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Emerging Technologies for Electric and Hybrid Vehicles
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Energies 2017,10, 1217 Toalleviate theproblemsat thedistributionstageof thegrid—which ishighlyaffectedbyEV penetration—Gengetal.,proposedachargingstrategycomprisingof twostagesaimedatproviding satisfactorychargingforall connectedEVswhileshifting the loadsonthe transformers [120]. Thefirst stageutilizedPontryagin’sminimumprincipleandwasbasedontheconceptofdynamicaggregator; it derived theoptimal chargingpower for all theEVs in the system. The secondstageused fuzzy logic todistribute thepowercalculated in thefirst stageamongtheEVs. According to theauthors, thesystemwasfeasible tobe implementedpractically [120]. In [116],Richardsonetal., employeda linearprogrammingbasedtechniquetocalculate theoptimal rateofchargingforeachEVconnected in adistribution network to enablemaximizedpowerdelivery to the vehicleswhilemaintaining thenetwork limits. ThisapproachcanprovidehighEVpenetrationpossible inexistingresidential power systemswithnoor a littleupgrade. Sortommeet al., developedanalgorithmtomaximize profit fromEVcharging inaunidirectionalV2Gsystemwhereanaggregator ispresent tomanagethe charging[146]. Table31summarizes thealgorithmspresented in this section. Table31.Summaryof thecontrolalgorithmspresented. References AlgorithmBasedon Application Magallanetal. [176] LuGredynamic frictionmodel Drivingcontrol systeminrear-wheel-driveEV Kangetal. [177] Optimization-basedcontrolallocationstrategy Drivingcontrol systeminfour-wheel-driveEV Tahamietal. [25] Fuzzy logic Drivingcontrol systeminall-wheel-driveEV Wangetal. [178] Proportional-integral (PI) closed loopcontrol system Drivingcontrol systemin in-wheel-driveEV Nametal. [179] ExtendedKalmanfiltering(EKF)method Drivingcontrol systemin in-wheel-driveEV Zhouetal. [180] Particlefilterandmulti-modeldata fusion SOCmeasurement for lithiumpolymerbatteries Mouraetal. [175] Markovprocess Powersplitting inmid-sizedsedanPHEV Huietal. [181] Torquecontrol strategy Heavyhybridvehiclesusingasingleenergysource Chenetal. [182] Quadraticprogramming Reductionof fuel consumptionbyeffectivebatterycurrentcontrol Lietal. [183] Fuzzy logic Attainingmaximumfuelefficiencywithoutexcessdischargingofbattery Yuanetal. [184] DynamicProgrammingandPontryagin’sMinimumPrinciple Efficientenergymanagement inparallelHEV usingAutomaticManualTransmissionorAMT Bernardetal. [185] Non-causaloptimizationalgorithm Reductionofhydrogenconsumption inFCEV Gengetal. [174] Equivalentconsumptionminimizationstrategy(ECMS) Energymanagement inPHEVemploying microturbine Suetal. [186] Estimationofdistribution(EDA)algorithm Intelligentchargingof largefleetofPHEVsdockedatamunicipalchargingstation Deilamietal. [140] Maximumsensitivitiesselection(MSS)optimization Loadmanagementsystemfor intelligent charging Mohamedetal. [187] Fuzzycontroller V2GsystemforEVchargingparksincorporatingrenewablegeneration Gengetal. [120] Pontryagin’sminimumprinciple, fuzzy logic LoadshiftingwhilechargingEVs in thedistributionnetwork Richardsonetal. [116] Linearprogramming EnablinghighEVpenetration inexistingresidentialpowersystemnetwork Sortommeetal [146] Preferredoperatingpoint (POP)algorithm Maximizingprofit fromEVchargingthroughanaggregator 68
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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