POWER MANAGEMENT STRATEGY FOR A …hpeng/COE-control-seminar-11...1 arc POWER MANAGEMENT STRATEGY...
Transcript of POWER MANAGEMENT STRATEGY FOR A …hpeng/COE-control-seminar-11...1 arc POWER MANAGEMENT STRATEGY...
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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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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
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DI H
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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
Stacy C. Davis, ORNL, TRANSPORTATION ENERGY DATA BOOK: EDITION 21
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PM EmissionPM Emission
Stacy C. Davis, ORNL, TRANSPORTATION ENERGY DATA BOOK: EDITION 21
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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
Eng. CommandGear Command
Motor Command
Battery
Transmission
Regenerative Braking ModeRecharge Mode: When and how much power?
Engine
Electric Motor
ControlModuleDriver
Eng. CommandGear Command
Motor Command
Battery
TransmissionEngine
Electric Motor
ControlModuleDriver
Eng. CommandGear Command
Motor Command
Battery
Transmission
Hybrid Mode : When and the split?
Engine
Electric Motor
ControlModuleDriver
Eng. CommandGear Command
Motor Command
Battery
Transmission
Engine Mode : When?
Engine
Electric Motor
ControlModuleDriver
Eng. CommandGear Command
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
Improved rule-based control law
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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OutlineOutlineIntroduction and Motivation
Simulation model and preliminary rule-based control results
Dynamic programming technique
Improved rule-based control law
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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OutlineOutlineIntroduction and Motivation
Simulation model and preliminary rule-based control results
Dynamic programming technique
Improved rule-based control law
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
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
4.5
5
Power Demand
PSR
PSR = (Eng Pwr) / (Eng Pwr+Mot Pwr)
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OutlineOutlineIntroduction and Motivation
Simulation model and preliminary rule-based control results
Dynamic programming technique
Improved rule-based control law
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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COE Control Seminar-11/01-2002- 40
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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OutlineOutlineIntroduction and Motivation
Simulation model and preliminary rule-based control results
Dynamic programming technique
Improved rule-based control law
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
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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
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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