A transferable online state monitoring and remaining useful life prediction method for lithium-ion batteries based on area ratio features

The cross-domain remaining useful life (RUL) prediction of lithium-ion batteries is critical for the safe and reliable operation of energy storage systems. However, practical applications still face two core challenges: limited real-time performance for online monitoring and reduced cross-domain generalization due to source-target distribution shifts. To address this issue, this paper proposes a transferable online state monitoring and RUL prediction method for lithium-ion batteries based on area ratio features. First, three area ratio features are extracted from the full charge–discharge curve, each exhibiting a correlation coefficient greater than 0.93 with the battery’s capacity. Subsequently, a cycle compression and degradation state mapping strategy is applied to unify the time scale, and a cross-domain binary classification model is developed to enable online state monitoring. Furthermore, clustering analysis is conducted on rapidly degrading samples, and key subclasses are selected to construct the transfer model. Finally, a radial basis function (RBF) kernel-based maximum mean discrepancy (MMD) constraint is incorporated into the long short-term memory (LSTM) feature representation layer to align the feature distributions of the source and target domains. Experimental results demonstrate that the cross-domain classification accuracy of the online state monitoring model exceeds 93%, with a runtime of less than 0.5 ms. The transfer-based RUL prediction model achieves a mean absolute percentage error (MAPE) of under 4%, indicating that the proposed method combines high prediction accuracy with real-time performance.