optimization techniques for the design of hybrid...
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Optimization Techniques for theOptimization Techniques for theDesign of Hybrid PropulsionDesign of Hybrid Propulsion
SystemsSystems
George Delagrammatikas
May 26, 1999
ARC HEV Project - Thrust Areas 4 and 5
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OutlineOutline
¥ Revisit of ARC 1998- Brief Overview - Alternative Problem Statements
- Apply to Hybrid SUV Problem
¥ Main Concerns Currently Being Addressed- Engine Scaling Issues
- Effect of Driving Cycles
- Product Platform Design
¥ Conclusions- Continuing Research
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ARCHEV 98 Case StudyARCHEV 98 Case Study
ADVISOR
Engine Scaling only requiresone input variable:
desired displacement of newengine
http://www.http://www.cttsctts..nrelnrel..govgov/analysis/analysis
TDES
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HEV Problem StatementsHEV Problem Statements
Maximize: Minimize:f(xADVISOR, xTDES) = fuel economy f(xADVISOR, xTDES) = 0-60 time
xADVISOR = {motor size, battery size} xADVISOR = {motor size, battery size}
xTDES = {engine size} xTDES = {engine size}
Subject to: Subject to:
0-60 time < 12 s fuel economy > 45 mpg
40-60 time < 5.3 s 40-60 time < 5.3 s
max speed > 85 mph max speed > 85 mph
0-85 time < 23.4 s 0-85 time < 23.4 s
5 s dist. > 140 ft 5 s dist. > 140 ft
max accel. > 0.5 gÕs max accel. > 0.5 gÕs
55 mph grade > 6.5% 55 mph grade > 6.5%
Maximize: Minimize:f(xADVISOR, xTDES) = fuel economy f(xADVISOR, xTDES) = 0-60 time
xADVISOR = {motor size, battery size} xADVISOR = {motor size, battery size}
xTDES = {engine size} xTDES = {engine size}
Subject to: Subject to:
0-60 time < 12 s fuel economy > 45 mpg
40-60 time < 5.3 s 40-60 time < 5.3 s
max speed > 85 mph max speed > 85 mph
0-85 time < 23.4 s 0-85 time < 23.4 s
5 s dist. > 140 ft 5 s dist. > 140 ft
max accel. > 0.5 gÕs max accel. > 0.5 gÕs
55 mph grade > 6.5% 55 mph grade > 6.5%
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Optimization Results (PNGV)Optimization Results (PNGV)
Fuel EconomyProblem
PerformanceProblem
Engine (kW) 32.6 34.2
Motor (kW) 42.1 75.1
Battery (kW) 53.5 97.2
Fuel Economy(mpg)
48.5 45.3
0-60 time (s) 10.2 7.9
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SUV ModelSUV Model
¥ Component Models- 6.5 L, 120 kW diesel engine
- SUV chassis, CD, Cr
- 4-speed Automatic Transmission (RWD)
¥ Hybridized SUV- Parallel Configuration (power assist)
- 25 kW Electric Motor
- NiMH Batteries (12 V Battery Packs @ 18 kg each)
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Optimization Results (SUV)Optimization Results (SUV)Fuel Economy
HEVPerformance
HEVBaseline
Conventional
Engine (kW) 100 115 120
Motor (kW) 41 50 -
Battery (# ofmodules)
12 31 -
Fuel Economy(mpg)
25.8 22.7 17.6
0-60 time (s) 11.3 9.6 14.8
Mass (kg) 2310 2652 1974
0-85 time (s) 23.2 18.2 25.9
5 sec.dist. (ft) 150.3 157.2 140.2
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Is the Engine Scaling Scheme Valid?Is the Engine Scaling Scheme Valid?
¥ Engine Geometries are Assumed to be Optimal- Is heat transfer the same?
È surface area to displaced volume ratio
- what about discrete variables?È number of cylinders
- no mention of injection timing (Tinj) as a variable
- need for validation of a range of engines
¥ Investigate Engine at Component Level- Investigate design space - variable sensitivity
- Increase model complexity - turbocharged
¥ Is there a Better Way?- Development of a robust methodology for automatic
geometry optimization
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TurbochargedTurbocharged Diesel Engine Study Diesel Engine StudyMaximize: xTDES:
f(xTDES) = power bore
stroke
Subject to: connecting rod length
overall phi < 0.7 compression ratio
heightoverall < 400 cm. fuel mass injected
heightclear > 5 mm. engine RPM
1.5 < conrl/stroke < 2.5
Sp < 12.0 m/s
bore/stroke > 1.1
bsfc < 300 g/kWhr
Maximize: xTDES:
f(xTDES) = power bore
stroke
Subject to: connecting rod length
overall phi < 0.7 compression ratio
heightoverall < 400 cm. fuel mass injected
heightclear > 5 mm. engine RPM
1.5 < conrl/stroke < 2.5
Sp < 12.0 m/s
bore/stroke > 1.1
bsfc < 300 g/kWhr
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Turbo-Diesel ResultsTurbo-Diesel Results
1.00E+01
1.50E+01
2.00E+01
2.50E+01
3.00E+01
3.50E+01
8 9 10 11 12 13 14 15 16
BORE
STROKE
CONRL
CMRTIO
Displacement (liters)
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Turbo-Diesel ResultsTurbo-Diesel Results
0.00E+00
5.00E+01
1.00E+02
1.50E+02
2.00E+02
2.50E+02
3.00E+02
3.50E+02
8 9 10 11 12 13 14 15 16
2.10E+03
2.15E+03
2.20E+03
2.25E+03
2.30E+03
2.35E+03
2.40E+03
2.45E+03
2.50E+03
2.55E+03
2.60E+03
FMIN
POWER
RPM
Displacement (liters)
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Engine Scaling EvaluationEngine Scaling Evaluation
¥ Strengths- Automatic engine optimization models
È Large-scale systems integration capability
- Quicker running times - accuracy not sacrificed
- Injection timings are now optimized internally
¥ Weaknesses- Manifolds are scaled using volume ratios
- Discrete variables are not dealt with in this frameworkÈ Number of cylinders?
È Number of valves?
- Validation for the entire range is still neededÈ Varying friction correlations are needed
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HEV Driving SchedulesHEV Driving Schedules
Urban Cycle Highway Cycle
SAE Test Procedure J1711
HWY #2HWY #1URBAN #2URBAN #1
mph
mph
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HIWAYCITY
PERFORMANCERUN
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227
239
251
263
**
** g/kWhr
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Driving Schedule IssuesDriving Schedule Issues
¥ Can anything be done to cluster operation pointsand then optimize an engine around that particularisland?
- Transmission and control strategiesÈ studies on variables that affect motor torque and speed
¥ Optimize for a set of operating modes by changingcontrol strategies?
- Power mode
- Fuel economy mode
¥ Are the driving cycles realistic?- Moore (SAE 961660)
È scale FTP velocities by ~ 1.3; power throughput concept
- Perturb driving schedule and use robust design techniques
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Perturbation ConceptPerturbation Concept
RUN/ANALYZE
PERTURB
min F = -(w1*mpg) + (w2* σ2)
σ
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CASE STUDY: Product PlatformCASE STUDY: Product PlatformDesignDesign
¥ Definition: Products which use common components inorder to reduce costs.
¥ Approach: Use of multi-objective function optimizationalong with Pareto sets (Nelson, Parkinson, &Papalambros, 1999).
¥ Case Study: Show optimal design of a conventional andparallel-HEV powertrain
1. IC engine as a common component
2. final drive as a common component
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Parallel HEV Optimal DesignParallel HEV Optimal DesignProblem StatementProblem Statement
Maximize:
fHEV(xHEV) = mpg (combined fuel economy)
xHEV = {engine, motor, battery size, final drive ratio}
Subject to:
g1-HEV = 0-60 mph time < 12 seconds
g2-HEV = 40-60 mph (passing time) < 5.3 seconds
g3-HEV = 0-85 mph time < 23.4 seconds
g4-HEV = maximum acceleration > 0.5 g
g5-HEV = maximum speed > 85 mph
g6-HEV = 5 second distance > 140 feet
g7-HEV = max grade at 55 mph > 6.5%
g8-HEV = max grade at launch > 30.0%
g9-HEV = Delta SOC for FUDS < 0.5%
g10-HEV = Delta SOC for FHDS < 0.5%
Maximize:
fHEV(xHEV) = mpg (combined fuel economy)
xHEV = {engine, motor, battery size, final drive ratio}
Subject to:
g1-HEV = 0-60 mph time < 12 seconds
g2-HEV = 40-60 mph (passing time) < 5.3 seconds
g3-HEV = 0-85 mph time < 23.4 seconds
g4-HEV = maximum acceleration > 0.5 g
g5-HEV = maximum speed > 85 mph
g6-HEV = 5 second distance > 140 feet
g7-HEV = max grade at 55 mph > 6.5%
g8-HEV = max grade at launch > 30.0%
g9-HEV = Delta SOC for FUDS < 0.5%
g10-HEV = Delta SOC for FHDS < 0.5%
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Conventional with CVTConventional with CVT Powertrain PowertrainOptimal Design ProblemOptimal Design Problem
StatementStatement
Minimize:
fCVT(xCVT) = 0-60 mph time
xCVT = {engine size, final drive ratio, cvt lower, cvt upper ratio}
Subject to:
g1-CVT = mpg (combined fuel economy) > 40 mpg
g2-CVT = 40-60 mph (passing time) < 5 seconds
g3-CVT = 0-85 mph time < 22 seconds
g4-CVT = maximum acceleration > 0.5 g
g5-CVT = maximum speed > 100 mph
g6-CVT = 5 second distance > 140 feet
g7-CVT = max grade at 55 mph > 6.5%
g8-CVT = max grade at launch > 30.0%
Minimize:
fCVT(xCVT) = 0-60 mph time
xCVT = {engine size, final drive ratio, cvt lower, cvt upper ratio}
Subject to:
g1-CVT = mpg (combined fuel economy) > 40 mpg
g2-CVT = 40-60 mph (passing time) < 5 seconds
g3-CVT = 0-85 mph time < 22 seconds
g4-CVT = maximum acceleration > 0.5 g
g5-CVT = maximum speed > 100 mph
g6-CVT = 5 second distance > 140 feet
g7-CVT = max grade at 55 mph > 6.5%
g8-CVT = max grade at launch > 30.0%
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Multi-Objective ProblemMulti-Objective ProblemStatementsStatements
Minimize:
f = (-1)* w1*fHEV(xHEV) + w2*fCVT(xCVT)
Subject to:
g1-10-HEV
g1-8-CVT
case 1:
h1 = (engine size)HEV = (engine size)CVT
case 2:
h1 = (final drive)HEV = (final drive)CVT
Minimize:
f = (-1)* w1*fHEV(xHEV) + w2*fCVT(xCVT)
Subject to:
g1-10-HEV
g1-8-CVT
case 1:
h1 = (engine size)HEV = (engine size)CVT
case 2:
h1 = (final drive)HEV = (final drive)CVT
CommonICE
CommonFD
Parallel-HEV
Conventional
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Results:Results: Pareto Pareto Set Set
Pareto Set Defining Design Trade-Offs
37.038.039.040.041.042.043.044.045.046.047.0
8.0 8.5 9.0 9.5 10.0 10.5
0-60 mph time (Conventional-CVT)
mp
g -
co
mb
ine
d (
Pa
rall
el-
HE
V)
Common Engine Common Final Drive
Pareto Set Defining Design Trade-Offs
37.038.039.040.041.042.043.044.045.046.047.0
8.0 8.5 9.0 9.5 10.0 10.5
0-60 mph time (Conventional-CVT)
mp
g -
co
mb
ine
d (
Pa
rall
el-
HE
V)
Common Engine Common Final Drive
null platform
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ConclusionsConclusions¥ HEV Project
- Further study of SUV feasible design domain
¥ Diesel Engine ÔStand-AloneÕ Optimization Framework- Automatic engine optimization capabilities
- Need validation, emissions, and manifold capabilities
¥ Driving Cycle Analyses- Developing methods for robust engine design
- Engine map use clustering with varying control strategies
¥ Platform Design- Framework ready for further study and application
- Apply to several common components
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ContributorsContributors
AdvisorsDr. Panos ‘Noah’ Papalambros
Dr. Nestor Michelena
Graduate StudentsRyan FelliniMike Sasena
AdvisorsDr. Dennis Assanis
Dr. Zoran Filipi
Graduate StudentGeorge Delagrammatikas
Optimization GroupOptimization Group Engine GroupEngine Group
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HighwayCycle
LOW SOC
HIGH SOC
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UrbanCycle
LOW SOC
HIGH SOC