Optimization Techniques for the Design of Hybrid …arc.engin.umich.edu/events/archive/annual/conf99/geodela.pdfTHE UNIVERSITY OF MICHIGAN arc AUTOMOTIVE RESEARCH CENTER Optimization

THE UNIVERSITY OF MICHIGAN

ar c AUTOMOTIVE RESEARCH CENTER

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



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



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



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%



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



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)



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



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



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



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)



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)



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



HEV Driving SchedulesHEV Driving Schedules

Urban Cycle Highway Cycle

SAE Test Procedure J1711

HWY #2HWY #1URBAN #2URBAN #1

mph

mph



HIWAYCITY

PERFORMANCERUN



227

239

251

263

**

** g/kWhr



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



Perturbation ConceptPerturbation Concept

RUN/ANALYZE

PERTURB

min F = -(w1*mpg) + (w2* σ2)

σ



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



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%



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%



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



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



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



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





HighwayCycle

LOW SOC

HIGH SOC



UrbanCycle

LOW SOC

HIGH SOC

Documents

Optimization Techniques for the Design of Hybrid …arc.engin.umich.edu/events/archive/annual/conf99/geodela.pdfTHE UNIVERSITY OF MICHIGAN arc AUTOMOTIVE RESEARCH CENTER Optimization