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一种改进的VRLA电池SOC估算方法.docx

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一种改进的VRLA电池SOC估算方法 Title:AnImprovedSOCEstimationMethodforVRLABatteries Abstract: StateofCharge(SOC)estimationisacrucialaspectofbatterymanagementsystemsforoptimizingtheperformanceandextendingthelifespanofValveRegulatedLeadAcid(VRLA)batteries.However,accuratelyestimatingtheSOCofVRLAbatteriesisachallengingtaskduetotheiruniquecharacteristics.ThispaperproposesanimprovedSOCestimationmethodforVRLAbatteriesbyincorporatingadvancedtechniquesandalgorithms.TheproposedmethodenhancestheaccuracyandreliabilityofSOCestimation,leadingtomoreefficientbatterymanagementsystems. 1.Introduction VRLAbatteriesarewidelyusedinvariousapplicationssuchastelecommunications,uninterruptiblepowersupplies(UPS),andelectricvehicles.AccurateSOCestimationisnecessaryforpredictingbatteryperformance,preventingoverchargingorover-discharging,andmaximizingbatterylifespan.TheexistingSOCestimationmethodsforVRLAbatterieshavelimitationsintermsofaccuracyandreliability.Therefore,amoreadvancedandimprovedmethodisrequired. 2.VRLABatteryCharacteristics UnderstandingtheuniquecharacteristicsofVRLAbatteriesiscrucialfordevelopinganaccurateSOCestimationmethod.Thissectiondiscussesthemaincharacteristicsincludingvoltagehysteresis,non-linearbehavior,andcapacityfade. 3.LiteratureReview ThissectionprovidesanoverviewoftheexistingSOCestimationmethodsforVRLAbatteries,includingCoulombcounting,model-basedmethods,andmachinelearningalgorithms.Theiradvantagesandlimitationsarediscussed,highlightingtheneedforanimprovedmethod. 4.ImprovedSOCEstimationMethod TheproposedmethodcombinestheadvantagesofCoulombcounting,model-basedmethods,andmachinelearningalgorithmstoaddressthelimitationsofexistingmethods.Itutilizesanadaptivefilteringtechniquetoenhanceaccuracyandreliability.Thedetailedstepsoftheproposedmethod,includingdataacquisition,preprocessing,featureextraction,andSOCestimation,arepresented. 5.ExperimentalValidation TovalidatetheeffectivenessoftheproposedSOCestimationmethod,experimentsareconductedusingaVRLAbatteryprototype.Theexperimentalsetup,da