loop Control
9 gpm fueling rate
Tin = 463 K
Controller effectively rejects noise.
Control switched ON
Cyclic variability is significantly
reduced when control is switched on.
Preliminary Results
Closed-Loop Control
Optimal controller results in a
tradeoff between tracking Theta23
and energy expended (via Tin).
The faster we make the Tin actuator,
the faster we can track Theta23.
Intake temperature required
to track Theta23.
Preliminary Results
Closed-Loop Control