文档名：基于等效电路模型和数据驱动模型融合的SOC和SOH联合估计方法
摘要：针对电池SOC与SOH估计结果相互影响,单独估计准确度不高的问题,该文提出了一种基于等效电路模型和数据驱动模型融合的SOC和SOH联合估计方法.通过构建考虑老化和SOC的电池二阶RC等效电路模型,采用带遗忘因子的递推最小二乘法,在不同SOC和SOH的情况下,对电池的参数进行在线辨识,实现电池参数在线辨识与电池SOC和SOH估计的耦合.以锂离子电池自SOC=20%到恒流充电阶段结束所需时间为输入,电池SOH值为输出,训练GPR模型,实现电池SOH估计.将输出的SOH估计值与电池的额定容量相乘,得到电池的实际容量,更新二阶RC状态空间方程,采用扩展卡尔曼滤波算法对电池进行SOC估计,实现电池SOH估计和SOC估计之间的联合.采用牛津大学电池退化数据集和NASA随机使用电池数据集进行算法验证,结果表明,所提联合估计方法能够在电池的生命周期内较准确地跟随锂离子电池SOC和SOH的真实值.
Abstract：Theaccuracyofstateofcharge(SOC)canbesignificantlyaffectedbybatteryaging,leadingtomisguidanceinthecalibrationofstateofhealth(SOH).ExistingstudiesoftenestimateSOCandSOHseparately,neglectingtheircloserelationshipandresultinginreducedestimationaccuracy.ThispaperproposesajointestimationmethodforSOCandSOHbasedonthefusionofanequivalentcircuitmodelandadata-drivenmodel.TheinfluencemechanismbetweenbatterySOCandSOHisrevealed,mitigatingtheirmutualinfluenceandenhancingtheaccuracyofSOCandSOHestimation.Firstly,byconstructingasecond-orderRCequivalentcircuitmodelofthebatteryconsideringagingandSOC,therecursiveleastsquaremethodwithaforgettingfactorisusedtoidentifybatteryparametersonlineunderdifferentSOCandSOHconditions.Secondly,therequiredtimefrom20%SOCtotheendoftheconstant-currentchargingstageisextracted.PearsonandSpearmanrelationshipsbetweenconstantcurrentchargetimeandSOHoflithium-ionbatteriesarecalculated.Thirdly,theactualtimerequiredfrom20%SOCtotheendoftheconstant-currentchargingphaseoflithium-ionbatteriesistakenasinputandbatterySOHasoutputtotraintheGPRmodeloffline.ThetrainedGPRmodelisoptimizedbyhyperparametersandusedforSOHprediction.Finally,theestimatedSOHoutputismultipliedbytheratedcapacityofthecelltoobtaintheactualcellcapacity,whichisusedtoupdatethesecond-orderRCstatespaceequation.Basedonthesecond-orderRCequivalentcircuitmodel,thebatterySOCwasestimatedbytheEKF.TheOxfordUniversitybatterydegradationdatasetandNASArandombatterydatasetareusedtoverifythejointestimationmethod.TheresultsshowthattheproposedmethodachieveslowaverageMAEandRMSEforSOCestimation(typicallylessthan0.04).InagingexperimentsofCell1～Cell8andRW3～RW6underdifferentworkingconditions,theaverageMAEandaverageRMSEarestable.TheactualinitialSOCvalueis1,andtheinitialvalueissetto0.7inthispaper.Withthedeclineinbatterycapacity,thejointestimateofbatterySOCcanfollowtheactualSOCmoreaccurately.Thejointestimationalgorithmisrobustandaccurate.Meanwhile,thereservation-onemethodisusedtoverifytheGaussianprocessregressionmodel.TheMAEandRMSEpredictedbySOHforCell1～Cell8arelessthan0.5%,andtheMAEandRMSEpredictedbySOHforRW3～RW6areabout0.05.AllthepredictedSOHvaluesareinanarrowconfidenceinterval.Thefollowingconclusionscanbedrawnfromthesimulationanalysis:(1)Comparedwiththeexistingbatterymodel,thedynamicsecond-orderRCequivalentcircuitmodelconsideringbatteryagingandSOCisconstructed.Inthecaseofbatteryaging,thevoltageobtainedbyfittingtheidentifiedcircuitparameterscantracktheactualvoltagewell.(2)Thejointestimationmethodappliesthereal-timeonlinemodifiedbatteryparametersandbatterySOHtoensurethatthebatterySOCisadjustedwithbatteryaging.TheSOCestimationisaccurate.(3)ThecombinedmethodappliestheestimatedSOCtoensureeffectivehealthfeatureextractionandimprovetheaccuracyofSOHprediction.
作者：刘萍 李泽文 蔡雨思 王文 夏向阳Author：LiuPing LiZewen CaiYusi WangWen XiaXiangyang
作者单位：长沙理工大学电气与信息工程学院长沙410114
刊名：电工技术学报 ISTICEIPKU
Journal：TransactionsofChinaElectrotechnicalSociety
年，卷(期)：2024, 39(10)
分类号：TM912
关键词：锂离子电池 荷电状态 健康状态 高斯过程回归 带遗忘因子的递推最小二乘法
Keywords：Lithium-ionbattery stateofcharge stateofhealth Gaussianprocessregression forgettingfactorrecursiveleastsquares
机标分类号：TM92TM343TK32
在线出版日期：2024年5月31日
基金项目：湖南省科技创新人才计划科技创新团队资助项目基于等效电路模型和数据驱动模型融合的SOC和SOH联合估计方法[
期刊论文] 电工技术学报--2024, 39(10)刘萍 李泽文 蔡雨思 王文 夏向阳针对电池SOC与SOH估计结果相互影响,单独估计准确度不高的问题,该文提出了一种基于等效电路模型和数据驱动模型融合的SOC和SOH联合估计方法.通过构建考虑老化和SOC的电池二阶RC等效电路模型,采用带遗忘因子的递推最小二...参考文献和引证文献
参考文献
引证文献
本文读者也读过
相似文献
相关博文
基于等效电路模型和数据驱动模型融合的SOC和SOH联合估计方法 Joint Estimation Method of SOC and SOH Based on Fusion of Equivalent Circuit Model and Data-Driven Model
基于等效电路模型和数据驱动模型融合的SOC和SOH联合估计方法.pdf
- 文件大小:
- 1.05 MB
- 下载次数:
- 60
-
高速下载
|
