phev control strategy optimization using matlab distributed … · phev control strategy...

PHEV Control Strategy Optimization Using MATLAB

Distributed Computing:From Pattern to Tuning

S. Pagerit, D. Karbowski, S. Bittner, A. Rousseau, P. SharerArgonne National LaboratorySponsored by Lee Slezak, U.S. DOE

G. SharmaThe MathWorks

MathWorks Automotive Conference3 June, 2008

2

Outline

IntroductionSetupGlobal Optimization for PatternsReal Time ControllerDIRECT Optimization for TuningConclusion

3

Outline


4

New Constraints = More Complex Vehicle

Higher use of math-based tools before and during design:– Model-Based design– Physical modeling– Monte Carlo analysis– …

Caveat:– Increased detail of modeling, complexity– Increased number of simulations

=> Longer calculation, analysis and development time

EnvironmentFuel Economy HEVs and

PHEVsCost and

Complexity

5

More Complex Vehicle = More Sensitive Control

Common Strategy= EV+CS Blended Strategy

Optimization Evaluates Control Strategy’s Potential

s

EV MODE

lowICE

η

highICE

η

offoff

km/h

CS MODE

Veh. speed

SOC

%

Depending on various driven distance, several modes are possible during charge depleting: Electric-only (EV) and Blended

Higher Electric PowerHigher Electric Energy Higher Control

FreedomFuel Savings

Potential

s

%

BLENDED

km/h

BLENDED

highICE

η

off

highICE

η

off off

6

Outline


7

Tool: PSAT – Powertrain Systems Analysis ToolkitSetup

Simulation

Analysis

8

Process: 3-Way Approach to Control Optimization

( ) ( ){ }tSOCtP nm ,

( ) ( ){ } ( )tJtSOCtP '0,000 ,1',1 ++

( ) ( ){ } ( )tJtSOCtP MMMM ',' ,1,1 ++

( )tU 0

( )tU M

( )TX( )0X.

.

.

.

.

.

Optimal Control Pattern, Minimal

Fuel Cons.

Teng

Backward model Bellman Principle

Tmc

ωeng

ωmc

Optimally tunedParameters

Control Logic DIRECT Algorithm

PSAT Various PSAT Control Strategies

PSAT

Various Control

Principles Rule-Based Control Design

Global Optimization

Control Design

Derivative Free Optimization

9

Hardware: Computation Time Requires Distributed Computing

MATLAB and Parallel Computing Toolbox

MATLAB Distributed Computing Server - 1 Header Node - 2 Worker Nodes

MATLAB Distributed Computing Server - 2 Worker Nodes

…

# 1

# 2 # 8

Server Rack: - PSAT Software - Simulation Results

10

Outline


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Finding Control Patterns Fast(er)

Robustness:– Different Cycles (e.g.: Urban, Highway,…)– Different Distances– Different Initial SOC

=> Set of 45 simulations=> Sequential computation time ~ 2 weeks

Using Distributed Computing:– Simulations run in parallel

=> Running time ~ 12 hours

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Global Optimization Showed Minimal Fuel Consumption Achieved in Blended Mode

0 10 20 30 40 50 60 70 80 90 1000.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% of total distance

SO

C

NEDC x10(gl. optim)UDDS x10 (gl. optim)Japan 10-15 x25 (gl. optim)Japan 10-15 x25 (PSAT)

Example of EV+CS Mode for comparison

3 cycles demonstrated blended strategy is optimal when knowing the distance

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Global Optimization Showed Engine Starting Condition Almost Proportional to Electrical Consumption

0 50 100 150 200 2500

5

10

15

20

25

30

35

Electric Energy Consumption (Wh/km)

Pwr w

heel

s (kW

) abo

ve w

hich

P(IC

E=O

N)=

.95

Cycle:

HWFET

10 miles

20 miles 40 miles

Driven Distance:

UDDS

LA92

14

Outline


15

Engine ON/OFF Logic & Engine Torque Request

engine torque2

eng _pwr_dmd1

divideress_soc_init

nit .soc_

ess pwr charging

Operating regionfor the engine

f(u)

Eng hot opt torq curve

? ? ?

Eng hot low torq curve

? ? ?

Eng hot high torq curve

? ? ?

acc pwr6

eng trq max5

eng spd4

SOC 3

wh spd dmd2

wh trq dmd1

eng pwr dmd

eng_trq_opt_dmd

Engine ON / OFF Logic in Stateflow

Engine Torque Demand in Simulink

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Blended Control Strategy Design Showed Significant Improvements Over EV Mode

-10%

-8%

-6%

-4%

-2%

0%

2%

0 16 32 48 64 80 96 112

Full Engine PowerOptimal Engine Power

Trip distance (km)

Up to 9% less fuel consumed

Fuel

Ene

rgy

Incr

ease

Ove

r EV/

CS

10 miles AER vehicle run on several UDDS cycles

17

Outline


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Control Parameter Tuning with DIRECT

Robustness:– Different Cycles– Different Distances

Iterative Process:– Control space is sampled at each iteration– One simulation is run for each sample– Control space is re-sampled around the ‘best’ Simulation

=> Convergence after 30 iterations ~ 400 simulations=> Sequential computation time ~ 2 days

Using Distributed Computing:– Simulations run in parallel

=> Running time ~ 5 hours

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The Longer the Electrical Distance, the More Robust

Influence of cycles (distance=23.7km)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

UDDS2 UDDS4 UDDS6 UDDS8

Fuel

Con

sum

ptio

n R

atio

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

Bat

tery

Ene

rgy

Rat

io

Fuel Consumption RatioBattery Energy Ratio

Influence of cycles (distance =71.2Km)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

UDDS2 UDDS4 UDDS6 UDDS8Cycles

Fuel

Con

sum

ptio

n R

atio

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Bat

tery

Ene

rgy

Rat

io

Fuel Consumption RatioBattery Energy Ratio

Tuned for a short distance (2xUDDS)

Tuned for a longer distance (6xUDDS)

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Outline


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Conclusion

Using a combination of optimization techniques and modeling based on PSAT and MATLAB, we were able to:– single out control patterns,– implement them in Simulink and Stateflow,– tune their parameters.

Only the use of distributed computing allows this process to be performed in a timely manner:– After setting up the MATLAB Parallel Computing Toolbox, and

Distributed Computing Servers, it took only 1 hour of development to get the first simulations running.

– The optimization times were reduced from more than 2 weeks to less than a day.

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ReferencesPlug-in Hybrid Electric Vehicle Control Strategy: Comparison between EV and Charge-Depleting OptionsSharer, P. / Rousseau, A. / Karbowski, D. / Pagerit, S.SAE World Congress 2008, April 2008

Impact of Component Size on Plug-In Hybrid Vehicle Energy Consumption Using Global OptimizationKarbowski, D. / Haliburton, C. / Rousseau, A. EVS 23, December 2007

Plug-in Hybrid Electric Vehicle Control Strategy Parameter Optimization Rousseau, A. / Pagerit, S. / Gao, D. EVS 23, December 2007

Plug-in Vehicle Control Strategy: From Global Optimization to Real Time ApplicationKarbowski, D. / Rousseau, A. / Pagerit, S. / Sharer, P. EVS22, October 2006.

Global Optimization to Real Time Control of HEV Power Flow: Example of a Fuel Cell Hybrid VehiclePagerit, S. / Rousseau, A. / Sharer, P.EVS 21, April 2005.

phev control strategy optimization using matlab distributed … · phev control strategy...

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