Seite - 159 - in Emerging Technologies for Electric and Hybrid Vehicles
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Energies 2017,10, 5
modelexpresses the fundamentalelectrochemical reactionsbycomplexnonlinearpartialdifferential
algebraicequations(PDAEs) [8]. It canaccuratelycapturethecharacteristicsof thebattery,butrequires
extensive computational power to obtain the solutions of the equations. Hence, suchmodels are
suitable for thebatterydesignrather thanthesystemlevel simulation. Incontrast, theECMabstracts
awaythedetailed internalelectrochemical reactionsandcharacterizes themsolelybysimpleelectrical
components; thus, it is ideal for circuit simulation software and implementation in embedded
microcontrollers. Theaccuracyof theECMishighlydependenton themodel structureandmodel
parameters. Theoretically, a higher order ECM can represent awider bandwidth of the battery
application and can generatemore accurate voltage estimation results. However, the high order
ECMcannot only increase the computational burden, but also reduce the numerical stability for
the furtherbatterystates’estimation[9,10].Hence, consideringatradeoffamongthemodelfidelity,
the computational burden and thenumerical stability, the secondorderECMis employed in this
paper [11–18]. Thecommonstructureof thesecondorderECMis illustrated inthetopsubfigureof
Figure1,where theopencircuitvoltage (OCV),which isa functionof stateof charge (SoC), stands
for theopencircuitvoltage,Rin is the internal resistance,whichrepresents theconductionandcharge
transfer processes [19–21], and two resistor-capacitor (RC) networks approximately describe the
diffusion process. Among them, the short-termRCnetworkmodels the fast dynamics diffusion
process (PartA in thebottomsubfigureofFigure1), and the long-termRCnetworkrepresents the
slowdynamics diffusion process (Part B in the bottom subfigure of Figure 1). The abovemodel
parameters canbe identified either through the time-domainor the frequency-domainparameter
extractionexperiments. For the time-domainparameterestimationmethods,modelparametersare
usually identified throughfitting the voltage response from theparameter extraction experiment
withtheexponential-basedfunctions. Theelectrochemical impedancespectroscopy(EIS) test is the
commonly-usedfrequency-domainparameterextractionexperiment.Comparedto the time-domain
testprocess,one limitationof theEIS test is that theamplitudeof thecurrentexcitation isso lowthat
the battery canbe consideredas equalizedduring thewhole test process,which seldomhappens
inHEV/EV applications. In order to overcome the above drawback, references [22–24] propose
superimposingthedirect current (DC)offsetover theEISsignals todetermine thecurrentdependency
of impedanceparameters. However, since significant time is required for theEIS test, the battery
SoC changes significantlyduring the test procedure if the amplitudeof the superimposed current
is improper. This can reduce theparameter estimationaccuracyandmake thismethodpractically
notapplicableatmoderateandhighcurrent rates [25,26]. Basedontheaforementionedanalysis, the
secondorderECMwithparametersestimatedbythe time-domainanalysis isdiscussed in thispaper.
159
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