梯次利用锂电池健康状态预测  

State of Health Prediction of Second-Use Lithium-Ion Battery

作　　者：孙冬[1,2] 许爽 Sun Dong;Xu Shuang(Zhengzhou University of Light Industry,Zhengzhou 450002 China;School of Mechatronics Engineering and Automation,Shanghai University,Shanghai 200072 China;Zhengzhou Institute of Technology,Zhengzhou 450044 China)

机构地区：[1]郑州轻工业学院,郑州450002 [2]上海大学机电工程与自动化学院,上海200072 [3]郑州工程技术学院,郑州450044

出　　处：《电工技术学报》2018年第9期2121-2129,共9页Transactions of China Electrotechnical Society

基　　金：上海市科委重点项目(14DZ1206302);河南省科技攻关项目(172102210069);河南省高校重点科研项目(18A470018)资助

摘　　要：从电动汽车中退役的锂电池在功能元件有效的情况下可进行梯次利用,针对退役锂电池处于离线状态且单体电池之间存在性能差异等问题,以锂电池欧姆内阻为研究对象,设计适用于梯次利用锂电池性能测试工况。基于锂电池一阶RC等效电路模型,研究基于增量式自回归模型(IARX)的健康特征数据提取方法,以此构建均值内阻、最小内阻和内阻-荷电状态(SOC)三种健康因子,建立健康寿命模型,提出基于多模型数据融合技术的锂电池健康状态(SOH)预测方法。实验和仿真结果表明:所建健康寿命模型适用于预测同种类退役锂电池SOH,验证了模型的有效性;基于多模型数据融合技术有利于提高锂电池SOH预测精度,验证了此方法的可行性。Lithium-ion battery retired from electric vehicles may be considered expanding their useful life for second use if their functional components are effective.The retired battery has been separated from battery management system and they are offline.Their residual capacity and performance are different from each other,so it is necessary to reevaluate their performance.In this paper,a suitable test profile for performance evaluation of retired battery was designed which is to identify ohm resistance based on the first-order RC model,and the health feature extraction method was proposed based on the identifiable I-ARX model.The three health indicators(HIs),average internal resistance,minimum internal resistance and resistance-SOC curve,were obtained from health test data.Based on the three HIs,battery health models were established and their effectiveness has been validated by simulation.Furthermore,an evaluation method using multi-model fusion technology was proposed to solve the issue by BP neural network,which is to enhance the accuracy of SOH prediction for the retired lithium-ion batteries,and its effectiveness was verified by experimental data.

关 键 词：梯次利用锂电池 健康状态预测 健康因子 健康模型 多模型数据融合技术 

分 类 号：TM912[电气工程—电力电子与电力传动]