What question did this study set out to answer?

The aim is to create a robust framework for forecasting lithium-ion battery capacity and estimating remaining useful life despite aging-induced fluctuations.

March 28, 2026Open Access

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

Puntos clave

The aim is to create a robust framework for forecasting lithium-ion battery capacity and estimating remaining useful life despite aging-induced fluctuations.
Decomposed raw battery capacity data using CEEMDAN to isolate degradation trends from disturbances.
Applied a temporal convolutional network to model the long-term trend component.
Utilized Gaussian process regression for local fluctuation characterization and predictive uncertainty assessment.
Employed Dempster-Shafer evidence theory to combine multi-source prognostic outputs.
Achieved RMSE values between 0.0257 and 0.0445 Ah for capacity forecasting.
Observed end-of-life cycle deviations ranging from 1.17 to 5.53 cycles.
Demonstrated a balanced trade-off between prediction accuracy and threshold-crossing stability compared to baseline methods.
Found increased prediction error with longer forecasting horizons, consistent with capacity extrapolation characteristics.

Resumen

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.

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