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 journalArticle

1 Citation (Scopus)

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

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Lithium
Neural networks
Ions
Consumer electronics
Secondary batteries
Energy storage
Learning systems
Health
Lithium-ion batteries
Deep learning
Electric potential
Battery management systems

Keywords

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

Cite this

A deep learning method for online capacity estimation of lithium-ion batteries. / Shen, S.; Sadoughi, Mohammadkazem; Chen, Xiangyi; Hong, Mingyi; Hu, Chao.

In: Journal of Energy Storage, Vol. 25, 100817, 10.2019.

Research output: Contribution to journalArticle

Shen, S. ; Sadoughi, Mohammadkazem ; Chen, Xiangyi ; Hong, Mingyi ; Hu, Chao. / A deep learning method for online capacity estimation of lithium-ion batteries. In: Journal of Energy Storage. 2019 ; Vol. 25.
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