STUDY OF TRANSIT BUS DUTY CYCLE AND ITS INFLUENCE ON

FUEL ECONOMY AND EMISSIONS OF DIESEL ELECTRIC-HYBRIDS

Jairo A SandovalPh.D. Dissertation Proposal

Center for Alternative Fuels, Engines and EmissionsMechanical and Aerospace Engineering

West Virginia UniversityMarch 5th, 2010

Outline1. Motivation2. IBIS3. Objectives4. Methodology and Preliminary Results5. Contribution6. Timeline

2

1. MotivationEnvironmental effects of transportationVehicle duty cycle Fuel economy &

Emissions Hybrid buses will not perform the same on all

service routesEmissions and fuel economy correlate very

well with cycle metrics, but transit agencies do not know the metrics corresponding to their operation (e.g. acceleration or % idle)

3

Drive Cycle MetricsMetric Units

Percentage of Idle %

Stops per mile mi-1

Maximum Speed,

Acceleration (+/-),

Grade (+/-)

-

Average Speed mph

Average Speed w/o idle mph

Aerodynamic Speed mph

Standard Deviation of

Speedmph

Standard Deviation of

Speed w/o Idlemph

Metric Units

Average Acceleration ft/s2

Average Deceleration ft/s2

Characteristic

Accelerationft/s2

Kinetic Intensity mi-1

Average Time of a Stop s

Fractions in

Accel./Decel-Speed Bins%

Fractions in Grade Bins %

Mean Grade

Ascent/Descent%

4

100/%1

1

IdleVV idleno

2

~

aeroV

aki

Time, Distance, Energy, …

2. Integrated Bus Information System - IBISSome features are:

Searchable databases of transit vehicle emissions studies

Predictive emissions modeling toolsConstruction of virtual fleets

F.E., CO2, CO, NOx, HC, PM, NMHC

Diesel, CNG, Diesel-Electric Hybrid

Source: IBIS Presentation, July 20095

IBIS (Continued) Emissions data are

available on no more than 16 standard cycles + Idle cycle

Second-by-second data micro-trips new entries into the dataset

Chassis-dynamometer test data Correlations for fuel economy and

emissions Correction factors Current inputs:

(Average speed, Stops per Mile)

(Average speed, Percentage Idle)

(Average speed, Std. Dev. Speed)

(Average speed, Kinetic Intensity)

ΔSOC=0?

6

3. Objectives1. To characterize transit bus operation

Correlations between the information available to transit agencies and the cycle metricsEvaluation of in-use routes and their properties

2. To evaluate the effects of driving cycle on the F.E., CO2 and NOx emissions of diesel-electric hybrids

Variety of driving conditionsRegression based modelAllow transit agencies to assess the best routes for the hybrids

7

4. Methodology and Preliminary ResultsCharacterization of Transit Duty Cycles:

GPS logs of transit activityCollect information from transit agencyAnalysisDevelop correlations

Hybrid Bus Emissions ModelingMY 2007-2009 Allison Hybrid BusANN model for fuel, CO2, and NOx ratesDevelop hybrid controller / CalibrationSimulations / AnalysisDevelop correlations

8

4.1. Characterization of Transit Duty CyclesGPS logs of in-use

routes. 2,900 mi / 230 hr

Classify routes:×Stop-and-go×Urban×Suburban×Commuter×Express

9

Trip distance / Trip time in light and heavy traffic

Option: Process user-provided GPS logs and extract the metrics

4.1. Characterization of Transit Duty Cycles

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y= 55.1395 - 1.4102*x+ 0.0991 (x-13.2385)∙ 2

R2 = 0.6937

ŪNI=13.8

ŪNI=17.4

ŪNI=35.7

ŪNI=42.8

4.2. Vehicle Dynamic ModelThe Road Load Equation:

11

dt

dVVmmgVmgVccVbVVVAcP errfdroad sincos

2

1 22

Aerodynamic Loads due Rolling Elevation Inertial

Drag to Driveline Resistance Load Load

Friction

Vehicle Dynamic Model (Cont.)ANL’s PSAT:

12

Vehicle Dynamic Model (Cont.)ANL’s PSAT:

13

Vehicle Dynamic Model (Cont.)2-Mode Transmission:

14

Engine /Input

Ring 1

Sun 1

Motor A /Motor 2

Ring 2

Sun 2

Motor B /Motor 1

Ring 3

Sun 3

Output

C1

C2

E1 E2 E3

Ca

rrie

r 1

Ca

rrie

r 2

Ca

rrie

r 3

4 ω’s / 4 τ’s. 4 Eqns. 4 Degrees of Freedom (2 ω’s / 2 τ’s)+ ModeRemove output τ and ω

4.3. ANN Engine ModelEmissions tests of MY 2006 Allison Hybrids (17

cycles) and BAE Series Hybrids (6 cycles)Cummins ISL280, ISB 260H

15

Speed (rpm)

Tor

que

(Nm

)

800 1000 1200 1400 1600 1800 2000 2200200

300

400

500

600

700

800

900

1000

1100

1200

0

0.2

0.4

0.6

0.8

1

1.2

1.4

In-use data – ISL 280 Chassis Tests – ISB 260H

ANN Engine Model (Cont.)Trust ECU torque and speedInputs: torque, speed, delayed first derivatives

(1, 5, 10 seconds)60% training, 20% validation, 20% testingCorrect for engine NOx certification levels

(2.5/1.5 g/brp-hr)

16

Input Output

radbasLayer

logsigLayer

tansigLayer

Output Layer(linear)

ANN Engine Model (Cont.)Generalization (Partial Training):

17

Unseen Cycles

Cycles Seen in Training

0.0

0.5

1.0

1.5

2.0

2.5

3.0

1 2 3 1 2 3 4 5 6 7 1 2 3 4 5 6 7 8

NO x

(g/

bh

p-h

r)

Measured Predicted

Arterial Braunschweig WMATA

0.0

0.5

1.0

1.5

2.0

2.5

3.0

1 2 1 2 3 4 5 6 0 1 2 3 4 5 6 7 8 9

NO x

(g/

bh

p-h

r)

Measured Predicted

CBD New York Bus OCTA

Training Set

ANN Engine Model (Cont.)Performance (OCTA cycle):

18

200 220 240 260 280 300 320 340 360 380 4000

5

10

Fuel R

ate

(g/s

)

Target

Output

200 220 240 260 280 300 320 340 360 380 400

5

10

15

20

25

CO

2 (g/s

)

200 220 240 260 280 300 320 340 360 380 4000

0.05

0.1

Time (s)

NO

x (g/s

)

ANN Engine Model (Cont.)Overall Performance

Fuel: 50% of results within 0.5% accuracy80% 1.1%95% 2.5%

CO2: 50% 1.3%80% 2.8%95% 5.8%

NOx: 50% 3%

80% 5%95% 10%

19

ANN Engine Model (Cont.)Performance of Fuel Model:

20

% E

rror

Fue

l

-4

-3

-2

-1

0

1

2

3

4

Art

eria

l

Bee

line

Bra

un

CB

D

Com

m

CS

HV

C

ET

CU

rban Idle

KC

M

Man

hatta

n

NY

Bus

NY

-Com

p

OC

TA

Par

is

Tra

ns

UD

DS

WM

AT

A

Cycle

S.E

. Fue

l0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Art

eria

l

Bee

line

Bra

un

CB

D

Com

m

CS

HV

C

ET

CU

rban Idle

KC

M

Man

hatta

n

NY

Bus

NY

-Com

p

OC

TA

Par

is

Tra

ns

UD

DS

WM

AT

A

Cycle

% Error Standard Error, g/s

2

ˆ 2

2

n

yy

DFE

SSEMSESE ii

ANN Engine Model (Cont.)Performance of CO2 Model:

21

% Error Standard Error, g/s

% E

rror

CO

2

-6

-4

-2

0

2

4

6

8

10

Art

eria

l

Bee

line

Bra

un

CB

D

Com

m

CS

HV

C

ET

CU

rban Idle

KC

M

Man

hatta

n

NY

Bus

NY

-Com

p

OC

TA

Par

is

Tra

ns

UD

DS

WM

AT

A

Cycle

S.E

. CO

20.0

0.5

1.0

1.5

2.0

2.5

Art

eria

l

Bee

line

Bra

un

CB

D

Com

m

CS

HV

C

ET

CU

rban Idle

KC

M

Man

hatta

n

NY

Bus

NY

-Com

p

OC

TA

Par

is

Tra

ns

UD

DS

WM

AT

A

Cycle

ANN Engine Model (Cont.)Performance of NOx Model:

22

% Error Standard Error, g/s

% E

rror

NO

x

-15

-10

-5

0

5

10

15

20

Art

eria

l

Bee

line

Bra

un

CB

D

Com

m

CS

HV

C

ET

CU

rban Idle

KC

M

Man

hatta

n

NY

Bus

NY

-Com

p

OC

TA

Par

is

Tra

ns

UD

DS

WM

AT

A

Cycle

S.E

. NO

x0.000

0.001

0.002

0.003

0.004

0.005

0.006

0.007

0.008

0.009

0.010

0.011

Art

eria

l

Bee

line

Bra

un

CB

D

Com

m

CS

HV

C

ET

CU

rban Idle

KC

M

Man

hatta

n

NY

Bus

NY

-Com

p

OC

TA

Par

is

Tra

ns

UD

DS

WM

AT

A

Cycle

5. ContributionCorrelate transit bus activity with

information available to transit agencies and cycle metricsIBIS Module

Effect of driving cycle on NOx, CO2, and F.C. Model to help transit agencies assess the best

routes for the hybridsIBIS Module

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6. Timeline

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ACTIVITY Done2010 2011

2 3 4 5 6 7 8 9 10 11 12 1 2 3

Collection of driving data

Sizing of hybrid bus components

Analysis of drive cycle data

Development and tuning of a controller for

the 2-mode hybrid bus

Development of engine fuel consumption,

CO2, and NOx model and inclusion in PSAT

Model validation

Computer simulations varying driving

conditions

Development of regression based emissions

model

Implementation in IBIS

Compilation of the document

Oral defense

Corrections and submission

Questions???

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