Multi-Scale Consistency Analysis and Unsupervised Detection of Energy Storage Batteries for Asynchronous Sampling

Consistency monitoring is essential for safe battery energy storage system operation, yet practical data often exhibit asynchronous sampling. This study proposes a tick-driven multi-scale consistency analysis and unsupervised detection method for energy storage batteries. Actual cell-voltage update instants are used as analysis ticks, while high-frequency string-level variables are aggregated over adjacent tick intervals to describe operating conditions. Voltage-dispersion features are then used for anomaly scoring and cell-level localization. The method was evaluated using two-day station data and a controlled 20 Ah 16-series module experiment. In the station dataset, 190 effective ticks were extracted, and a transient consistency deterioration at 24,023 s showed a voltage range of 0.049 V and a standard deviation of 0.0057 V. In the controlled experiment, 185 ticks were obtained from 166,797 voltage samples after 900 s batch resampling, reducing cell-level evaluation instances by over 99%; cell #13 was identified as the dominant high-response cell. The method provides an interpretable framework for consistency monitoring and abnormal-cell localization under asynchronous sampling.