锂电池健康评估和寿命预测

36120151ChineseJournalofScientificInstrumentVol.36No.1Jan．20152014-08ＲeceivedDate2014-08*61301205、20112302120027、9140A17050114HT01054、HIT．NSＲIF2014017*150080。、。。TP206+．3A510．4030Surveyonlithium-ionbatteryhealthassessmentandcyclelifeestimationLiuDatongZhouJianbaoGuoLimengPengYuSchoolofElectricalEngineeringandAutomationHarbinInstituteofTechnologyHarbin150080ChinaAbstractAslithium-ionbatteryiswidelyappliedthehealthassessmentandremainingcyclelifeestimationoflithi-um-ionbatterygraduallybecomeachallengeandresearchhotspotsinmanyfields．Inthispaperthecurrentre-searchstatusofthehealthmanagementandcyclelifepredictionisanalyzedtheremainingcyclelifepredictionap-proachesoflithium-ionbatteryaresummarized．InthissurveywemainlyanalyzetherelatedtopicsofremainingusefullifeestimationincludingbatterymanagementsystemBMSbatterydegradationidentificationcyclelifees-timation．Finallyacasestudyforaerospaceapplicationisdescribedtobrieflyinstantiatetherecentlydevelopedtechnology．Bysummarizingthepreliminaryresearchrelatedworkthefuturedevelopingtrendandchallengearean-alyzed．Keywordslithium-ionbatterybatterymanagementsystemremainingusefullifeestimationPHM1、、、、、Prius、Volt、LeafE6。、、、、1NASA、、ＲOSETTA。21、、、2362、。3。/、。3。4-6。1999AFＲL2013787。、。、batterymanagementsystemBMS。7-8910、2BMS11。state-of-chargeSOCstate-of-healthSOH、。、。、。2BMS2．1BMSBMS、、、、。BMS212。BMS、、、、、、。1、BMS2BMS345、67BMS8、、、。BMS、3BMS。2．2SOCSOHBMS。SOC。SOC、、。SOC。912-13SOC、、、、、、、、、supportvectormachineSVMKalmanfilterKF、、。SOH。SOH。SOH3、KF、SOH212SOH。2．3BMSBMS13、、BMS。BMS//、、、、。BMS、、、、、、、、。、BMS、、、、。、、re-mainingusefullifeＲUL、prognosticsandhealthmanagementPHM。ＲUL。、ＲUL。3、314。。ＲUL15。ＲUL11。、11。。ＲUL12、ＲUL。1ＲULFig．1SchematicdiagramofＲULpredictionforlithium-ionbattery3．1、。。healthindexHIＲUL。、16-17。18-19SOH。3．2。、。、、、。1、、、、、。2021SEI2220SEIFuller’s23436。2、、。242526。Moo27SOH。28—Ah-VSOH。SOH。3。SOH。29、、KFSOH。。19SOCDEKFSOH。30SOHDEKFSOH。electrochemicalimpedancespectros-copyEIS、ω。EIS、Warburg。31EIS。32EIS。33EIS。NASA34-35。Zenati36SOH。37、、SOH3。16SOHSOH。EIS2138-40。4Kim41EKFSOH。42DEKFSOH。43EKFSOCSOCSOH。SOH。。Widodo44HIWilliard45HISOHLiu46TIEDVDHIHI。1、。2。。。。3。EISEIS。。15、、。4ＲULＲUL15。ＲUL12。ＲUL11。、47。、、48。ＲULＲUL15。1ＲULＲUL。ＲUL、49。4．1ＲUL、ＲUL50。、、。ＲUL、。1、、、、SOC、、11。、、。、SEI、。202151SEI525322℃EIS54、SEI55565758、、959、、60-63、1460-6164-65。ＲUL、12。6650。。2。6712636。Ｒint、ＲC68。PNGV69、CPE、Tanh707170。ＲC72ＲCＲC。ＲC、73ＲC74。75。76-77。、78。、。3。。3479、80。ＲUL。EIS81-8324。84、2585、86。ＲULrelevancevectormachineＲVM25、85ＲULＲUL。。particlefilterPF24347981-828487、PFunscentedparticlefilterUPF82、PFsequentialimportanceresamplingPFSIＲ-PFPFauxiliarysequentialimportancere-samplingPFASIＲ-PF86、KF80、EKF83、KFun-scentedKFUKF、dualEKFDEKF88PFKF。PF89-90PF。。PFＲUL、。、。PF。PFEKF。4．2ＲUL87。、、、ＲUL。。1AＲAＲAＲ。AＲ。AＲMAMAAＲ17AＲ、。AＲ91。AＲ/AＲMAautoregressiveconditionalheteroscedasticAＲCH、generalizedautore-gressiveconditionalheterosecdasticGAＲCH、thresholdautoregressiveTAＲ、bi-linearmodels92。ＲULSaha93AＲMA。AＲMA87AＲnonlineardegrada-tionAＲND-AＲ。94AＲAＲＲUL。AＲ、。AＲ。2artificialneuralnetworksANN、95。ANNＲUL。2003ANNＲUL96ANNＲULANN97、ANN98ANN98。ANN。Liu99recursivelevenberg-marquardtＲLMadaptiverecurrentneuralnetworkAＲNNＲE、ＲCTTANNＲUL。Ｒezvani100ＲUL。ANN101structuredneuralnetworkSNN、、SOCSNNSNNＲUL。SNN。ＲULJon35ANNSigmoidＲufus102ANNconfidencepredictionneuralnetworkCPNN。ANN1ANNANN。echostatenetworksESNLiu46ESNＲULESN。2ANN。3ANNＲUL102ANNＲUL。4ANN。3supportvectormachineSVMVC103。SVMANN、、836。SVMANN。SVMLS-SVM。SVMSOC104-107SOH107。ＲULSVMＲULSaha108EISＲVMWidodo44、SVMNuhic109SVMFisher、ＲUL。SVMSVMSVM102Pattipati16ＲandlesL-HPPC、EISSVMＲUL。SVMPattipati110hiddenmarkovmodelHMMSVMＲULSVMHMMSVM。Ｒufus102SVMＲUL。SVMMercer、、、SVM。4SVMrelevancevectormachineＲVM111、、SVMＲVM。ＲVM。ＲVM9ＲUL3489-90。ＲVMＲUL44ＲVMＲULSVM112ＲVMＲUL。ＲVMＲULＲVMPFＲUL3493ＲVMＲVMPFＲVMＲUL。Pecht25ＲVM95%ＲUL。ＲVMSVM。ＲVMＲVMＲUL。5gaussianprocessregressionGPＲ。GPＲＲUL。Saha5ＲE+ＲCTGPＲＲE+ＲCTＲUL。26ＲUL。GPＲ。4．3ＲULＲUL。19113。ＲULPFSaha93ＲVM-PFPFEISＲVMPFＲVMＲVMＲUL。108ＲVMPFＲVMPF。87PFAＲPFＲULPF。96ANN、、AＲMAＲUL。114ＲVM、SVM、ANN。、。4．4ＲULＲUL1、、。2。3ＲUL、。、、。5、、、BMSBMS、。14667876521012BMS、、。、、。。5．146BMS。BMS、、、、115。、、、、、。BMSＲUL。1。2ＲUL。ＲUL1ＲUL1036。2。5．21PF、GPＲＲVMＲVM、ＲVMＲUL。2FPGAASIC。reconfigurablecomputingＲCＲULFPGA、。FPGA。FPGA。115FPGA2。2ＲULFig．2Frameworkoflithium-ionbatteryＲULpredictionmethodforaerospaceplatform、116。。ＲUL。5．3BMSFPGA。BMS3。3BMSFig．3ArchitectureblockdiagramofintelligentBMSplatform6、、。、、。1、23456/、78910。、111BMS、、、BMS、、。1．J．200630170-73．ANXYTANLSH．Developmentoflithium-ionbatter-iesasnewpowersourcesforspaceapplicationJ．Chi-neseJournalofPowerSources200630170-73．2LULHANXLIJetal．Areviewonthekeyissuesforlithium-ionbatterymanagementinelectricvehiclesJ．JournalofPowerSources2013226272-288．3STUAＲTTFANGFWANGXetal．Amodularbat-terymanagementsystemforHEVsC．InProceedingsoftheSAEFutureCarCongressArlingtonVAUSA20021-9．4．M．2009．TANWCH．SpacecraftsystemsengineeringM．Bei-jingSciencePress2009．5GOEBELKSAHABSAXENAAetal．Christophers-enprognosticsinbatteryhealthmanagementJ．IEEEInstrumentationandMeasurementMagazine200811433-40．6WILLIAＲDNHEWHENDＲICKSCetal．Lessonslearnedfromthe787dreamlinerissueonlithium-ionbat-teryreliabilityJ．Energies2013694682-469