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Energies 2017,10, 437
Vehicle specification
Engine Max power 115 kW
Max torque 185 Nm
MG1 Max power 70 kW
Max torque 50 Nm
MG2 Max power 90 kW
Max torque 270 Nm
Battery Max power 50 kW
Capacity 25Ah
Vehicle Mass 1800 kg
Tire radius 0.32 m
Gear ratio PG 2.6
G1-G2 2.478
G3-G4 1.0
/)
'PIKPG
S
C
R
PG B2L B2R
B1
B3L G3
G4
CL1
BK3
BK2
BK1 /)
*&%$CVVGT[
G1
G2
Output
Figure10.Reference topologyandspecificationswithdrivetrainelementsandadditionalclutchand
brakes. MG:motorandgenerator,CL#: clutch,PG#: planetarygear,B#L:bearingon left side,B#R:
bearingonrightside,HDC:highvoltageDC/DCconverter,BK#: brake,G#:gear.
Table3.Powerelectronics (PE)anddrivetrain loss foreachoperatingmode.
OperatingMode EV#1 EV#2 PowerSplit Parallel Series
PEloss Battery,HDC,MG2 Battery,
HDC,MG2,
MG1 Battery,
HDC,MG2,
MG1 Battery,
HDC,MG2 Battery,HDC,
MG2,MG1
Drivetrain
loss Loaded Gear,BRG Gear,BRG,PG Gear,BRG,
PG Gear,BRG,
PG Gear,BRG,PG
Un-loaded BRG,Churning,
MG1unloaded,
BK# BRG,
Churning,
BK#,Pump BRG,
Churning,
BK# BRG,
Churning,
BK#,Pump BRG,Churning,
BK#,CL1,Pump
4.BackwardSimulatorUsingDynamicProgramming
To evaluate themaximumpotential in the fuel economyof the eight candidates in Figure 7,
abackwardsimulatorwasdevelopedusingdynamicprogramming(DP).SinceDPisable tofindthe
optimalSOCtrajectoryregardlessofthecontrolstrategy,whichguaranteesminimumfuelconsumption
for thegivenPHEVconfiguration[31,32], itwasusedfor thecomparativeanalysisof thecandidates
for thepresenceorabsenceof thedrivetrain losses.
ThePHEVsysteminFigure7has twocontrolvariables, enginespeedandtorque,andonestate
variable,batterySOC[33]. Foreachtimestep,k−1, the instantaneousoptimaloperatingpointof the
engine isdeterminedfor thespecificbatterypower.Whentheoperatingpointof theengine isgiven,
thePElossanddrivetrain lossarecalculated[22].Consideringthecomponent loss, theoptimal fuel
consumptionrate,gk−1, isobtainedthroughthe instantaneousoptimizationprocess.
After the process is completed, global optimization is performed to find theminimum fuel
consumptionover thewholedrivingcycle. Theglobaloptimizationprocesscanberepresentedasa
recursiveequation[33,34]. Therecursiveequationandconstraintarerepresentedas
Recursiveequation : Jk∗(SOCk)={gk−1(ωek−1,Tek−1)+ Jk−1∗(SOCk−1)}
Constraint : SOCinitial−SOCfinal=0, (8)
wherek is thediscrete timestep, Jk∗ is theminimumfuelconsumptionfrom1tokstep, Jk−1∗ is the
minimumfuel consumption from1 to k− 1 step,ωek−1 andTek−1 are respectively the speedand
torqueof theengine thathas theminimumfuelconsumptionrateat thek−1step,andgk−1 is the fuel
consumptionrateat thek−1step.
274
Emerging Technologies for Electric and Hybrid Vehicles
- Title
- Emerging Technologies for Electric and Hybrid Vehicles
- Editor
- MDPI
- Location
- Basel
- Date
- 2017
- Language
- English
- License
- CC BY-NC-ND 4.0
- ISBN
- 978-3-03897-191-7
- Size
- 17.0 x 24.4 cm
- Pages
- 376
- Keywords
- electric vehicle, plug-in hybrid electric vehicle (PHEV), energy sources, energy management strategy, energy-storage system, charging technologies, control algorithms, battery, operating scenario, wireless power transfer (WPT)
- Category
- Technik