控制理论与应用2024，Vol.41Issue(8)：1377-1385,9.DOI:10.7641/CTA.2023.30025
掩码表征迁移策略下的锂电池变工况健康状态预测
A masked feature transfer strategy for lithium battery state of health prediction under variable working conditions
摘要
Abstract
Lithium battery state of health(SOH)prediction can evaluate battery aging.Due to differences in battery working conditions,lithium battery training data(source domain)and online application data(target domain)have different distributions,and transfer learning is an effective method to solve the above problems.However,on the one hand,tradition-al transfer learning methods require a large number of source domain data labels,and the SOH measurement is difficult to provide sufficient labels.On the other hand,these methods cannot make full use of existing expert knowledge.To solve the above problems,this paper innovatively proposes a masked feature transfer strategy(MFTS),which realizes the SOH prediction of the lithium battery under variable working conditions with unlabeled source domain data.First,a masked self-supervised framework is designed,which can automatically extract robust representations in source domain data with-out labels.Secondly,an expert knowledge module is proposed to guide the extracted features to approach the expert features,thus realizing the integration of expert knowledge.Finally,a double learning rate method is proposed to perform synchronous variable speed training on the feature extraction and the SOH prediction network,and achieves the accurate prediction of the target domain SOH while transferring the knowledge of the source domain.Based on the NASA's public data set,the prediction error of the proposed MFTS model in the six sets of experiments is all less than or equal to 4.08％.关键词
锂离子电池/健康状态/掩码表征迁移策略/变工况迁移Key words
Lithium battery/state of health/masked feature transfer strategy/variable working conditions transfer引用本文复制引用
王一航,陈旭,沈萌,赵春晖..掩码表征迁移策略下的锂电池变工况健康状态预测[J].控制理论与应用,2024,41(8):1377-1385,9.基金项目
国家自然科学基金杰出青年基金项目(62125306),NSFC-浙江两化融合联合基金项目(U1709211)资助.Supported by the National Natural Science Foundation of China(62125306)and the NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization(U1709211). （62125306）
