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基于模糊卡尔曼滤波器的锂电池荷电状态与健康状态预测（英文）.docx

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基于模糊卡尔曼滤波器的锂电池荷电状态与健康状态预测（英文） Title:LithiumBatteryStateofChargeandHealthPredictionbasedonFuzzyKalmanFilter Abstract: Aslithium-ionbatteriesarewidelyusedinvariousapplicationsrangingfromportableelectronicstoelectricvehicles,accuratepredictionoftheirstateofcharge(SOC)andhealthiscrucialforensuringtheiroptimalperformanceandlongevity.ThispaperproposesanovelapproachforpredictingtheSOCandhealthoflithiumbatteriesbasedontheFuzzyKalmanFilter(FKF)algorithm.ByintegratingfuzzylogicandtheKalmanfilter,theFKFalgorithmisabletohandleuncertaintiesandnon-linearitycommonlyencounteredinbatterysystems.TheFKFalgorithmisimplementedandvalidatedusingreal-worldbatterydatasets,demonstratingitseffectivenessinaccuratelypredictingtheSOCandhealthoflithiumbatteries. 1.Introduction 1.1Background Lithium-ionbatterieshavebecomethepreferredchoiceforenergystorageinvariousapplicationsduetotheirhighenergydensity,longcyclelife,andenvironmentalfriendliness.However,theirperformanceandlongevityaresignificantlyinfluencedbytheirSOCandhealthconditions.Therefore,accuratepredictionoftheSOCandhealthoflithiumbatteriesisessentialforoptimizingtheirutilizationandextendingtheirlifespan. 1.2Objectives TheobjectiveofthisstudyistodevelopapredictionmodelforlithiumbatterySOCandhealthbasedontheFKFalgorithm.TheFKFalgorithmtakesintoaccounttheuncertaintiesandnon-linearitiesinherentinbatterysystems,providingamoreaccurateandreliablepredictioncomparedtotraditionalmethods. 2.LiteratureReview 2.1StateofCharge(SOC)Estimation SeveralmethodshavebeenproposedforSOCestimation,includingopen-circuitvoltage(OCV),coulombcounting,model-basedmethods,andKalmanfilter-basedmethods.Eachmethodhasitsadvantagesandlimitations,andacombinationofthesemethodsmayprovidemoreaccurateSOCestimation. 2.2BatteryHealthPrediction Batteryhealthpredictionisachallengingtaskduetothecomplexelectrochemicalreactionsandphysicaldegradationmechanismsinvolved.Variousapproacheshavebeenproposed,includingelectrochemicalimpedancespectroscopy(EIS),equivalentcircuitmodels,andmachinele