A novel lithium-ion battery state-of-health estimation method for fast-charging scenarios based on an improved multi-feature extraction and bagging temporal attention network

Accurately estimating the state of health (SOH) of fast-charging lithium-ion batteries is crucial for safely and reliably operating battery systems. However, handling data scarcity and rapid charging scenarios under diverse operational conditions is challenging. In this paper, a novel approach for estimating the SOH of lithium-ion batteries (LIBs) is introduced based on an improved multisegment feature extraction and bagging temporal attention network. First, four health features, including the time differences observed at equal voltages, the cumulative integral of voltage changes, the total circuit charge variation and the levels of slope peaks, are identified. Second, a residual bidirectional long short-term memory (Bi-LSTM) attention mechanism is designed to focus on the temporal dimension of battery data by incorporating a multilayered complex neural network design comprising convolutional layers, pooling layers, Bi-LSTM layers and fully connected layers. This design effectively captures the features and relationships contained in battery data. The predictions derived from randomly initialized parameters and multiple submodels are stacked to improve the generalizability of the model. Finally, comprehensive evaluations are conducted through comparative experiments, ablation studies and noise experiments, with six evaluation metrics considered across three datasets. The MAE, RMSE, MAEP and MAXE of the proposed model reach 0.366, 0.605, 0.519 and 1.56, respectively. The results indicate that the proposed method enhances the robustness, resilience and generalizability of the estimates produced under different conditions and noise scenarios.