A Lithium-Ion Battery Remaining Useful Life Prediction Framework Based on Mode Decomposition and CNN-TimeXer

Abstract

Remaining useful life (RUL) prediction is essential for lithium-ion battery health management, yet accurate prognosis remains difficult because battery degradation trajectories are strongly non-stationary and often contain mixed-scale fluctuations together with local regeneration-like disturbances. To address this issue, this study proposes a DOA-VMD-CNN-TimeXer framework for RUL-oriented prognosis through capacity trajectory prediction and reconstruction. In the proposed framework, the Dream Optimization Algorithm (DOA) is first used to adaptively optimize the key variational mode decomposition (VMD) parameters so that the measured capacity sequence can be decomposed into modal components with clearer temporal structures. A CNN-enhanced TimeXer predictor is then constructed for component-wise forecasting, where the CNN branch extracts local fluctuation features and the TimeXer branch models long-range temporal dependencies. Finally, the predicted modal components are reconstructed into the capacity trajectory to support threshold-based end-of-life inference. Comparative experiments on four CALCE lithium-ion battery cells under 5:5 and 6:4 train–test splits show that the proposed framework achieves RMSE values ranging from 0.010316 to 0.014528, while all corresponding R2 values remain above 0.994. In addition, the proposed method shows closer agreement with the measured degradation trajectories and more stable prediction behavior than the compared methods, indicating its effectiveness for lithium-ion battery RUL-oriented prognosis under the evaluated CALCE settings.