Estimating Series Battery Pack Capacity From Charge–Discharge Behavior at Unilateral Boundaries

Battery pack capacity estimation is crucial for improving the performance, safety, and reliability of energy storage systems. However, the inherent cell inconsistency and unobservable internal electrochemical reactions make this task challenging, especially when the battery behavior is only partially observed. To address this challenge, this article proposes a novel framework, UniCap, to estimate the capacity of series battery packs by utilizing partial behavior observed near the lower voltage boundary. Within this framework, the traditional estimation process is reformulated into two tractable subtasks. First, a hybrid K-nearest neighbors method is developed to nondestructively quantify unreleased capacity caused by cell inconsistency. Second, a cross-stream cell-wise transformer is proposed to predict the cell behavior and corresponding capacity at the unknown upper voltage boundary. Based on the above, the lower bound of the pack capacity can be inferred. Without requiring full charge or discharge cycles, the proposed framework provides an efficient solution for battery pack capacity estimation. Validated on a large-scale industrial dataset comprising 1757 battery packs, the framework achieves state-of-the-art performance with a mean absolute error of 0.0968 Ah. Notably, compared with existing industrial workflows, the proposed method significantly reduces test duration with only marginal accuracy degradation, thereby improving automation and production efficiency.