Forecasting state-of-health of lithium-ion batteries using variational long short-term memory with transfer learning

Seongyoon Kim, Yun Young Choi, Ki Jae Kim, Jung Il Choi

Research output: Contribution to journalArticlepeer-review

Abstract

Accurate forecasting of state-of-health and remaining useful life of Li-ion batteries ensure their safe and reliable operation. Most previous data-driven prediction methods assume the same distributions between training and testing batteries. Because of different operating conditions and electrochemical properties of batteries, however, distribution discrepancy exists in real-world applications. To address this issue, we present a deep-learning-based health forecasting method for Li-ion batteries, including transfer learning to predict states of different types of batteries. The proposed method simultaneously predicts the end of life of batteries and forecasts degradation patterns with predictive uncertainty estimation using variational inference. Three types of batteries are used to evaluate the proposed model; one for source and the others for target datasets. Simulation results reveal that the proposed model reduces efforts required to collect data cycles of new battery types. Further, we demonstrate the generality and robustness of the proposed method in accurately forecasting the state-of-health of Li-ion batteries without past information, which applies to cases involving used batteries.

Original languageEnglish
Article number102893
JournalJournal of Energy Storage
Volume41
DOIs
Publication statusPublished - 2021 Sep

Bibliographical note

Funding Information:
This work was supported by the Energy Efficiency & Resources Core Technology Program of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) , and financial grants from the Ministry of Trade, Industry & Energy, Republic of Korea (No. 20172420108640 ), and National Research Foundation of Korea (NRF) grant funded by the Korean government (Ministry of Science and ICT) ( NRF-2017R1E1A1A0-3070161 and NRF-20151009350 ), and the National Supercomputing Center with supercomputing resources including technical support (KSC-2020-INO-0056). This research was partially supported by the Graduate School of Yonsei University Research Scholarship Grants in 2020 . Computing resources were supplied by the National IT Industry Promotion Agency (NIPA).

Funding Information:
This work was supported by the Energy Efficiency & Resources Core Technology Program of the Korea Institute of Energy Technology Evaluation and Planning (KETEP), and financial grants from the Ministry of Trade, Industry & Energy, Republic of Korea (No. 20172420108640), and National Research Foundation of Korea (NRF) grant funded by the Korean government (Ministry of Science and ICT) (NRF-2017R1E1A1A0-3070161 and NRF-20151009350), and the National Supercomputing Center with supercomputing resources including technical support (KSC-2020-INO-0056). This research was partially supported by the Graduate School of Yonsei University Research Scholarship Grants in 2020. Computing resources were supplied by the National IT Industry Promotion Agency (NIPA).

Publisher Copyright:
© 2021 Elsevier Ltd

All Science Journal Classification (ASJC) codes

  • Renewable Energy, Sustainability and the Environment
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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