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J. Karl Hedrick Carlos Zavala Pannag Sanketi Mechanical Engineering Dept., University of California, Berkeley. Model-Based Control for Automotive Cold Start Applications. 2007 CHESS Winter Meeting. Regulation limit. HC. Cumulative HC amount. 100. Speed. Speed[km/h]. 0. 0. 25. 50. - PowerPoint PPT Presentation
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Model-Based Control for Automotive Cold Start Applications
J. Karl Hedrick Carlos Zavala
Pannag Sanketi
Mechanical Engineering Dept., University of California, Berkeley
2007 CHESS Winter Meeting
Coldstart Challenges
Low emission
- Suppressing emissions, especially HC
High quality
- Driveability : noise & vibration
- Robustness against environmental condition and disturbance
Less cost
- Calibration effort
- Design process, especially verification
- Computational load
- Sensors
0 25 50 75 1000
100
Spe
ed[k
m/h
]
Cum
ula
tive
HC
am
ount
Time[sec]
HC
Speed
Regulation limit
Coldstart in IC Engines-The problem
• The catalyst is not active below temperatures of around 300C- 400C
• Cold Combustion Chambers and poor vaporization in intake manifold
• Oxygen Sensor not active at cold temperatures
…more than 90% of Hydrocarbon (HC) emissions is produced during the Coldstart Cycle
Model Based Approach to Emissions Reduction during
Coldstart
• Utilizes formal description of the engine to derive efficient ways of control. – Physical Models. Intuitive representation.– Black box models. Non-physical parameters.– Gray models. Combination of the two above
• Motivation - improved control - efficient generation of software - software reusability
Model Based Strategy
ImplementationAnd
Testing
Engine Model Catalyst Model
Model Validation
Controller Design
Next designiteration
Control Oriented Modeling
• Simplicity in models is important
dx/dt= f(x,u)
Lumped Parameter Model (preferably low order ODE)Complex nonlinear system
Engine Subsystems
• Manifold Dynamics • Fuel Dynamics • Catalytic Converter
• Torque Gen• Raw HC• Exh Temp
Engine Subsystems Modeling
For control of air-fuel ratio, idle speed, models developed ~1980
• Combustion torque generation• Rotational dynamics and time delays• Actuator and sensor dynamics
• Air and fuel dynamics• Catalytic converter dynamics• Engine thermal dynamics
General Purpose Engine Modeling
Cold Start Engine Modeling
Fuel Dynamics Model
• Poor vaporization when air intake is cold
Puddle
AFR Estimation
using Fuel Dynamics
Use of fuel-dynamics model to predict AFR.
“Fuel Dynamics Model For Engine Coldstart”, Zavala, et.al, IMECE2006-15203, Nov. 2006
Catalytic Converter Model*
conversionefficiency map
cat. substratethermal
dynamics
O2 storagedynamics
internal convection:
* [Brandt, Wang, Grizzle, 1997]
external convection:
Qin=hinAin(Texh - Tcat )
Qout=houtAout(Tcat - Tamb )
Important Elements in a Catalyst Model
Heat transfer coefficients of the catalyst
Important Elements in a Catalyst Model
0 10 20 30 40 50 60 70 80 90 1000
50
100
150
200
250
300
350
T cat (
C)
Time (s)
Typical Experimental Catalyst Temperature Profile
Plateau in the Tcat profile
–Due to evaporation of moisture
–Starting point can be detected (~470 C)
–For finding the end of plateau, various methods – adaptation, offline calculation of evaporation heat*
*[Sanketi, Zavala, Hedrick et al., AVEC ’06]
Experimental plots of Catalyst
Raw HC and Texh Modeling
• Simple, intuitive models
– Suitable for controller design
• Inputs chosen based on physics and experimental data
– AFR, Spark directly affect combustion
– Changes in RPM affect the combustion quality
• Sum of first order linear systems
– such behavior observed in exp
• Saturations, offsets on inputs exist
• Use of Least Squares to find parameters
Texh Modeling
Raw HC Modeling
Control Design
– Performance requirements
– Uncertainty
– Nonlinearities
– Actuator bandwidth
– Sensor noise
– Disturbances
Plant?yu
Once the plant is defined, the synthesis of a controller should considered :
Throttle angle
Lab Engine Interface
Texh sensor
AFR sensor
HC Analyzer
Tcat sensor
Catalyst model
Engine out HC estimation
Catalyst temperature estimation
Tailpipe HC estimation
Amount of Fuel
Spark Timing
AIR AIR
Variable Valve Timing
In cylinder pressure measurement
HC formation model
Air induction dynamics
Fuel induction dynamics
Thermal model
Two control approaches: mean value-hybrid
Plantmodel
ControlObjectives
ControllerDesign
Controller
Plantmodel
ControlObjectives
Hybrid ControllerDesign
Controller 1 … Controller n
Controller i
Controller kController j
…
Mean Value Hybrid
ControllerOperation Operation
Design Design
Mean Value MIMO Controller
Trying out different profiles
• Different HC desired and Tcat profiles
Desired Profiles
Model-Based Integration of Embedded Systems
• analysis of complex embedded systems
• software assurance through modeling in all phases of software development process
• Handling hybrid system analysis• Software timing analysis
The complexity of automotive systems demands the use of more sophisticated tools for control software verification:
Why hybrid models?
• Advantages
– It accounts for continuous dynamics and discrete events.
– It offers a more detailed description compared to mean-value models.
• Disadvantages
– No analytic solutions for stability analysis
– More complicated than mean-value models.
– Analysis tools still in development
Engine Model
• Mean Value models of Intake air flow and manifold air mass. (continuous dynamics).
• Air and fuel flowing into cylinder calculated for each combustion cycle.(Discrete quantities).
• Strokes of engine considered as discrete events using finite state machines (FSM:hybrid) .
• Torque and pollutants modeled for each combustion cycle. (continuous functions based on events: hybrid).
Controller Verification of Hybrid Systems
• Question of stability and evolution of the states
• Model simulations cannot cover all possible trajectories inside a set
• Reachability analysis
– Tells you how your state space will behave with time starting given a set of initial conditions and bounds on inputs
– Very useful in verifying the controller performance
Example Hybrid Controller
• Cumulative tailpipe HC function of both raw HC and catalyst efficiency
• Trade off exists between the two objectives
• A high level hybrid controller to exploit the trade-off
Control Hierarchy
The low level Texh and AFR controllers use spark timing and fuel injection rate as the inputs respectively
The Tcat and HC dynamic surface controllers use Texh and AFR respectively
The hybrid controller switches between the Tcat and HC dynamic surface controllers
Hybrid Controller Modes
Helps fast catalyst light-off
Helps keep the raw emissions low
Reachable sets
Test: starting from a safe set, remain in the set.
Set of Initial states
Forward Reachable Set
TargetSet
Backwards Reachable Set
Test: starting from an unsafe set, never touch the set of initial conditions
Backwards reachable set calculation
is the set of states for which, for all control actions, there exists a disturbance action which can drive the system to in at most
Say, the control u wants to keep the system away from target set of states whereas the disturbance d tries to drive the system to the target set G(0).
Now how to compute this set?
Turns out that it can be computed by solving a HJI PDERef. Tomlin et.al
TargetSet G(0)
Backwards Reachable Set G(t)
Reachability Analysis of Coldstart Controller*
Backwards Reachability
*[Sanketi, Zavala, Hedrick]- IJC, 2006
Conclusions
• Hybrid modeling helped to achieve a more detailed description of engine operation
• Hybrid control gave the chance to explore the tradeoff of hydrocarbon emissions level and catalyst light-off.
• Hybrid modeling is a useful tool for coldstart analysis.
Future of coldstart control
• Fewer experiments for model validation. • Closed-Loop control design• Easy adaptation to new engines.• Automated code generation. • Automated software validation and
verification.• Use of AFR and HC production sensors and/or
model based observers.