Transfer Learning for Adapting Battery State-of-Health Estimation from Laboratory to Field Operation

Søren Byg Vilsen*, Daniel-Ioan Stroe

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

The importance of accurate estimation of the state-of-health (SOH) for Lithium-ion (Li-ion) batteries is going to increase as Li-ion batteries become more integrated into daily life. As the reliance on Li-ion batteries increases so does the need for battery pack size optimisation and the extension of battery lifetime. Data-driven methods for estimation of the SOH of Li-ion batteries have shown to have good performance under laboratory conditions, but often fail to achieve similar performance when used in real life applications. This is a consequence of the field data seldomly matching the laboratory data, which is a necessary condition of most data-driven methods. A method which aims to account for discrepancies between laboratory and field data is transfer learning. This paper shows how the transfer learning algorithm kernel mean matching can be used to transfer both multiple linear regression (MLR) and bootstrapped random vector functional link (BRVFL) models from the laboratory to the field. It is shown that these methods can achieve mean absolute percentage errors (MAPE’s) smaller than 1% on both laboratory and field data simultaneously.
Original languageEnglish
JournalIEEE Access
Number of pages15
ISSN2169-3536
Publication statusE-pub ahead of print - 2022

Keywords

  • Bagging random vector functional link neural networks
  • feature extraction
  • Lithium-ion batteries
  • multiple linear regression
  • transfer learning

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