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Energies 2017,10, 1217
control systemssuchas tractioncontrol, cruisecontrol, anddifferentdrivingmodeshavebeenbeing
applied inconventionalvehicles fora longtime.Applicationofsuchsystemsappearedmoreefficient
inEVsas thedrivingforcesofEVscanbecontrolledwithmoreease,with lessconversionrequired
in-between themechanical and the electrical domains. In anycondition, forces act onavehicle at
different directions; for a driving control system, if is essential to perfectly perceive these forces,
alongwith other sensory inputs, andprovide torques to thewheels tomaintain desired stability.
InFigure65, the forces indifferentdirectionactingoneachwheelofacar is showninahorizontal
plane. In [176],Magallan et al., proposedandsimulateda control system toutilize themaximum
torque ina rear-wheel-driveEVwithout causing the tires to skid. Themodel theyworkedonhad
independent driving systems for the two rearwheels. A slidingmode system, based on aLuGre
dynamic frictionmodel,wasusedtoestimate thevehicle’svelocityandwheel sliponunknownroad
surfaces. Utilizing thesedata, the control algorithmdetermined themaximumallowable traction
force,whichwasappliedto theroadbytorquecontrollingof the tworearmotors. JuyongKangetal.,
presentedanalgorithmaimedatdriving control systems for four-wheel-driveEVs in [177]. Their
vehiclemodelhadtwomotorsdrivingthe frontandtherearshafts. Thealgorithmhadthreeparts: a
supervisory level fordetermine thedesirabledynamicsandcontrolmode,anupper levelcomputing
theyawmoment and traction force inputs, anda lower level determining themotor andbraking
commands. Thissystemproveduseful forenhancing lateral stability,maneuverability,andreducing
rollover. Figure66showstheactingcomponentsof this systemonavehiclemodelwhileFigure67
showsadetaileddiagramof thesystemwith the inputs, controller levels, andactuators. Tahamietal.,
introducedastability systemfordrivingassistance for all-wheeldriveEVs in [25]. They traineda
neuralnetworktoproduceareferenceyawrate.Afuzzy logiccontrollerdictated independentwheel
torques;asimilarcontrollerwasusedforcontrollingwheel slip. ThissystemisshowninFigure68.
In [178],Wanget al., showedasystemtoassist steeringusingdifferentialdrive for in-wheeldrive
system.Aproportional integral (PI) closed loopcontrol systemwasusedhere tomonitor thereference
steeringposition. Itwas achievedbydistributing torqueat the frontwheels. Direct yawmoment
control and traction controlwerealso employed tomake thedifferentialdrive systembetter. This
approachmaintainedthe lateral stabilityof thevehicle, andimprovedstabilityathighspeeds. The
structureof this systemisshowninFigure69. Inaseparate studyconductedbyNametal., lateral
stabilityofanin-wheeldriveEVwasattainedbyestimatingthesideslipangleof thevehicleemploying
sensors tomeasure lateral tire forces [179]. In this study,a stateobserverwasproposedwhichwas
derived fromextended-Kalman-filtering (EKF)methodandwasevaluatedby implementing inan
experimentalEValongsideMatlab/Simulink-Carsimsimulations.
Energymanagement isabig issue forEVs. Propermeasurementof theavailableenergy iscrucial
forcalculatingtherangeandplans thedrivingstrategythereafter. Forvehicleswithmultipleenergy
sources (e.g.,HEVs), efficient energymanagement algorithmsare required tomakeproperuseof
the energy on-board. Zhou et al., proposed a battery state-of-charge (SOC)measuring algorithm
for lithiumpolymerbatterieswhichmadeuseof a combinationofparticle filter andmulti-model
data fusion technique toproduce results real timeand isnot affectedbymeasurementnoise [180].
Theyuseddifferentbatterymodelsandpresentedthe tuningstrategies foreachmodelaswell. Their
multi-modelapproachprovedtobemoreeffective thansinglemodelmethods forprovidingreal time
results.Workingprincipleof this systemisshowninFigure70.Mouraetal., exploredefficientways
to split powerdemandamongdifferentpower sourcesofmid-sized sedanPHEVs in [175],which
canbeusedforothervehicleconfigurationsaswell. Theirmethodmadeuseofdifferentdrivecycles,
rather thanusing a single one; assessed thepotential of depleting charge in a controlledmanner;
andconsideredrelativepricingof fuelandelectricity tooptimallymanagethepowerof thevehicle.
In[181],Huietal.,presentedanovelhybridvehicleusingparallelhybridarchitecturewhichemployed
ahydraulic/electric synergyconfigurationtomitigate thedrawbacks facedbyheavyhybridvehicles
usingasingleenergysource. Transitionamong theoperatingmodesof suchavehicle is shownin
Figure71. Theydevelopedanalgorithmtooptimize thekeyparametersandadopteda logic threshold
62
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