Deep learning-based Remaining Useful Life Prediction of Lithium-ion Battery Considering Two-phase Aging Process

The aging process of lithium-ion batteries is typically nonlinear, characterized by a knee point that divides it into two distinct phases: a slow aging phase and a rapid aging phase. Predicting the remaining useful life (RUL) in rapid phase is crucial for providing users with timely alerts to replace the battery before its end of life (EOL). A two-stage identification and prediction framework has been proposed to identify the current aging phase of a battery and predict its RUL if it is in the rapid aging phase, requiring data from only the most recent three cycles of the charge process. In the first stage, a 2D-ResNet is constructed for aging phase identification, offering strategies with high accuracy or low risk depending on user requirements. In the second stage, a Gated-ResNet-Attention model is innovatively constructed to filter out unreliable temperature features. The model achieved mean absolute error and root mean square error values of 6.94 and 8.12, respectively. The proposed framework can identify aging process and predict the RUL in rapid aging phase accurately with minimal input requirements. This approach reduces data storage requirements while providing users with sufficient indicative information for proactive battery maintenance.