基于大数据的电池健康状态(SoH)的估算及应用
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摘要
为了突破因电池管理系统(BMS)存储和计算能力不足导致的传统电池健康状态(SoH)估算方法的局限,提出了基于互联网平台在线大数据的SoH估算方法。研究了数据离散特性、电池单体一致性等因素对此估算方法的影响;结合某平台的在线大数据进行了此方法的集成应用,对单车以及分车辆品牌、分地域、分时域进行了多维度的电池SoH衰减比较验证。结果表明:此方法能够有效估算单个动力电池系统的SoH及其变化,并且能够与其他数据类型进行多维度整合,对动力电池进行大数据画像分析。
A traditional state of health(SoH) estimation method was proposed based on online big data online platform to break through the limitations of SoH estimation methods caused by insufficient battery management system(BMS) storage and computing power. The effects of data discrete characteristics and battery cell consistency on the method were studied. The method was integrated into specific big-data platform, and the batteries' SoH decrease were verified in muti-dimensions such as different vehicle brands, different locations and different time. The results show that this method can effectively estimate SoH of single power battery system and its variation, as well as integrate within other data in muti-dimensions and analyze the big data image of power batteries.
A traditional state of health(SoH) estimation method was proposed based on online big data online platform to break through the limitations of SoH estimation methods caused by insufficient battery management system(BMS) storage and computing power. The effects of data discrete characteristics and battery cell consistency on the method were studied. The method was integrated into specific big-data platform, and the batteries' SoH decrease were verified in muti-dimensions such as different vehicle brands, different locations and different time. The results show that this method can effectively estimate SoH of single power battery system and its variation, as well as integrate within other data in muti-dimensions and analyze the big data image of power batteries.
引文
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[5]XIONG Rui,SUN Fengchun,GONG Xianzhi,et al.Adata-driven based adaptive state of charge estimator of lithium-ion polymer battery used in electric vehicles[J].Energy,2013,63(6):1421-1433.
[6]Nuhic A,Terzimehic T,Soczka-Guth T,et al.Health diagnosis and remaining useful life prognostics of lithiumion batteries using data-driven methods[J].J Power Sources,2013,239:680-688.
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[8]HU Xiaosong,YUAN Hao,ZOU Changfu,et al.CoEstimation of state of charge and state of health for lithium-ion batteries based on fractional-order calculous[J].IEEE Trans Vehi Tech,2018:10319-10329
[9]Doyle M,Newman J.Modeling of galvanostatic charge and discharge of the llithium/polymer/insertion cell[J].JElevtrochem Soc,1993,140:1526-1533.
[10]Sabatier J,Merveillaut M,Francisco J M,et al.Lithiumion batteries modeling involving fractional differentiation[J].J Power Sources,2014,262:36-43.
[11]CUI Yingzhi,DU Chunyu,YIN Geping,et al.Multistress factor model for cycle lifetime prediction of lithium ion batteries with shallow-depth discharge[J].J Power Sources,2015,279:123-132.
[12]Pinson M B,Bazant M Z.Theory of SEI formation in rechargeable batteries:Capacity Fade,accelerated aging and lifetime prediction[J].J Electrochem Soc,2012,160(2):A243-A250.
[13]孙冬,许爽.梯次利用锂电池健康状态预测[J].电工技术学报,2018,9:2121-2129.SUN Dong,XU Shuang,State of health prediction of second-use lithium-ion battery[J].J Electr Engi Tech,2018,9:2121-2129.(in Chinese)
[14]戴海峰,王楠,魏学哲,等.车用动力锂离子电池单体不一致性问题研究综述[J],汽车工程,2014,36(2):181-188.DAI Haifeng,WANG Nan,WEI Xuezhe,et al.Review of inconsistent problems in vehicle lithium ion battery units[J].Automotive Engineering,2014,36(2):181-188.(in Chinese)
[15]王震坡,孙逢春,林程.不一致性对动力电池组使用寿命影响的分析[J].北京理工大学学报:自然科学版,2006,26(7):17-20.WANG Zhenpo,SUN Fengchun,LIN Cheng.Analysis of the influence of inconsistency on the service life of power battery packs[J].J Beijing Inst Tech:Natu Sci,2006,26(7):17-20.(in Chinese)