How battery capacities are correctly estimated considering latent short-circuit faults

Hongchang CAI, Xiaopeng TANG, Xin LAI, Yanan WANG, Xuebing HAN, Minggao OUYANG, Yuejiu ZHENG*

*Corresponding author for this work

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

Abstract

Capacity is a key factor in assessing battery health. Traditional capacity estimation methods assume by default the battery is in a normal state. When there is a latent short-circuit fault, the measured current deviates from the actual current flowing into or out of the battery unit, leading to errors in capacity estimation. To address this challenge, we have constructed an end-cloud collaborative framework to accurately estimate the module's capacity considering the latent short-circuit faults. The core scheme is to estimate the module's charging capacity and discharging capacity simultaneously, obtain the module's operating mode based on the historical relationship between the two, conduct quantitative diagnosis for the short-circuit fault, and feedback the accurate capacity value. This framework addresses the coupled issue of erroneous capacity estimation in the presence of latent short-circuit faults, and the inability to diagnose module external short-circuit faults due to the lack of a comparison. This has profound significance for achieving more reliable battery safety management.
Original languageEnglish
Article number124190
JournalApplied Energy
Volume375
Early online date13 Aug 2024
DOIs
Publication statusE-pub ahead of print - 13 Aug 2024

Bibliographical note

Publisher Copyright:
© 2024 Elsevier Ltd

Keywords

  • Battery module
  • Capacity estimation
  • End- cloud collaboration
  • Quantitative diagnosis
  • Short-circuit

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