GPCN: A Decomposition-Based Hybrid Model for a Lithium-Ion Capacity Forecasting and RUL Inference Framework

To address the non-stationary fluctuations caused by capacity regeneration and measurement noise during lithium-ion battery aging, this paper proposes a decomposition-guided heterogeneous prognostic framework for capacity forecasting and remaining useful life (RUL) inference. First, the raw capacity sequence is decomposed by CEEMDAN to separate the long-term degradation trend from short-term regeneration-related disturbances across different time scales. Next, a temporal convolutional network (TCN) is employed to model the trend component, while Gaussian process regression (GPR) is used to characterize local fluctuation behavior and provide predictive uncertainty. Finally, Dempster–Shafer (D-S) evidence theory is introduced to fuse multi-source prognostic outputs, yielding a more robust capacity trajectory for end-of-life (EOL) threshold localization and RUL estimation. Experiments are conducted on the lithium-ion battery dataset released by NASA Ames. Across the four tested battery cells, the proposed method achieves RMSE values of 0.0257–0.0445 Ah and EOL cycle deviations of 1.17–5.53 cycles, while yielding a more balanced trade-off than representative baselines between point-wise prediction accuracy and threshold-crossing stability. Moreover, under direct multi-step forecasting, the prediction error increases with the forecasting horizon, which is consistent with the expected characteristics of long-horizon capacity extrapolation. Overall, this work provides an implementable and interpretable prognostic framework for battery health assessment in the presence of capacity regeneration phenomena.