A masked feature transfer strategy for lithium battery state of health prediction under variable working conditions
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％.
Lithium batterystate of healthmasked feature transfer strategyvariable working conditions transfer
国家科技期刊平台
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