A deep learning method for online capacity estimation of lithium-ion batteries

S. Shen, Mohammadkazem Sadoughi, Xiangyi Chen, Mingyi Hong, Chao Hu

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

The past two decades have seen an increasing usage of lithium-ion (Li-ion) rechargeable batteries in diverse applications including consumer electronics, power backup, and grid-scale energy storage. To guarantee safe and reliable operation of a Li-ion battery pack, battery management systems (BMSs) should possess the capability to monitor, in real time, the state of health (SOH) of the individual cells in the pack. This paper presents a deep learning method which utilizes deep convolutional neural network (DCNN) for cell-level capacity estimation based on the voltage, current, and charge capacity measurements during a partial charge cycle. The unique features of DCNN include the local connectivity and shared weights, which enable the model to accurately estimate battery capacity using the measurements during charge. To the best of our knowledge, this is one of the first attempts to apply deep learning to the online capacity estimation of Li-ion batteries. Ten-year daily cycling data from eight implantable Li-ion cells and half-year cycling data from 20 18650 Li-ion cells were utilized to verify the performance of the proposed deep learning method. Compared with traditional machine learning methods such as shallow neural networks and relevance vector machine (RVM), the proposed deep learning method is demonstrated to produce higher accuracy and robustness in the online estimation of Li-ion battery capacity.

Original languageEnglish (US)
Article number100817
JournalJournal of Energy Storage
Volume25
DOIs
StatePublished - Oct 2019

Bibliographical note

Funding Information:
This research was in part supported by the US National Science Foundation (NSF) Grant Nos. CNS-1566579 and ECCS-1611333 , the NSF I/UCRC Center for e-Design , and the U.S. Department of Transportation, Office of the Assistant Secretary for Research and Technology (USDOT/OST-R) through the Midwest Transportation Center (MTC). Any opinions, findings or conclusions in this paper are those of the authors and do not necessarily reflect the views of the sponsoring agencies. The authors would also like to express special thanks to Dr. Gaurav Jain and Dr. Hui Ye at Medtronic, Inc. for sharing the long-term cycling data for this study. Finally, the authors gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.

Funding Information:
This research was in part supported by the US National Science Foundation (NSF) Grant Nos. CNS-1566579 and ECCS-1611333, the NSF I/UCRC Center for e-Design, and the U.S. Department of Transportation, Office of the Assistant Secretary for Research and Technology (USDOT/OST-R) through the Midwest Transportation Center (MTC). Any opinions, findings or conclusions in this paper are those of the authors and do not necessarily reflect the views of the sponsoring agencies. The authors would also like to express special thanks to Dr. Gaurav Jain and Dr. Hui Ye at Medtronic, Inc. for sharing the long-term cycling data for this study. Finally, the authors gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.

Keywords

  • Capacity estimation
  • Deep learning
  • Health monitoring
  • Lithium-ion batteries

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