POWER MANAGEMENT STRATEGY FOR A …hpeng/COE-control-seminar-11...1 arc POWER MANAGEMENT STRATEGY FOR A PARALLEL HYBRID ELECTRIC TRUCK Chan-Chiao (Joe) Lin, Huei Peng, Jessy W. Grizzle

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POWER MANAGEMENT STRATEGY FOR A POWER MANAGEMENT STRATEGY FOR A PARALLEL HYBRID ELECTRIC TRUCKPARALLEL HYBRID ELECTRIC TRUCK

Chan-Chiao (Joe) Lin, Huei Peng, Jessy W. Grizzle The University of Michigan

Introduction and Motivation

Simulation model and preliminary rule-based control

Dynamic programming techniques

Improved rule-based control law

Simulation results

Dyno test results

Conclusions

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COE Control Seminar-11/01-2002- 2

What is a Hybrid Vehicle?What is a Hybrid Vehicle?

• A HV has at least two sources of motive power

- Prime: IC engines, fuel cells, gas turbines

- Secondary: Batteries, flywheels, ultra-capacitors, hydraulics

• The most popular type: ICE + battery/motor (HEV)

• Bottom-line: “Better” than any single power source.

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Potential Steps Towards Breakthrough Potential Steps Towards Breakthrough in Fuel Efficiencyin Fuel Efficiency

0

10

20

30

40

50

60

70

80

90Es

timat

ed M

etro

-Hig

hway

Fue

l Eco

nom

y (m

pg)

80

27

∆ 6mpg∆ 4mpg

∆ 8mpg

∆ 10mpg

∆ 10mpg

∆ 15mpg

Description: Current Taurus Ultralite Aero/Roll Conv. P/T Match Opt. P/T Match Adv. P/T Adv. Hybrid P/T

Weight (lbs): 3200 2000 2000 2000 2000 2000 TBD

Powertrain: 3.0 L, 2-valve Same Same 1.4 L, A/T 1.4 L, Auto M/T 1.2L CIDI, Auto M/T SameSource: Mike Schwarz, Ford Motor Company

Ligh

t-wei

ght

Aer

o-ro

ll

Smal

l eng

ine

MT

DI H

EVarc


HEV ConfigurationsHEV ConfigurationsSeries

Simpler mechanical transmissionsOptimum engine efficiency and low emissionBoth ICE and Electric drive rated to the maximum power requirement (main disadvantage)lower overall efficiency (main disadvantage)

Parallel (e.g. Chrysler MYBRID ESX2)More flexibility—sizing and control design more complicatedLower power/weight/cost, and better efficiency.

Series-parallel combination (e.g. Toyota Prius)

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Growing Market (Toyota)Growing Market (Toyota)

102,96789,19752,26933,24317,988332Cumulative total

102,96713,77036,92819,02615,25517,656332Total byyear/month

5689151239Coaster hybrid (bus)

2,0945201,574----Crown mild hybrid system

11,7265,8405,886----EstimaHybridminivan

89,0917,40229,45919,01115,24317,653323Prius

1997-2002/3

2002/1-3

20012000199919981997PeriodModel

http://www.toyota.com/about/news/environment/2002/04/22-1-hybrid.html

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Advanced PowertrainAdvanced Powertrain

0

10

20

30

40

50

60

0 20 40 60 80 100 120PERCENT LOAD

PER

CEN

T TH

ERM

AL

EFFI

CIE

NC

Y FUEL CELL SYSTEM USING H2 (GM Data)

HSDI DIESEL

G-DI ENGINEPass

. Car

Ave

rage

Po

wer

Bus

/Tru

ck A

vera

ge

Pow

er

Source: Ricardo

Why hybrid vehicles?- load leveling- regenerative braking

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Transportation Sector Contribution to Oil Transportation Sector Contribution to Oil GapGap

Stacy C. Davis, ORNL, TRANSPORTATION ENERGY DATA BOOK: EDITION 21

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Ground Vehicle Energy Use Ground Vehicle Energy Use

source: EIA Annual Energy Outlook, 1998Federal Highway Administration, Highway Statistics

Actual

0

2

4

6

8

10

12

14

1970 1980 1990 2000 2010 2020

Ener

gy U

se -

Mill

ion

Barr

els

per D

ay

Class 3-8 Trucks

Class 1-2 Trucks(Pickups, Vans, SUVs)

(90% 2a, 10% 2b)

Automobiles

Projected

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EmissionEmissionEngine out vs. tail-pipe

Temperature effect?

This presentation:Engine out, hot engine only

V. Johnson, NREL

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NoxNox EmissionEmission


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PM EmissionPM Emission


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Hybrid Vehicle Control ProblemsHybrid Vehicle Control Problems

ConsumersGovernment/Green consumers

To satisfy

Test matrixDriving cyclesScenarios

Short/fast (detailed)Long/slow (simple)

Horizon/dynamics

NVH/driveability/fuel economy/emission

Fuel economy/ emission

Performance target

Robust, adaptive, ad-hoc

Optimal, intelligent

Algorithm

Low level (throttle, current, duty cycle)

High level (Power)

“Control signal”

Servo-loopMain-loop

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Power Management Problem for Parallel HEVPower Management Problem for Parallel HEV

• Goal: To develop a control strategy to minimize fuel consumption(and emission) while meeting the power demand from the driver

• Key problem: determine the total amount of power to be generated, and its split between the two power sources

• Maintain adequate battery energy (charge sustaining)

Driver

Engine

Electric Motor

ControlModule

Eng. CommandGear Command

Motor Command

Battery

TransmissionEngine

Electric Motor

ControlModuleDriver


Motor Command

Battery

Transmission

Regenerative Braking ModeRecharge Mode: When and how much power?

Engine

Electric Motor

ControlModuleDriver


Motor Command

Battery

TransmissionEngine

Electric Motor

ControlModuleDriver


Motor Command

Battery

Transmission

Hybrid Mode : When and the split?

Engine

Electric Motor

ControlModuleDriver


Motor Command

Battery

Transmission

Engine Mode : When?

Engine

Electric Motor

ControlModuleDriver


Motor Command

Battery

Transmission

Motor Mode : When?

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Preliminary RulePreliminary Rule--Based Control StrategyBased Control Strategy

Power request

Driver

Power Split Control

Charge Sustaining

Strategy

• Motor only mode• Engine only mode• Hybrid mode

Desired vehicle speed

Actual vehicle speed

Battery SOC

Recharge Control

Braking Control

• Engine provides additional power to charge the battery

• Regenerative braking

• Friction brake

0 200 400 600 800 1000 1200 1400 1600 1800 20000.5

0.55

0.6

0.65

Time (sec)

SOC

800 1000 1200 1400 1600 1800 2000 2200 2400

50

100

150

200

250

300

350

400

450

500

Engine Speed (rpm )

Eng

ine

Torq

ue (

Nm

)

Fuel Consumption (kg/kW hr)

0.270.26

0.250.24

0.23

0.22 0.21

60.214

0.21

2

0.23

0.24

0.26

0.250.27

Motor only

Power assistLow power

Motor only

Medium power Use engine

High power Both engine

& motor

the battery is charged by the engine at a constant “recharge power” level until SOC hits the max boundary

chreqengchmot PPPPP +=−= ,

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Problems of Existing (MainProblems of Existing (Main--loop) Control loop) Control ApproachApproach

Based on simple concepts (e.g., load leveling) and mostly static maps.

Tuning is done through trial-and-error.

Don’t know the full potential (is 20% improvement good?)

Not re-useable (fuel economy vs. fuel economy plus emission).

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2003 Fuel Economy Champions2003 Fuel Economy Champions

57/56

Honda Insight (Automatic)

52/4546/51Gas Mileage

(City/Highway)

Toyota Prius(Automatic)

Honda Civic (Manual)

http://www.fueleconomy.gov/feg/feg2000.htm

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OutlineOutlineIntroduction and Motivation

Simulation model and preliminary rule-based control results

Dynamic programming techniques


Simulation results

Dyno test results

Conclusions

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Vehicle/DrivelineEngine

Baseline Truck: Navistar International Baseline Truck: Navistar International 4700 Series4700 Series

V8 DI DieselTurbocharged, Intercooled7.3 litersBore: 10.44 cmStroke: 10.62 cmCompression Ratio: 17.4Rated Power: 210 HP@2400 rpm

Total Mass: 7258 KgWheelbase: 3.7 mCG Location: 2.2 m from frontFrontal Area: 5 m2

Air Drag Coefficient (CD): 0.84 Speed Automatic TransmissionRear Wheel Drive - 4x2

From www.navistar.com

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IM

Inte

r co

oler

Air

Exhaust Gas

TrnsTC

C

EMT

D

Traction Force

PS

DS

DS

ICM

Vehicle Dynamics

EngineDrivetrain

Motor

Battery

Power Control Module

Schematic of the HybridSchematic of the Hybrid--Electric TruckElectric Truck

Parallel hybrid

Post-transmission type

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Hybrid Truck ParametersHybrid Truck Parameters

Curb weight: 7504 kgVehicle

4 speed, GR: 3.45/2.24/1.41/1.0

Automatic Transmission

Power density: 350 (W/kg)

Energy density: 34 (Wh/kg)

Module number: 25

Capacity: 18Ah

Lead-acid Battery

49kWDC Motor

V6, 5.475L, 157HP/2400rpmDI Diesel Engine

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HEHE--VESIM in SimulinkVESIM in Simulink

0

slope

Double Click to Close All

Load Output Variables

Double Click to Plot Result

T wheel

Brake

Slope

w wheel

v veh

VEHICLE DYNAMICS

HEVController

Motor cmd

w motor

Current

T motor

ELECTRIC MOTOR

cyc_mph

Dring Cycle

Load Input Data

DRIVER

w eng T motorGearw shaftclutch cmd

T pump

T shaft

w motor

w transDRIVELINE

T pump

Eng cmdw eng

DIESEL ENGINE

Current soc

BATTERY

HHybrid ybrid EElectric lectric VVehicle ehicle EEngine ngine SIMSIMulationulation

Assanis, D.N. et al. “Validation and Use of SIMULINK Integrated, High Fidelity, Engine-In-Vehicle Simulation of the International Class VI Truck,”SAE Paper No. 2000-01-0288Lin, C.C., Filipi, Z.S., Wang, Y., Louca, L.S., Peng, H., Assanis, D.N., and Stein, J.L., “Integrated, Feed-Forward Hybrid Electric Vehicle Simulation in SIMULINK and its Use for Power Management Studies”, SAE Paper No. 2001-01-1334

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Preliminary RulePreliminary Rule--Based ControlBased ControlBraking rule

Power split rule

Recharge rule

0.45765.739513.159Hybrid Truck

(Preliminary Rule-Base)

0.50805.346610.343Conventional Truck

PM (g/mi)NOx (g/mi)FE (mi/gal)

800 1000 1200 1400 1600 1800 2000 2200 2400

50

100

150

200

250

300

350

400

450

500

E ngine S peed (rpm )

Eng

ine

Torq

ue (N

m)

Fue l Cons um pt ion (k g/k W hr)

0.270.26

0.250.24

0.23

0.22 0.216

0.214

0.21

2

0.23

0.24

0.26

0.250.27

Motor only

Power assist

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Dynamic programming technique


Simulation results

Dyno test results

Conclusions

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Dynamic Optimization Based Algorithm

Objective• To develop an optimal control policy that minimizes the fuel

consumption over a giving driving cycleAdvantage

• Explore the full potential of hybridization• Benchmark for a better control strategy

Methodology• A sequence of decisions applied to a dynamic system to

minimize the total fuel costs --> Multistage Decision Process• Optimal control problem

f (0) f (1) f (N-1))0(x)1(x )2(x )1( −Nx

)(Nx

)0(u

)0(L )1(L )1( −NL

)1(u )1( −Nu

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• Discrete-time Nonlinear System

Problem Formulation

))(),(()1( kukxfkx =+• Control Inputs

Fuel injection rate and gear shift command to the transmission

• Minimize the Cost Function

• Constraints

( ) ( )( ) ( )

_ _min max

_ _min max

_ min _ max

min max

( )

( ) ( ) ( )

( ), ( ) ( ) ( ), ( )

( )

e e e

e e e e e

m m m m m

k

T k T k T k

T k SOC k T k T k SOC k

SOC SOC k SOC

ω ω ω

ω ω

ω ω

≤ ≤

≤ ≤

≤ ≤

≤ ≤

( )1

0

12

0

( ), ( ) ( ( ))

= ( ) ( ) ( ) ( ( ) )

N

k

N

fk

J L x k u k G x N

fuel k NOx k PM k SOC N SOCµ υ α

−

=

−

=

= +

+ ⋅ + ⋅ + −

∑

∑

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Dynamic Programming (DP) AlgorithmDynamic Programming (DP) Algorithm

Interpolation to find 1*

+kJ

[ ]*1

( 1)( ( 1)) min ( ( 1), ( 1)) ( ( ))N

u NJ x N L x N u N G x N−

−− = − − +

* *1

( )( ( )) min ( ( ), ( )) ( ( 1)) k k

u kJ x k L x k u k J x k+ = + +

Solve recursive equation backwards in time

Obtain an optimal, time-varing, state-feedback control policy*( ( ), )u x k k

DP creates a family of optimal paths for all possible initial conditions

k 1k +

x x

*1

ˆkJ +

1kJ +

kLStep N-1:

Step k, for 10 −<≤ Nk

Stored in a table for each of the quantized states and time stages

State and control values are quantized into finite grids

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Procedure of Dynamic Optimization

Dynamic Programming

Optimal Control Law, u(x(k),k)

Simulation(Complete HE-VESIM )

Driving Cycle

Fuel Economy, Vehicle Response

Dynamic Optimization Process

Reduced order HEV model

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0 100 200 300 400 500 600 700 800 900 10000

20

40

Veh

Spd

(MP

H)

UDDSHDVActual

0 100 200 300 400 500 600 700 800 900 1000

0.54

0.56

0.58

SO

C

0 100 200 300 400 500 600 700 800 900 10000

50

100

Eng

Pw

r

0 100 200 300 400 500 600 700 800 900 1000

-200

2040

Mot

Pw

r

Time (sec)

EPA Urban Dynamometer Driving Schedule EPA Urban Dynamometer Driving Schedule for Heavyfor Heavy--Duty Vehicles (Duty Vehicles (UDDSHDV) CycleUDDSHDV) Cycle

Final SOC

Initial SOC

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Fuel Economy ComparisonFuel Economy Comparison

0.4465.62713.705DP

0.45765.739513.159Hybrid Truck(Preliminary Rule-Base)

0.50805.346610.343Conventional Truck

PM (g/mi)NOx (g/mi)FE (mi/gal)

0, 0µ υ= =

12

0

= ( ) ( ) ( ) ( ( ) )N

fk

J fuel k NOx k PM k SOC N SOCµ υ α−

=

+ ⋅ + ⋅ + − ∑

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Fuel Economy and Emission ResultsFuel Economy and Emission Results

{ }{ }

0,5,10,20,

0,100,200,400,60

40

0 800,

µ

υ

∈

∈

12

0

= ( ) ( ) ( ) ( ( ) )N

fk

J fuel k NOx k PM k SOC N SOCµ υ α−

=

+ ⋅ + ⋅ + − ∑

12.8 12.9 13 13.1 13.2 13.3 13.4 13.5 13.6 13.73.6

4

4.4

4.8

5.2

5.6

Fuel Economy (mi/gal)

NO

x Em

issi

ons

(g/m

i)

µ=[0, 5 10, 20, 40], ν=0 µ=10, ν=[0, 100, 200, 400] µ=20, ν=[0, 200, 400, 600] µ=40, ν=[0, 400, 600, 800, 1000]

µ=0ν=0

µ=5ν=0

µ=40ν=0

µ=10ν=0

µ=10ν=400

µ=20ν=600

µ=40ν=1000

3.8 4 4.2 4.4 4.6 4.8 5 5.2 5.4 5.6 5.80.37

0.4

0.43

0.46

0.49

NOx Emissions (g/mi)

PM E

mis

sion

s (g

/mi)

µ=[0, 5 10, 20, 40], ν=0 µ=10, ν=[0, 100, 200, 400] µ=20, ν=[0, 200, 400, 600] µ=40, ν=[0, 400, 600, 800, 1000]

µ=0ν=0

µ=5ν=0

µ=40ν=0

µ=40ν=1000

µ=10ν=400

µ=20ν=0

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Simulation results

Dyno test results

Conclusions

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Design Procedure by Dynamic Optimization

Preliminary Rule-based Controller

Improved Rule-based Controller

Dynamic Programming

Optimal Control Law, u(x(k),k)

Simulation(Complete HE-VESIM )

Driving Cycle

Fuel Economy, Vehicle Response

Dynamic Optimization Process

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Improved RuleImproved Rule--Based ControlBased Control

Braking rule

Power split rule

Recharge rule

Transmission shift strategy

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Improved Transmission ShiftImproved Transmission Shift

0 100 200 300 400 500 600 700 800 900 1000012345

Gea

r

β=0

0 100 200 300 400 500 600 700 800 900 1000012345

Gea

r

β=0.5

0 100 200 300 400 500 600 700 800 900 1000012345

Gea

r

Time (sec)

β=1.5

( )1

0

6 2

( 1)( ) 40 ( ) 800 ( )

5 10 ( ( )

)

)

(x x

N

k

f

J fuel k NOx k PM k

S

g

OC N S

k

C

k g

O

β−

=

= + ⋅ + ⋅ ⋅

+ ⋅ ⋅ −

+ + −∑

The original optimal gear trajectory has frequent shifting which is undesirable.

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Improved Transmission ShiftImproved Transmission Shift

0 20 40 60 80 100 120 140 160 180 200 2200

10

20

30

40

50

60

70

80

90

100

Transmission Speed (rad/s)

Eng

ine

Pow

er D

eman

d (k

W)

1st gear2nd gear3rd gear4th gear

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Improved Power Split RuleImproved Power Split Rule

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10.5

1

1.5

2

2.5

3

3.5

4

Power Demand / Trans Speed (kN-m)

Pow

er S

plit

Rat

io (P

SR)

Approximated optimal PSR curve

Optimal operating points

PSR > 1

PSR = 1

PSR < 1

eng

dem

PPSR

P=

engdemmot

demeng

PPP

PPSRP

−=

×=

0 (Motor-only mode)0 1 (Power-assist mode)

1 (Enigne-only mode)1 (Recharging mode)

PSRPSR

PSRPSR

=< <

=>

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Improved Recharge RuleImproved Recharge Rule

Rule_c1

Rule_0

Rule_d1

Rule_c2

Rule_d2

Rule_d3

Rule_c3

Rule_c4

Rule_d4

0.62

0.57

0.67

0.52

0.47

0.72

0.42

0.77

0.37

0 20 40 60 80 100 120 1400.5

1

1.5

2

2.5

3

3.5

4

4.5

Power Demand

PSR

PSR = (Eng Pwr) / (Eng Pwr+Mot Pwr)

0 20 40 60 80 100 120 1400.5

1

1.5

2

2.5

3

3.5

4

Power Demand

PSR


0 20 40 60 80 100 120 1400.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Power Demand

PSR


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Simulation results

Dyno test results

Conclusions

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UDDSHDV CycleUDDSHDV Cycle ResultsResults

739.560.39924.642213.237DP (FE & Emis)

787.09650.42924.835512.8738New Rule-Based

840.630.45765.739513.159Baseline Rule-Based

Performance Measure *

PM (g/mi)

NOx (g/mi)

FE (mi/gal)

Performance Measure: 40 800fuel NOx PM+ ⋅ + ⋅

Cycle beating?

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739.56 (-12.0%)0.3994.6413.237DP

787.1 (-6.4%)0.4294.8312.874New Rule-Based

840.630.45765.7413.159Pre Rule-Based

UDDSHDV

847.7 (-10.7%)0.446.1712.97DP

894 (-5.8%)0.4886.2712.72New Rule-Based

948.830.5097.2812.84Pre Rule-Based

WVUINTER(RDP 3)

526.7 (-21.5%)0.2592.7815.4DP

574.63 (-14.4%)0.2962.92714.58New Rule-Based

671.2250.3554.4315.31Pre Rule-Based

WVUSUB(RDP 2)

403.6 (-35.0%)0.1612.0416.63DP

480.7 (-22.6%)0.2192.4115.36New Rule-Based

621.20.3323.8716.18Pre Rule-Based

WVUCITY(RDP 1)

Weighted Cost(Fuel+40*NOx+600*PM)

PM (g/mi)

NOx(g/mi)mpg

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Simulation results

Dyno test results (Fuel Economy Only)

Conclusions

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FedEx HEV Truck ProposalFedEx HEV Truck ProposalFedEx and the Alliance for Environmental Innovation proposed to develop an environmentally progressive truck that would reduce emission by 90% and improve fuel efficiency by 50%, while work as well as FedEx’s current (1999 W700) white delivery trucks and cost about the same over the vehicle’s lifetime.

Winning design(s) that meet targets will get FedEx purchase (10-50 pre-production by mid 2003, production vehicles by 2004).

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Current FedEx Pickup/Delivery Vehicle Current FedEx Pickup/Delivery Vehicle SpecificationSpecification

• Year 1999 baseline vehicle (W700 series)

• GVWR: 16,000 lbs

• Cargo capacity: approximately 670 cubic feet, 6000 lbs

• Cummins 138 kW 6-cylinder diesel engine

• Allison 4-speed automatic transmission (AT)

(http://www.environmentaldefense.org/alliance)

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Eaton Prototype Hybrid Electric TruckEaton Prototype Hybrid Electric Truck• Same chassis and body• 125 kW 4-cylinder diesel engine• 6-speed automated manual

transmission (AMT)

EngineEngine ClutchClutch MotorMotor TransmissionTransmission

BatteryBattery

VehicleVehicle

Supervisory Controller

Enginecontroller

Transcontroller

Mot/Batterycontroller

PM TrapPM Trap

• 44 kW AC motor • Lithium-Ion battery• PM trap

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Test of Prototype Vehicle Test of Prototype Vehicle

• Modified FUDS cycle

• Tested at Southwest Research Institute (SwRI)

• Chassis dynamometer testing

• EPA standard procedure to determine fuel economy and exhaust emissions

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Fuel Economy and Emission ResultsFuel Economy and Emission Results• Hybrid Prototype #1: baseline prototype hybrid truck

• Hybrid Prototype #2: redesign control strategy (Our Dynamic Programming Design Technique) and add PM trap

24

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Fuel SavingFuel SavingRough Calculation

50000 miles/yr/truck50000 trucks in the USOriginal: 10miles/gallon

15,000,000 gallons/yrsaved by

Control algorithm improvement

arc


http://www.sierralegal.org/reports/climtorfinal.pdf

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ConclusionsConclusionsDesigning the power management strategy for HEV by learning from the Dynamic Programming (DP) results has the clear advantage of being near-optimal, accommodates multiple objectives, and systematic.

Improved rule-based control strategy can be developed by analyzing the DP results

Significant reduction in NOx and PM emissions can be achieved at the price of a small increase in fuel consumption

The new control strategy was found to be robust on different cycles

POWER MANAGEMENT STRATEGY FOR A …hpeng/COE-control-seminar-11...1 arc POWER MANAGEMENT STRATEGY FOR A PARALLEL HYBRID ELECTRIC TRUCK Chan-Chiao (Joe) Lin, Huei Peng, Jessy W. Grizzle

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