Seite - 163 - in Emerging Technologies for Electric and Hybrid Vehicles
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Energies 2017,10, 5
where VRC,short(0) and VRC,long(0) are the initial voltages of corresponding RC networks and
τshort = RshortCshort, τlong = RlongClong, which represent the short-term and the long-term time
constants, respectively.
SubstitutingEquation(5) intoEquation(2), theoutputequation is rewrittenas:
Vt(t)=OCV(SoC)+ IRin+VRC,short(0)e − tτshort +VRC,short(0)e − tτlong + IRshort(1−e− t
τshort)+ IRlong(1−e − tτlong) (6)
Duringtherestperiod,wherethereisnocurrentexcitation(I=0),Equation(6)canbesimplifiedto:
Vt(t)=OCV(SoC)+VRC,short(0)e − tτshort +VRC,long(0)e − tτlong (7)
With the knowledge of Rin and charging/discharging OCV-SoC relationships, RC network
parameters (Rshort,Cshort,Rlong andClong) can be obtained through fitting the experimental data
withrelevantexponential functions,as
⎧⎨
⎩ y= IRshort(1−e − tτshort)+ IRlong(1−e − tτlong) I =0
y=VRC,short(0)e − tτshort +VRC,long(0)e − tτlong I=0 (8)
where y = Vt − OCV(SoC) − IRin. Since there only exists 2% SoC variation during each
pulse-charging/discharging period, it is reasonable tomake an assumption that theRCnetwork
parameterskeepconstantduringthisperiod. Inaddition, consideringthat thebatteryhasconverged
to thesteadystateaftera2-hrest,VRC,short(0)andVRC,long(0)aresetaszeroat thebeginningof the
pulse-charging/dischargingperiod.
Basedon theaboveanalysis, theRCnetworkparameters canbeestimated throughfitting the
experimentaldatasetwithEquation(8). Thecost functionof thecurvefittingmethod J is tominimize
the sumof squarederrors between the estimation results and themeasureddata, subjected to the
followingconstraints: ⎧⎨
⎩ J=minr,τ n
∑
k=1 [Vmt (tk)−Vet (r,τ,tk)]2
s.t. Rshort, τshort, Rlong, τlong >0 (9)
where tk is the input timesequence,n is the lengthof thefittedexperimentaldataset, r=[Rshort,Rlong],
τ= [τshort,τlong],Vet is themodel estimatedvoltageandV m
t is thevoltagemeasurements fromthe
pulse-rest test.
3.RCNetworkParametersEstimation
BasedontheIntroduction inSection1, theRCnetworkparametersshowdiversevaluesunder
differentoperatingscenarios. InHEV/EVapplications,batteriesusuallywork in twotypical scenarios:
theCCchargingscenarioandthedynamicdrivingscenario. In theCCchargingscenario, continuous
externalchargingcurrentsareappliedtothebatteries,andthetransportof ions ismainlydrivenbythe
electricfield.While for thedynamicaldrivingscenario, especially for theurbandrivingcondition, the
loadcurrenthas thecharacteristicsofdiscontinuousamplitudevaluesandawide-spreadfrequency
spectrum. Inthiscase,besides theelectricfield, thegradient inconcentrationisalso largelyresponsible
for the transport of ionswithinbatteries [45]. Therefore, theRCnetworkparameters employed in
differentoperatingscenariosshouldbe identifiedthroughdifferent identificationapproaches.
3.1. RCNetworkParameters for theCCChargingScenario
Thepolarizationvoltage(VP) isadoptedtoillustratethevariationofRCnetworkparametersunder
theCCexcitation.Accordingto theaforementionedbatteryoutputequation,VP canbeobtainedas:
VP=VRC,short+VRC,long=Vt−OCV(SoC)− IRin (10)
163
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