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Peer-reviewedCC BY 4.0Batteries

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

  • Tianle Liu1
  • Tengjiao Lu1
  • Changlin Yang2
  • Peng Pan1
  • Chen Li3
  • Tengjiao He4
4 affiliations
  1. 1School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, China
  2. 2Energy AI Department, Zhejiang Tianneng Energy Storage Technology Development Co., Ltd., Huzhou 313100, China
  3. 3School of Engineering, Cardiff University, Cardiff CF24 3AA, UK
  4. 4College of Information Science and Technology, Jinan University, Guangzhou 510632, China
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Abstract

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.

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Cite

BibTeXRIS
BibTeX
@article{liu2026structureaware,
  title = {{Structure-Aware Lossless Compression for Cell-Level BESS Monitoring Data}},
  author = {Tianle Liu and Tengjiao Lu and Changlin Yang and Peng Pan and Chen Li and Tengjiao He},
  journal = {Batteries},
  year = {2026},
  month = oct,
  doi = {10.3390/batteries12100396},
  url = {https://www.mdpi.com/2313-0105/12/10/396},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Structure-Aware Lossless Compression for Cell-Level BESS Monitoring Data
AU  - Tianle Liu
AU  - Tengjiao Lu
AU  - Changlin Yang
AU  - Peng Pan
AU  - Chen Li
AU  - Tengjiao He
PY  - 2026
DA  - 2026/10/04
JO  - Batteries
DO  - 10.3390/batteries12100396
UR  - https://www.mdpi.com/2313-0105/12/10/396
AB  - 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.
SN  - 2313-0105
ER  -