Remaining useful life prediction of lithium-ion battery packs using prior cell degradation and limited pack-level data

Accurate remaining useful life (RUL) prediction of lithium-ion battery packs is important for reliable operation and maintenance. However, pack-level lifecycle degradation data are costly and time-consuming to obtain, which limits the development of data-driven prediction models. In addition, pack degradation is not directly equivalent to cell degradation because it is affected by inter-cell inconsistency and series constraints. This discrepancy further increases the difficulty of RUL prediction with limited pack-level data. To address this problem, this paper proposes a cross-level RUL prediction method using prior cell degradation and limited pack-level data. Full-lifecycle cell data are first used to establish long-timescale state of health (SOH) trend priors and short-timescale degradation-rate priors through a multi-kernel deep Gaussian process. Limited early-stage pack-level data and a voltage-derived inconsistency feature are then introduced to calibrate the discrepancy between cell-level priors and pack-level degradation behavior through cross-level scaling and residual compensation. During recursive prediction, particle filtering is used to fuse the corrected SOH prediction and degradation-rate prediction, so that the pack-level SOH trajectory and RUL can be obtained. Experimental results on laboratory and electric-vehicle battery pack datasets show that the proposed method achieves an average RUL prediction error of about 4 cycles when only 17.46% of the pack-level lifecycle data are used for model construction. These results indicate that the proposed method can reduce the dependence on costly pack-level lifecycle data while maintaining accurate pack-level RUL prediction under limited-data conditions.