Transfer-driven prognosis from battery cells to packs: An application with adaptive differential model decomposition

The modelling of performance degradation and lifespan prediction in lithium-ion batteries is crucial for their efficient and stable operation. However, research on performance degradation and lifespan prediction under the setting of multiple cell groups has not yet received sufficient attention. To address this gap, we designed and conducted degradation experiments on battery cells and packs, highlighting and demonstrating the disparities between individual battery cells and packs. This realistic disparity ultimately motivated our investigation and development of a transfer-driven prognostic approach for lithium-ion battery packs. First, we utilized the Euclidean distance for normalizing cell-level trajectories and introduced a two-stage decomposition approach for feature stabilization and differential model construction. Subsequently, we developed a cell-pack transfer pipeline based on Euclidean distance to mitigate domain discrepancies. Finally, we achieved simultaneous trajectory distribution prediction using a probability-based approach incorporating the unscented transform. Our prediction approach achieved a stabilized prediction error below 5% at both the cell level and the pack level for both early and real-time lifetime predictions, offering a valuable contribution to the field of lithium-ion battery pack performance prognosis.