Structure-Aware Lossless Compression for Cell-Level BESS Monitoring Data

Large-scale battery energy storage systems (BESSs) continuously generate cell-level monitoring data. These data increase storage demand and communication load in cloud-edge systems. Exact measurement values are required for archival and diagnostic tasks, which motivates lossless compression. This paper releases an industrial cell-level dataset collected from a 215 kWh BESS and analyzes its compression-relevant properties. The data exhibit finite decimal precision, strong temporal dependence, spatial similarity, and different value ranges across measurement variables. Based on these properties, we propose a structure-aware lossless compression method. The method groups the data by measurement variable and reversibly scales the values to integers. It applies temporal and spatial differencing, followed by signed-value remapping, bit-width packing, and final lossless coding. Experiments on AMD and Raspberry Pi platforms evaluate global block-based compression using all 59,452 records and real-time compression. The proposed method achieves compression ratio of 0.779% in the global block-based setting, while its real-time ratio is 1.801%. The real-time configuration requires 0.198 ms per time record on AMD and 1.252 ms on Raspberry Pi.