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Emerging Technologies for Electric and Hybrid Vehicles
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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
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Emerging Technologies for Electric and Hybrid Vehicles
Titel
Emerging Technologies for Electric and Hybrid Vehicles
Herausgeber
MDPI
Ort
Basel
Datum
2017
Sprache
englisch
Lizenz
CC BY-NC-ND 4.0
ISBN
978-3-03897-191-7
Abmessungen
17.0 x 24.4 cm
Seiten
376
Schlagwörter
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)
Kategorie
Technik
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Emerging Technologies for Electric and Hybrid Vehicles