Compressing and reconstructing the voltage data for lithium-ion batteries using model migration and un-equidistant sampling techniques

Xiaopeng TANG, Furong GAO, Xin LAI

Research output: Journal PublicationsJournal Article (refereed)peer-review

15 Citations (Scopus)

Abstract

The long-term storage of the batteries' operating data is critical to tracing and analysing their historical use but challenged by the Trillions of bytes of raw data generated per day. For battery pack applications such as electrified transportation, recording the single-cell voltage requires tens of times more space than other signals such as the pack current. Therefore, an efficient data compressor for the voltage is urgently required to save storage. We here propose to record the entire current trajectory but only partial voltage data in the data-compressing phase to save space. Understanding that the battery's load profiles are often non-stationary, determining an optimum voltage-recording strategy is critical to the reconstruction accuracy but, unfortunately, an NP-hard problem. In this case, a heuristic method is proposed to seek a near-optimum solution with reduced computation. In addition, a battery model is also identified in the compressing phase so that the voltage trajectory can be readily calculated from the recorded current when data reconstructing is required. To compensate for the potential mismatch of the identified model, we establish a migration network using the recorded (partial) data. A piece-wise linear corrector is further fused into the reconstruction algorithm to not only guarantee zero errors at the voltage-recording points but also simplify the design of the above-mentioned heuristic optimisation algorithm. Experimental results show that the root-mean-squared-error of the reconstructed data could be bounded by 5 mV when more than 95% of the voltage data are compressed, paving the way to more efficient storage of large-scale battery operating data.

Original languageEnglish
Article number100186
JournaleTransportation
Volume13
Early online date10 Jun 2022
DOIs
Publication statusPublished - Aug 2022
Externally publishedYes

Bibliographical note

Funding Information:
We would also like to thank Prof. Jingyi Lu and Dr. Yuanqiang Zhou for the discussions on the Workflow 2. The first author would also like to thank the Guangzhou HKUST Fok Ying Tung Research Institute for the continuing support during the Hong Kong's unrest and 2019-nCoV's outbreak. This work was supported partly by Guangdong scientific and technological project (2019A050516002), Guangzhou Scientific and Technological Project (202002030323), and National Natural Science Foundation of China (51977131).

Publisher Copyright:
© 2022 Elsevier B.V.

Keywords

  • Data compressing
  • Data storage
  • Lithium-ion battery management system
  • Model migration

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